{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a37dfd6d",
   "metadata": {},
   "source": [
    "# 03 결과 도출 — 표·그림과 검증\n",
    "\n",
    "`02_분석.ipynb`의 산출물로 포털 카드 「여섯 외생을 나란히 놓고 보니」(`exog-deepdive.html`)와 「유가 한 변수를 파보니」(`oil-deepdive.html`)의\n",
    "표·그림을 다시 만든다. 공표 문구 가운데 계산과 맞지 않거나 근거가 달라진 항목은 별도 표로 정리한다. 표 7은 정비 전후의 외생 반사실(참고),\n",
    "표 8–11·그림 4는 2026-10 정비판의 선형 SHAP·유가 진단·시나리오 폭(02 10절)이다. 마지막 셀은 이전 판 공표 수치와 정비판 기록 수치를 계산 결과와 대조한다\n",
    "(반올림 범위 안에서 일치해야 통과)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34efa5f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:39.457990Z",
     "iopub.status.busy": "2026-10-08T08:04:39.457489Z",
     "iopub.status.idle": "2026-10-08T08:04:40.181768Z",
     "shell.execute_reply": "2026-10-08T08:04:40.181264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.10 · pandas 2.2.3 · numpy 2.4.6\n",
      "  ONLINE=0 (FRED 유가 재수집)  FULL=0 (확장 점검)  FRED 키 없음(공개 CSV 사용)\n",
      "  외생 과거(정상형)     있음  data\\inputs\\exog_hist_v2.csv\n",
      "  외생 시나리오(정상형)   있음  data\\inputs\\exog_scen_v2.csv\n",
      "  외생 수준          있음  data\\inputs\\exog_scenarios_v2.csv\n",
      "  시나리오 충격        있음  data\\inputs\\dsge_exog_paths.csv\n",
      "  BOKDPM 관측      있음  data\\inputs\\bokdpm_obs_2000_2025.csv\n",
      "  FRED WTI       있음  data\\inputs\\fred_POILWTIUSDM.csv\n",
      "  FRED 두바이       있음  data\\inputs\\fred_POILDUBUSDM.csv\n",
      "  동결 계수          있음  data\\inputs\\varx_coef_frozen.npz\n",
      "  패널 총량          있음  data\\inputs\\levels_agg.csv\n",
      "  패널 36          있음  data\\inputs\\levels_sa.csv\n",
      "  패널 62          있음  data\\inputs\\levels_62.csv\n",
      "  패널 69          있음  data\\inputs\\levels_69.csv\n"
     ]
    }
   ],
   "source": [
    "import os, json\n",
    "import config as C            # BLAS 스레드 설정이 적용되도록 numpy보다 먼저 불러온다\n",
    "import numpy as np, pandas as pd\n",
    "import exog_diagnosis as F\n",
    "pd.set_option(\"display.float_format\", lambda v: f\"{v:,.3f}\")\n",
    "pd.set_option(\"display.width\", 200)\n",
    "C.setup()\n",
    "import matplotlib.pyplot as plt\n",
    "import koreanize_matplotlib  # noqa: F401  (한글 글꼴)\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "rd = lambda name, **kw: pd.read_csv(os.path.join(C.OUTPUT, name), encoding=\"utf-8-sig\", **kw)   # noqa: E731\n",
    "eff = rd(\"exog_effects.csv\", index_col=0)\n",
    "tot = rd(\"model_totals.csv\", index_col=0)\n",
    "paths = rd(\"exog_paths_summary.csv\", index_col=0)\n",
    "per = rd(\"exog_persistence.csv\", index_col=0)\n",
    "orank = rd(\"oil_rank.csv\", dtype={\"분류\": str})\n",
    "oil_cmp = rd(\"oil_effect_compare.csv\", index_col=0)\n",
    "dollars = rd(\"oil_dollar_paths.csv\", index_col=0)\n",
    "gap = rd(\"oil_level_gap.csv\", index_col=0)\n",
    "var = rd(\"oil_source_variants.csv\", index_col=0)\n",
    "src_chk = rd(\"oil_source_check.csv\", index_col=0)\n",
    "bm = rd(\"oil_benchmark.csv\", index_col=0)\n",
    "dub = rd(\"dubai_sim.csv\", index_col=0)\n",
    "shap_chk = rd(\"shap_check.csv\", index_col=0)\n",
    "bat = rd(\"battery_parent_check.csv\", index_col=0)\n",
    "SH = {r: rd(f\"shap_{r}.csv\", index_col=0) for r in (\"36\", \"62\", \"69\")}\n",
    "NAME = {\"유가\": \"유가\", \"세계수요\": \"세계수요\", \"실질금리\": \"실질금리\", \"실질환율\": \"실질환율\", \"고용_취업자\": \"고용(취업자)\", \"우리경기\": \"우리경기\"}\n",
    "INK, MUTED, S1C, S2C = \"#1a1f26\", \"#5c6570\", \"#2a78d6\", \"#eb6834\"   # 글자색·보조 글자색·계열 1·계열 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d3586c",
   "metadata": {},
   "source": [
    "## 표 1. 외생 6종의 실질부가가치 순효과(2035년, S1) — 반사실(해당 외생 고정) 재전망"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c42cd963",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.182767Z",
     "iopub.status.busy": "2026-10-08T08:04:40.182767Z",
     "iopub.status.idle": "2026-10-08T08:04:40.226230Z",
     "shell.execute_reply": "2026-10-08T08:04:40.226230Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "기준 전망(S1): 총량 2,326 → 2,970조원(연 2.47%), 62 바텀업 2,178 → 3,507조원(연 4.88%)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_ceb42\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ceb42_level0_col0\" class=\"col_heading level0 col0\" >변환</th>\n",
       "      <th id=\"T_ceb42_level0_col1\" class=\"col_heading level0 col1\" >장기</th>\n",
       "      <th id=\"T_ceb42_level0_col2\" class=\"col_heading level0 col2\" >총량_효과_조원</th>\n",
       "      <th id=\"T_ceb42_level0_col3\" class=\"col_heading level0 col3\" >총량_CAGR기여_%p</th>\n",
       "      <th id=\"T_ceb42_level0_col4\" class=\"col_heading level0 col4\" >바텀업_효과_조원</th>\n",
       "      <th id=\"T_ceb42_level0_col5\" class=\"col_heading level0 col5\" >바텀업_CAGR기여_%p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ceb42_level0_row0\" class=\"row_heading level0 row0\" >실질금리</th>\n",
       "      <td id=\"T_ceb42_row0_col0\" class=\"data row0 col0\" >level</td>\n",
       "      <td id=\"T_ceb42_row0_col1\" class=\"data row0 col1\" >일시</td>\n",
       "      <td id=\"T_ceb42_row0_col2\" class=\"data row0 col2\" >+162</td>\n",
       "      <td id=\"T_ceb42_row0_col3\" class=\"data row0 col3\" >+0.57</td>\n",
       "      <td id=\"T_ceb42_row0_col4\" class=\"data row0 col4\" >+354</td>\n",
       "      <td id=\"T_ceb42_row0_col5\" class=\"data row0 col5\" >+1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ceb42_level0_row1\" class=\"row_heading level0 row1\" >고용(취업자)</th>\n",
       "      <td id=\"T_ceb42_row1_col0\" class=\"data row1 col0\" >dlog</td>\n",
       "      <td id=\"T_ceb42_row1_col1\" class=\"data row1 col1\" >일시</td>\n",
       "      <td id=\"T_ceb42_row1_col2\" class=\"data row1 col2\" >+62</td>\n",
       "      <td id=\"T_ceb42_row1_col3\" class=\"data row1 col3\" >+0.22</td>\n",
       "      <td id=\"T_ceb42_row1_col4\" class=\"data row1 col4\" >+32</td>\n",
       "      <td id=\"T_ceb42_row1_col5\" class=\"data row1 col5\" >+0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ceb42_level0_row2\" class=\"row_heading level0 row2\" >유가</th>\n",
       "      <td id=\"T_ceb42_row2_col0\" class=\"data row2 col0\" >diff</td>\n",
       "      <td id=\"T_ceb42_row2_col1\" class=\"data row2 col1\" >영구</td>\n",
       "      <td id=\"T_ceb42_row2_col2\" class=\"data row2 col2\" >+15</td>\n",
       "      <td id=\"T_ceb42_row2_col3\" class=\"data row2 col3\" >+0.05</td>\n",
       "      <td id=\"T_ceb42_row2_col4\" class=\"data row2 col4\" >+65</td>\n",
       "      <td id=\"T_ceb42_row2_col5\" class=\"data row2 col5\" >+0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ceb42_level0_row3\" class=\"row_heading level0 row3\" >실질환율</th>\n",
       "      <td id=\"T_ceb42_row3_col0\" class=\"data row3 col0\" >diff</td>\n",
       "      <td id=\"T_ceb42_row3_col1\" class=\"data row3 col1\" >일시</td>\n",
       "      <td id=\"T_ceb42_row3_col2\" class=\"data row3 col2\" >+5</td>\n",
       "      <td id=\"T_ceb42_row3_col3\" class=\"data row3 col3\" >+0.02</td>\n",
       "      <td id=\"T_ceb42_row3_col4\" class=\"data row3 col4\" >+10</td>\n",
       "      <td id=\"T_ceb42_row3_col5\" class=\"data row3 col5\" >+0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ceb42_level0_row4\" class=\"row_heading level0 row4\" >세계수요</th>\n",
       "      <td id=\"T_ceb42_row4_col0\" class=\"data row4 col0\" >level</td>\n",
       "      <td id=\"T_ceb42_row4_col1\" class=\"data row4 col1\" >일시</td>\n",
       "      <td id=\"T_ceb42_row4_col2\" class=\"data row4 col2\" >+2</td>\n",
       "      <td id=\"T_ceb42_row4_col3\" class=\"data row4 col3\" >+0.01</td>\n",
       "      <td id=\"T_ceb42_row4_col4\" class=\"data row4 col4\" >-12</td>\n",
       "      <td id=\"T_ceb42_row4_col5\" class=\"data row4 col5\" >-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ceb42_level0_row5\" class=\"row_heading level0 row5\" >우리경기</th>\n",
       "      <td id=\"T_ceb42_row5_col0\" class=\"data row5 col0\" >level</td>\n",
       "      <td id=\"T_ceb42_row5_col1\" class=\"data row5 col1\" >일시</td>\n",
       "      <td id=\"T_ceb42_row5_col2\" class=\"data row5 col2\" >-1</td>\n",
       "      <td id=\"T_ceb42_row5_col3\" class=\"data row5 col3\" >-0.00</td>\n",
       "      <td id=\"T_ceb42_row5_col4\" class=\"data row5 col4\" >+8</td>\n",
       "      <td id=\"T_ceb42_row5_col5\" class=\"data row5 col5\" >+0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce2cef0b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = eff.copy()\n",
    "t1[\"장기\"] = per[\"판정\"]\n",
    "t1 = t1.sort_values(\"총량_CAGR기여_%p\", ascending=False)\n",
    "t1.index = [NAME[k] for k in t1.index]\n",
    "t1 = t1[[\"변환\", \"장기\", \"총량_효과_조원\", \"총량_CAGR기여_%p\", \"바텀업_효과_조원\", \"바텀업_CAGR기여_%p\"]]\n",
    "t1.to_csv(os.path.join(C.OUTPUT, \"table1_exog_effects.csv\"), encoding=\"utf-8-sig\")\n",
    "print(f\"기준 전망(S1): 총량 {tot.loc['총량', '2025(조원)']:,.0f} → {tot.loc['총량', '2035_S1(조원)']:,.0f}조원(연 {tot.loc['총량', 'CAGR(%)']:.2f}%), \"\n",
    "      f\"62 바텀업 {tot.loc['62', '2025(조원)']:,.0f} → {tot.loc['62', '2035_S1(조원)']:,.0f}조원(연 {tot.loc['62', 'CAGR(%)']:.2f}%)\")\n",
    "t1.style.format({\"총량_효과_조원\": \"{:+,.0f}\", \"바텀업_효과_조원\": \"{:+,.0f}\", \"총량_CAGR기여_%p\": \"{:+.2f}\", \"바텀업_CAGR기여_%p\": \"{:+.2f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bcb289b",
   "metadata": {},
   "source": [
    "## 그림 1. 외생별 10년 CAGR 기여 — 총량 모형과 62 바텀업"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "940c67a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.227233Z",
     "iopub.status.busy": "2026-10-08T08:04:40.227233Z",
     "iopub.status.idle": "2026-10-08T08:04:40.372501Z",
     "shell.execute_reply": "2026-10-08T08:04:40.371998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "o = eff.sort_values(\"바텀업_CAGR기여_%p\")\n",
    "y = np.arange(len(o))\n",
    "fig, ax = plt.subplots(figsize=(8, 4.2))\n",
    "ax.barh(y + 0.19, o[\"총량_CAGR기여_%p\"], height=0.36, color=S1C, label=\"총량 예측모형\")\n",
    "ax.barh(y - 0.19, o[\"바텀업_CAGR기여_%p\"], height=0.36, color=S2C, label=\"62 바텀업\")\n",
    "for yi, (a, b) in enumerate(zip(o[\"총량_CAGR기여_%p\"], o[\"바텀업_CAGR기여_%p\"])):\n",
    "    ax.text(max(a, 0) + 0.02, yi + 0.19, f\"{a:+.2f}\", va=\"center\", fontsize=8, color=MUTED)\n",
    "    ax.text(max(b, 0) + 0.02, yi - 0.19, f\"{b:+.2f}\", va=\"center\", fontsize=8, color=MUTED)\n",
    "ax.set_yticks(y, [NAME[k] for k in o.index], color=INK)\n",
    "ax.axvline(0, color=\"#9aa3ad\", lw=0.8)\n",
    "ax.set_xlabel(\"2026–2035 연평균 성장률 기여(%p)\", color=MUTED)\n",
    "ax.grid(axis=\"x\", alpha=.25); ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
    "ax.legend(frameon=False, loc=\"lower right\")\n",
    "ax.set_title(\"외생별 실질부가가치 CAGR 기여(2035, S1)\", loc=\"left\", color=INK)\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig1_exog_effects.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6969b6b9",
   "metadata": {},
   "source": [
    "## 표 2. 시나리오 폭(max−min, 연평균) — 2026년과 2035년"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "464e38c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.374003Z",
     "iopub.status.busy": "2026-10-08T08:04:40.373502Z",
     "iopub.status.idle": "2026-10-08T08:04:40.379013Z",
     "shell.execute_reply": "2026-10-08T08:04:40.378508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_85274\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_85274_level0_col0\" class=\"col_heading level0 col0\" >폭_2026</th>\n",
       "      <th id=\"T_85274_level0_col1\" class=\"col_heading level0 col1\" >폭_2035</th>\n",
       "      <th id=\"T_85274_level0_col2\" class=\"col_heading level0 col2\" >판정</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_85274_level0_row0\" class=\"row_heading level0 row0\" >유가</th>\n",
       "      <td id=\"T_85274_row0_col0\" class=\"data row0 col0\" >25.16</td>\n",
       "      <td id=\"T_85274_row0_col1\" class=\"data row0 col1\" >51.750</td>\n",
       "      <td id=\"T_85274_row0_col2\" class=\"data row0 col2\" >영구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85274_level0_row1\" class=\"row_heading level0 row1\" >세계수요</th>\n",
       "      <td id=\"T_85274_row1_col0\" class=\"data row1 col0\" >1.79</td>\n",
       "      <td id=\"T_85274_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
       "      <td id=\"T_85274_row1_col2\" class=\"data row1 col2\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85274_level0_row2\" class=\"row_heading level0 row2\" >실질금리</th>\n",
       "      <td id=\"T_85274_row2_col0\" class=\"data row2 col0\" >4.80</td>\n",
       "      <td id=\"T_85274_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
       "      <td id=\"T_85274_row2_col2\" class=\"data row2 col2\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85274_level0_row3\" class=\"row_heading level0 row3\" >실질환율</th>\n",
       "      <td id=\"T_85274_row3_col0\" class=\"data row3 col0\" >11.87</td>\n",
       "      <td id=\"T_85274_row3_col1\" class=\"data row3 col1\" >0.004</td>\n",
       "      <td id=\"T_85274_row3_col2\" class=\"data row3 col2\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85274_level0_row4\" class=\"row_heading level0 row4\" >고용(취업자)</th>\n",
       "      <td id=\"T_85274_row4_col0\" class=\"data row4 col0\" >531.61</td>\n",
       "      <td id=\"T_85274_row4_col1\" class=\"data row4 col1\" >8.630</td>\n",
       "      <td id=\"T_85274_row4_col2\" class=\"data row4 col2\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_85274_level0_row5\" class=\"row_heading level0 row5\" >우리경기</th>\n",
       "      <td id=\"T_85274_row5_col0\" class=\"data row5 col0\" >2.94</td>\n",
       "      <td id=\"T_85274_row5_col1\" class=\"data row5 col1\" >0.000</td>\n",
       "      <td id=\"T_85274_row5_col2\" class=\"data row5 col2\" >일시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce319f1d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = per.copy()\n",
    "t2.index = [NAME[k] for k in t2.index]\n",
    "t2.to_csv(os.path.join(C.OUTPUT, \"table2_persistence.csv\"), encoding=\"utf-8-sig\")\n",
    "t2.style.format({\"폭_2026\": \"{:,.2f}\", \"폭_2035\": \"{:,.3f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f66c63",
   "metadata": {},
   "source": [
    "## 그림 2. 유가 경로와 시나리오 충격의 누적\n",
    "\n",
    "왼쪽은 연평균 유가(달러/배럴)이다. 과거 구간은 VARX에 들어간 값(BOKDPM 평활 `LOIL_us`)으로, 두바이유 기준이다.\n",
    "오른쪽은 S2·S3·S5의 유가 충격(`oil_g` 편차, 분기)과 그 누적합인 수준차(S − S1, 100·ln)를 나눠 보인다. 충격은 8분기 뒤 사라지지만 수준차는 남는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0609d7b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.379513Z",
     "iopub.status.busy": "2026-10-08T08:04:40.379513Z",
     "iopub.status.idle": "2026-10-08T08:04:40.660036Z",
     "shell.execute_reply": "2026-10-08T08:04:40.659533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsge = pd.read_csv(C.DSGE_EXOG, encoding=\"utf-8-sig\")\n",
    "SC = {\"S1\": S1C, \"S2\": S2C, \"S3\": \"#1baf7a\", \"S4\": \"#eda100\", \"S5\": \"#e87ba4\"}\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12, 4.0), gridspec_kw={\"width_ratios\": [1.6, 1, 1]})\n",
    "ax = axes[0]\n",
    "h = dollars.loc[:2025, \"S1\"]\n",
    "ax.plot(h.index, h.values, color=INK, lw=2, label=\"과거(두바이 기준)\")\n",
    "for s in [\"S1\", \"S2\", \"S3\", \"S4\", \"S5\"]:\n",
    "    f = dollars.loc[2025:, s]\n",
    "    ax.plot(f.index, f.values, color=SC[s], lw=2, ls=\"--\" if s == \"S4\" else \"-\", label=s)\n",
    "for s, dy in [(\"S5\", 0), (\"S1\", 4), (\"S3\", -4), (\"S2\", 0)]:\n",
    "    v = dollars.loc[2035, s]\n",
    "    ax.text(2035.4, v + dy, (\"S1·S4\" if s == \"S1\" else s) + f\" ${v:.0f}\", va=\"center\", fontsize=8, color=INK)\n",
    "ax.set_xlim(2000, 2039); ax.set_ylabel(\"달러/배럴(연평균)\", color=MUTED)\n",
    "ax.grid(alpha=.25); ax.spines[[\"top\", \"right\"]].set_visible(False); ax.legend(frameon=False, fontsize=8, loc=\"upper left\")\n",
    "ax.set_title(\"(가) 유가 수준\", loc=\"left\", color=INK)\n",
    "q = np.arange(1, 41)\n",
    "g1 = dsge[dsge[\"시나리오\"] == \"S1\"][\"oil_g\"].to_numpy()[:40]\n",
    "for s in [\"S2\", \"S3\", \"S5\"]:\n",
    "    dev = dsge[dsge[\"시나리오\"] == s][\"oil_g\"].to_numpy()[:40] - g1\n",
    "    axes[1].plot(q, dev, color=SC[s], lw=2, label=s)\n",
    "    axes[2].plot(q, np.cumsum(dev), color=SC[s], lw=2, label=s)\n",
    "for a, t, yl in [(axes[1], \"(나) 충격: oil_g 편차\", \"분기 성장률 편차(100·Δln)\"), (axes[2], \"(다) 수준차: 편차 누적합\", \"S - S1(100·ln)\")]:\n",
    "    a.axhline(0, color=\"#9aa3ad\", lw=0.8); a.set_xlabel(\"2026Q1 이후 분기\", color=MUTED); a.set_ylabel(yl, color=MUTED)\n",
    "    a.grid(alpha=.25); a.spines[[\"top\", \"right\"]].set_visible(False); a.legend(frameon=False, fontsize=8); a.set_title(t, loc=\"left\", color=INK)\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig2_oil_paths.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b511e6e",
   "metadata": {},
   "source": [
    "## 표 3. 유가의 실질부가가치 순효과(2035년, S1) — 총량 모형과 62 바텀업"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed7b240d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.660036Z",
     "iopub.status.busy": "2026-10-08T08:04:40.660036Z",
     "iopub.status.idle": "2026-10-08T08:04:40.666513Z",
     "shell.execute_reply": "2026-10-08T08:04:40.666513Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c3893\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c3893_level0_col0\" class=\"col_heading level0 col0\" >2025(조원)</th>\n",
       "      <th id=\"T_c3893_level0_col1\" class=\"col_heading level0 col1\" >2035(조원)</th>\n",
       "      <th id=\"T_c3893_level0_col2\" class=\"col_heading level0 col2\" >유가 고정 2035(조원)</th>\n",
       "      <th id=\"T_c3893_level0_col3\" class=\"col_heading level0 col3\" >유가 효과(조원)</th>\n",
       "      <th id=\"T_c3893_level0_col4\" class=\"col_heading level0 col4\" >유가 효과(%)</th>\n",
       "      <th id=\"T_c3893_level0_col5\" class=\"col_heading level0 col5\" >CAGR(%)</th>\n",
       "      <th id=\"T_c3893_level0_col6\" class=\"col_heading level0 col6\" >유가 고정 CAGR(%)</th>\n",
       "      <th id=\"T_c3893_level0_col7\" class=\"col_heading level0 col7\" >CAGR 기여(%p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >모형</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c3893_level0_row0\" class=\"row_heading level0 row0\" >총량 예측모형</th>\n",
       "      <td id=\"T_c3893_row0_col0\" class=\"data row0 col0\" >2,326</td>\n",
       "      <td id=\"T_c3893_row0_col1\" class=\"data row0 col1\" >2,970</td>\n",
       "      <td id=\"T_c3893_row0_col2\" class=\"data row0 col2\" >2,956</td>\n",
       "      <td id=\"T_c3893_row0_col3\" class=\"data row0 col3\" >+14.6</td>\n",
       "      <td id=\"T_c3893_row0_col4\" class=\"data row0 col4\" >+0.49</td>\n",
       "      <td id=\"T_c3893_row0_col5\" class=\"data row0 col5\" >2.47</td>\n",
       "      <td id=\"T_c3893_row0_col6\" class=\"data row0 col6\" >2.42</td>\n",
       "      <td id=\"T_c3893_row0_col7\" class=\"data row0 col7\" >+0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c3893_level0_row1\" class=\"row_heading level0 row1\" >62 바텀업</th>\n",
       "      <td id=\"T_c3893_row1_col0\" class=\"data row1 col0\" >2,178</td>\n",
       "      <td id=\"T_c3893_row1_col1\" class=\"data row1 col1\" >3,507</td>\n",
       "      <td id=\"T_c3893_row1_col2\" class=\"data row1 col2\" >3,442</td>\n",
       "      <td id=\"T_c3893_row1_col3\" class=\"data row1 col3\" >+65.5</td>\n",
       "      <td id=\"T_c3893_row1_col4\" class=\"data row1 col4\" >+1.87</td>\n",
       "      <td id=\"T_c3893_row1_col5\" class=\"data row1 col5\" >4.88</td>\n",
       "      <td id=\"T_c3893_row1_col6\" class=\"data row1 col6\" >4.68</td>\n",
       "      <td id=\"T_c3893_row1_col7\" class=\"data row1 col7\" >+0.197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce31bb500>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_cmp.to_csv(os.path.join(C.OUTPUT, \"table3_oil_effect.csv\"), encoding=\"utf-8-sig\")\n",
    "oil_cmp.style.format({c: \"{:,.0f}\" for c in [\"2025(조원)\", \"2035(조원)\", \"유가 고정 2035(조원)\"]} |\n",
    "                     {\"유가 효과(조원)\": \"{:+,.1f}\", \"유가 효과(%)\": \"{:+.2f}\", \"CAGR(%)\": \"{:.2f}\", \"유가 고정 CAGR(%)\": \"{:.2f}\", \"CAGR 기여(%p)\": \"{:+.3f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73d7eb5",
   "metadata": {},
   "source": [
    "## 표 4. 유가에 가장 민감한 산업 — 36·62·69분류(10년 로그성장 기여, %p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f359c61c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.667516Z",
     "iopub.status.busy": "2026-10-08T08:04:40.667516Z",
     "iopub.status.idle": "2026-10-08T08:04:40.675569Z",
     "shell.execute_reply": "2026-10-08T08:04:40.675569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_5add1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5add1_level0_col0\" class=\"col_heading level0 col0\" >유가 영향 1위</th>\n",
       "      <th id=\"T_5add1_level0_col1\" class=\"col_heading level0 col1\" >기여(%p)</th>\n",
       "      <th id=\"T_5add1_level0_col2\" class=\"col_heading level0 col2\" >수혜(+) 상위 3</th>\n",
       "      <th id=\"T_5add1_level0_col3\" class=\"col_heading level0 col3\" >피해(−) 상위 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >분류</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5add1_level0_row0\" class=\"row_heading level0 row0\" >36</th>\n",
       "      <td id=\"T_5add1_row0_col0\" class=\"data row0 col0\" >코크스 및 석유정제품 제조업</td>\n",
       "      <td id=\"T_5add1_row0_col1\" class=\"data row0 col1\" >+41.06</td>\n",
       "      <td id=\"T_5add1_row0_col2\" class=\"data row0 col2\" >기계 및 장비 제조업(+14.0) · 예술, 스포츠 및 여가관련 서비스업(+12.1) · 운송장비 제조업(+10.8)</td>\n",
       "      <td id=\"T_5add1_row0_col3\" class=\"data row0 col3\" >주거용 건물 건설업(-6.1) · 토목건설업(-6.0) · 통신업(-4.7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5add1_level0_row1\" class=\"row_heading level0 row1\" >62</th>\n",
       "      <td id=\"T_5add1_row1_col0\" class=\"data row1 col0\" >14.석탄ㆍ석유제품</td>\n",
       "      <td id=\"T_5add1_row1_col1\" class=\"data row1 col1\" >+31.36</td>\n",
       "      <td id=\"T_5add1_row1_col2\" class=\"data row1 col2\" >25.기타기계ㆍ장비(+11.3) · 59.문화서비스업(+11.2) · 27.기타 운송장비(+10.8)</td>\n",
       "      <td id=\"T_5add1_row1_col3\" class=\"data row1 col3\" >43.통신업(-4.4) · 29.전기ㆍ가스ㆍ증기ㆍ공기조절공급업(-4.0) · 33.건설업(-3.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5add1_level0_row2\" class=\"row_heading level0 row2\" >69</th>\n",
       "      <td id=\"T_5add1_row2_col0\" class=\"data row2 col0\" >14.석탄ㆍ석유제품</td>\n",
       "      <td id=\"T_5add1_row2_col1\" class=\"data row2 col1\" >+30.83</td>\n",
       "      <td id=\"T_5add1_row2_col2\" class=\"data row2 col2\" >이차전지(+15.8) · 25.기타기계ㆍ장비(+12.0) · 27.기타 운송장비(잔여)(+10.7)</td>\n",
       "      <td id=\"T_5add1_row2_col3\" class=\"data row2 col3\" >토목건설(-5.1) · 건물건설건축보수(-4.8) · 43.통신업(-4.2)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce329f140>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "for r in (\"36\", \"62\", \"69\"):\n",
    "    g = orank[orank[\"분류\"] == r]\n",
    "    top = g[g[\"|기여| 최대\"]].iloc[0]\n",
    "    plus = g[(g[\"구분\"] == \"상위(+)\") & ~g[\"|기여| 최대\"]].head(3)\n",
    "    minus = g[g[\"구분\"] == \"하위(−)\"].head(3)\n",
    "    rows.append({\"분류\": r, \"유가 영향 1위\": top[\"산업\"], \"기여(%p)\": top[\"유가 기여(%p)\"],\n",
    "                 \"수혜(+) 상위 3\": \" · \".join(f\"{a}({v:+.1f})\" for a, v in zip(plus[\"산업\"], plus[\"유가 기여(%p)\"])),\n",
    "                 \"피해(−) 상위 3\": \" · \".join(f\"{a}({v:+.1f})\" for a, v in zip(minus[\"산업\"], minus[\"유가 기여(%p)\"]))})\n",
    "t4 = pd.DataFrame(rows).set_index(\"분류\")\n",
    "t4.to_csv(os.path.join(C.OUTPUT, \"table4_oil_sensitive.csv\"), encoding=\"utf-8-sig\")\n",
    "t4.style.format({\"기여(%p)\": \"{:+.2f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ecd9a8",
   "metadata": {},
   "source": [
    "## 그림 3. 62개 산업의 유가 기여(S1, 10년 로그성장 %p) — 상·하위 10개"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "733656c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.676573Z",
     "iopub.status.busy": "2026-10-08T08:04:40.676573Z",
     "iopub.status.idle": "2026-10-08T08:04:40.881865Z",
     "shell.execute_reply": "2026-10-08T08:04:40.881865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x620 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = SH[\"62\"][\"유가\"].sort_values()\n",
    "s = pd.concat([s.head(10), s.tail(10)])\n",
    "fig, ax = plt.subplots(figsize=(8, 6.2))\n",
    "ax.barh(range(len(s)), s.values, color=[S1C if v > 0 else S2C for v in s.values], height=0.7)\n",
    "ax.set_yticks(range(len(s)), [i.split(\".\", 1)[1] for i in s.index], fontsize=8, color=INK)\n",
    "for i, v in enumerate(s.values):\n",
    "    ax.text(v + (0.4 if v > 0 else -0.4), i, f\"{v:+.1f}\", va=\"center\", ha=\"left\" if v > 0 else \"right\", fontsize=7.5, color=MUTED)\n",
    "ax.axvline(0, color=\"#9aa3ad\", lw=0.8)\n",
    "ax.set_xlim(s.min() - 3, s.max() + 4)\n",
    "ax.set_xlabel(\"유가 기여(10년 로그성장, %p) — 파랑: 상승 기여, 주황: 하락 기여\", color=MUTED)\n",
    "ax.grid(axis=\"x\", alpha=.25); ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
    "ax.set_title(\"62분류 산업별 유가 기여(선형 SHAP, S1)\", loc=\"left\", color=INK)\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig3_oil_shap_62.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17225dc3",
   "metadata": {},
   "source": [
    "## 표 5. 유가 과거값의 원천별 결과 — 공표 입력·FRED 두바이·FRED WTI\n",
    "\n",
    "공표 입력의 유가 과거값은 두바이유와 일치하므로 첫째·둘째 행이 같다. 셋째 행은 문서상 정의(WTI)대로 추정했을 때의 결과이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27fe754d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.882868Z",
     "iopub.status.busy": "2026-10-08T08:04:40.882868Z",
     "iopub.status.idle": "2026-10-08T08:04:40.887882Z",
     "shell.execute_reply": "2026-10-08T08:04:40.887882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_f9f14\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f9f14_level0_col0\" class=\"col_heading level0 col0\" >총량 2035(조원)</th>\n",
       "      <th id=\"T_f9f14_level0_col1\" class=\"col_heading level0 col1\" >총량 유가효과(조원)</th>\n",
       "      <th id=\"T_f9f14_level0_col2\" class=\"col_heading level0 col2\" >총량 유가효과(%)</th>\n",
       "      <th id=\"T_f9f14_level0_col3\" class=\"col_heading level0 col3\" >62 2035(조원)</th>\n",
       "      <th id=\"T_f9f14_level0_col4\" class=\"col_heading level0 col4\" >62 유가효과(조원)</th>\n",
       "      <th id=\"T_f9f14_level0_col5\" class=\"col_heading level0 col5\" >62 유가효과(%)</th>\n",
       "      <th id=\"T_f9f14_level0_col6\" class=\"col_heading level0 col6\" >62 실질금리효과(조원)</th>\n",
       "      <th id=\"T_f9f14_level0_col7\" class=\"col_heading level0 col7\" >석유제품 유가 SHAP(%p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >유가 과거값</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f9f14_level0_row0\" class=\"row_heading level0 row0\" >공표 입력(exog_hist)</th>\n",
       "      <td id=\"T_f9f14_row0_col0\" class=\"data row0 col0\" >2,970.38</td>\n",
       "      <td id=\"T_f9f14_row0_col1\" class=\"data row0 col1\" >14.60</td>\n",
       "      <td id=\"T_f9f14_row0_col2\" class=\"data row0 col2\" >0.49</td>\n",
       "      <td id=\"T_f9f14_row0_col3\" class=\"data row0 col3\" >3,507.00</td>\n",
       "      <td id=\"T_f9f14_row0_col4\" class=\"data row0 col4\" >65.47</td>\n",
       "      <td id=\"T_f9f14_row0_col5\" class=\"data row0 col5\" >1.87</td>\n",
       "      <td id=\"T_f9f14_row0_col6\" class=\"data row0 col6\" >354.21</td>\n",
       "      <td id=\"T_f9f14_row0_col7\" class=\"data row0 col7\" >31.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f9f14_level0_row1\" class=\"row_heading level0 row1\" >FRED 두바이 100·Δln</th>\n",
       "      <td id=\"T_f9f14_row1_col0\" class=\"data row1 col0\" >2,970.38</td>\n",
       "      <td id=\"T_f9f14_row1_col1\" class=\"data row1 col1\" >14.60</td>\n",
       "      <td id=\"T_f9f14_row1_col2\" class=\"data row1 col2\" >0.49</td>\n",
       "      <td id=\"T_f9f14_row1_col3\" class=\"data row1 col3\" >3,507.00</td>\n",
       "      <td id=\"T_f9f14_row1_col4\" class=\"data row1 col4\" >65.47</td>\n",
       "      <td id=\"T_f9f14_row1_col5\" class=\"data row1 col5\" >1.87</td>\n",
       "      <td id=\"T_f9f14_row1_col6\" class=\"data row1 col6\" >354.21</td>\n",
       "      <td id=\"T_f9f14_row1_col7\" class=\"data row1 col7\" >31.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f9f14_level0_row2\" class=\"row_heading level0 row2\" >FRED WTI 100·Δln</th>\n",
       "      <td id=\"T_f9f14_row2_col0\" class=\"data row2 col0\" >2,923.70</td>\n",
       "      <td id=\"T_f9f14_row2_col1\" class=\"data row2 col1\" >5.73</td>\n",
       "      <td id=\"T_f9f14_row2_col2\" class=\"data row2 col2\" >0.20</td>\n",
       "      <td id=\"T_f9f14_row2_col3\" class=\"data row2 col3\" >3,511.48</td>\n",
       "      <td id=\"T_f9f14_row2_col4\" class=\"data row2 col4\" >57.11</td>\n",
       "      <td id=\"T_f9f14_row2_col5\" class=\"data row2 col5\" >1.63</td>\n",
       "      <td id=\"T_f9f14_row2_col6\" class=\"data row2 col6\" >342.82</td>\n",
       "      <td id=\"T_f9f14_row2_col7\" class=\"data row2 col7\" >34.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce55fd3a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.to_csv(os.path.join(C.OUTPUT, \"table5_oil_source.csv\"), encoding=\"utf-8-sig\")\n",
    "var.style.format(\"{:,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eacd49a8",
   "metadata": {},
   "source": [
    "## 표 6. 두바이유 교체 시산(축약 OLS, 원 시산 재현)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d2ecacde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.888882Z",
     "iopub.status.busy": "2026-10-08T08:04:40.888882Z",
     "iopub.status.idle": "2026-10-08T08:04:40.893888Z",
     "shell.execute_reply": "2026-10-08T08:04:40.893888Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "로그차분 상관 corr(ΔWTI, Δ두바이): 원 시산 표본 0.9540 (2000Q1–2026Q3) · 2000Q1–2025Q4 0.9610\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_35f49\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_35f49_level0_col0\" class=\"col_heading level0 col0\" >WTI계수</th>\n",
       "      <th id=\"T_35f49_level0_col1\" class=\"col_heading level0 col1\" >DUB계수</th>\n",
       "      <th id=\"T_35f49_level0_col2\" class=\"col_heading level0 col2\" >WTI기여</th>\n",
       "      <th id=\"T_35f49_level0_col3\" class=\"col_heading level0 col3\" >DUB기여</th>\n",
       "      <th id=\"T_35f49_level0_col4\" class=\"col_heading level0 col4\" >변화</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >산업</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_35f49_level0_row0\" class=\"row_heading level0 row0\" >14.석탄ㆍ석유제품</th>\n",
       "      <td id=\"T_35f49_row0_col0\" class=\"data row0 col0\" >0.290</td>\n",
       "      <td id=\"T_35f49_row0_col1\" class=\"data row0 col1\" >0.308</td>\n",
       "      <td id=\"T_35f49_row0_col2\" class=\"data row0 col2\" >+24.9</td>\n",
       "      <td id=\"T_35f49_row0_col3\" class=\"data row0 col3\" >+26.5</td>\n",
       "      <td id=\"T_35f49_row0_col4\" class=\"data row0 col4\" >+1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35f49_level0_row1\" class=\"row_heading level0 row1\" >25.기타기계ㆍ장비</th>\n",
       "      <td id=\"T_35f49_row1_col0\" class=\"data row1 col0\" >0.104</td>\n",
       "      <td id=\"T_35f49_row1_col1\" class=\"data row1 col1\" >0.109</td>\n",
       "      <td id=\"T_35f49_row1_col2\" class=\"data row1 col2\" >+8.9</td>\n",
       "      <td id=\"T_35f49_row1_col3\" class=\"data row1 col3\" >+9.3</td>\n",
       "      <td id=\"T_35f49_row1_col4\" class=\"data row1 col4\" >+0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35f49_level0_row2\" class=\"row_heading level0 row2\" >27.기타 운송장비</th>\n",
       "      <td id=\"T_35f49_row2_col0\" class=\"data row2 col0\" >0.085</td>\n",
       "      <td id=\"T_35f49_row2_col1\" class=\"data row2 col1\" >0.086</td>\n",
       "      <td id=\"T_35f49_row2_col2\" class=\"data row2 col2\" >+7.3</td>\n",
       "      <td id=\"T_35f49_row2_col3\" class=\"data row2 col3\" >+7.4</td>\n",
       "      <td id=\"T_35f49_row2_col4\" class=\"data row2 col4\" >+0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35f49_level0_row3\" class=\"row_heading level0 row3\" >33.건설업</th>\n",
       "      <td id=\"T_35f49_row3_col0\" class=\"data row3 col0\" >-0.012</td>\n",
       "      <td id=\"T_35f49_row3_col1\" class=\"data row3 col1\" >-0.003</td>\n",
       "      <td id=\"T_35f49_row3_col2\" class=\"data row3 col2\" >-1.0</td>\n",
       "      <td id=\"T_35f49_row3_col3\" class=\"data row3 col3\" >-0.3</td>\n",
       "      <td id=\"T_35f49_row3_col4\" class=\"data row3 col4\" >+0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35f49_level0_row4\" class=\"row_heading level0 row4\" >43.통신업</th>\n",
       "      <td id=\"T_35f49_row4_col0\" class=\"data row4 col0\" >-0.001</td>\n",
       "      <td id=\"T_35f49_row4_col1\" class=\"data row4 col1\" >-0.010</td>\n",
       "      <td id=\"T_35f49_row4_col2\" class=\"data row4 col2\" >-0.1</td>\n",
       "      <td id=\"T_35f49_row4_col3\" class=\"data row4 col3\" >-0.9</td>\n",
       "      <td id=\"T_35f49_row4_col4\" class=\"data row4 col4\" >-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_35f49_level0_row5\" class=\"row_heading level0 row5\" >46.금융업</th>\n",
       "      <td id=\"T_35f49_row5_col0\" class=\"data row5 col0\" >0.016</td>\n",
       "      <td id=\"T_35f49_row5_col1\" class=\"data row5 col1\" >0.011</td>\n",
       "      <td id=\"T_35f49_row5_col2\" class=\"data row5 col2\" >+1.4</td>\n",
       "      <td id=\"T_35f49_row5_col3\" class=\"data row5 col3\" >+0.9</td>\n",
       "      <td id=\"T_35f49_row5_col4\" class=\"data row5 col4\" >-0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce3290530>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t6 = dub.copy()\n",
    "t6.to_csv(os.path.join(C.OUTPUT, \"table6_dubai.csv\"), encoding=\"utf-8-sig\")\n",
    "print(f\"로그차분 상관 corr(ΔWTI, Δ두바이): 원 시산 표본 {bm.loc['원 시산 표본(파일 전체)', 'corr']:.4f} \"\n",
    "      f\"({bm.loc['원 시산 표본(파일 전체)', '첫분기']}–{bm.loc['원 시산 표본(파일 전체)', '끝분기']}) · \"\n",
    "      f\"2000Q1–2025Q4 {bm.loc['2000Q1–2025Q4', 'corr']:.4f}\")\n",
    "t6.style.format({\"WTI계수\": \"{:.3f}\", \"DUB계수\": \"{:.3f}\", \"WTI기여\": \"{:+.1f}\", \"DUB기여\": \"{:+.1f}\", \"변화\": \"{:+.1f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468070b4",
   "metadata": {},
   "source": [
    "## 공표 문구 점검 — 정정·쟁점\n",
    "\n",
    "카드 본문의 서술 가운데 계산과 맞지 않거나 근거가 달라진 항목이다. 아래 검증 셀에는 계산과 일치하는 공표 수치만 넣고, 이 표의 '불일치'·'쟁점' 항목은\n",
    "README의 정정 기록에 같은 내용으로 적었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed27174b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.894903Z",
     "iopub.status.busy": "2026-10-08T08:04:40.894903Z",
     "iopub.status.idle": "2026-10-08T08:04:40.903331Z",
     "shell.execute_reply": "2026-10-08T08:04:40.903331Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_776fa\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_776fa_level0_col0\" class=\"col_heading level0 col0\" >항목</th>\n",
       "      <th id=\"T_776fa_level0_col1\" class=\"col_heading level0 col1\" >공표 문구</th>\n",
       "      <th id=\"T_776fa_level0_col2\" class=\"col_heading level0 col2\" >계산 결과</th>\n",
       "      <th id=\"T_776fa_level0_col3\" class=\"col_heading level0 col3\" >판정</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row0_col0\" class=\"data row0 col0\" >유가 원천(유가 카드 ①·각주, 그림 축)</td>\n",
       "      <td id=\"T_776fa_row0_col1\" class=\"data row0 col1\" >유가 = LOIL_us = FRED POILWTIUSDM(WTI)</td>\n",
       "      <td id=\"T_776fa_row0_col2\" class=\"data row0 col2\" >VARX 유가 과거값 = 100·ln(두바이유), 최대 차이 0.0001; WTI와는 -30.3~+19.6(100·ln) 차이</td>\n",
       "      <td id=\"T_776fa_row0_col3\" class=\"data row0 col3\" >불일치(정정)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row1_col0\" class=\"data row1 col0\" >두바이유 교체 권고(유가 카드 ⑧-5)</td>\n",
       "      <td id=\"T_776fa_row1_col1\" class=\"data row1 col1\" >POILWTIUSDM → POILDUBUSDM 한 줄 교체</td>\n",
       "      <td id=\"T_776fa_row1_col2\" class=\"data row1 col2\" >추정 입력이 이미 두바이유이다. WTI로 추정하면 총량 유가효과 +14.6 → +5.7조원</td>\n",
       "      <td id=\"T_776fa_row1_col3\" class=\"data row1 col3\" >전제 불성립(정정)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row2_col0\" class=\"data row2 col0\" >유가 과거 수준(유가 카드 ② 부제)</td>\n",
       "      <td id=\"T_776fa_row2_col1\" class=\"data row2 col1\" >2008년 $109 → 2025년 $69</td>\n",
       "      <td id=\"T_776fa_row2_col2\" class=\"data row2 col2\" >연평균 2008년 $89.1, $109는 2012년($109.0); 2025년 $69.2</td>\n",
       "      <td id=\"T_776fa_row2_col3\" class=\"data row2 col3\" >불일치(연도 표기)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row3_col0\" class=\"data row3 col0\" >바텀업 증폭(외생 카드 ⑤)</td>\n",
       "      <td id=\"T_776fa_row3_col1\" class=\"data row3 col1\" >유가·실질금리는 바텀업이 총량의 약 2배(유가 65 vs 15조)</td>\n",
       "      <td id=\"T_776fa_row3_col2\" class=\"data row3 col2\" >실질금리 2.2배, 유가 4.5배</td>\n",
       "      <td id=\"T_776fa_row3_col3\" class=\"data row3 col3\" >유가 부분 불일치</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row4_col0\" class=\"data row4 col0\" >바텀업 증폭(유가 카드 ④)</td>\n",
       "      <td id=\"T_776fa_row4_col1\" class=\"data row4 col1\" >바텀업이 유가 효과를 약 4배 증폭</td>\n",
       "      <td id=\"T_776fa_row4_col2\" class=\"data row4 col2\" >4.49배</td>\n",
       "      <td id=\"T_776fa_row4_col3\" class=\"data row4 col3\" >부합</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row5_col0\" class=\"data row5 col0\" >실질금리 대 유가(외생 카드 ②)</td>\n",
       "      <td id=\"T_776fa_row5_col1\" class=\"data row5 col1\" >실질금리가 유가의 열 배</td>\n",
       "      <td id=\"T_776fa_row5_col2\" class=\"data row5 col2\" >총량 11.1배, 바텀업 5.4배</td>\n",
       "      <td id=\"T_776fa_row5_col3\" class=\"data row5 col3\" >총량 기준 부합</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row6_col0\" class=\"data row6 col0\" >WTI·두바이 상관(유가 카드 ⑥)</td>\n",
       "      <td id=\"T_776fa_row6_col1\" class=\"data row6 col1\" >로그성장 상관 0.954</td>\n",
       "      <td id=\"T_776fa_row6_col2\" class=\"data row6 col2\" >0.9540(2000Q1–2026Q3, 끝 분기는 7월 한 달); 원 출력 표기 기간 2000Q1–2025Q4로는 0.9610</td>\n",
       "      <td id=\"T_776fa_row6_col3\" class=\"data row6 col3\" >값 재현, 기간 표기 쟁점</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row7_col0\" class=\"data row7 col0\" >스프레드(유가 카드 ⑥)</td>\n",
       "      <td id=\"T_776fa_row7_col1\" class=\"data row7 col1\" >최근 스프레드 약 $3</td>\n",
       "      <td id=\"T_776fa_row7_col2\" class=\"data row7 col2\" >2026년 7월 WTI $79.7 − 두바이 $77.1 = $2.6; 2025Q4는 두바이가 $3.9 높음</td>\n",
       "      <td id=\"T_776fa_row7_col3\" class=\"data row7 col3\" >시점 의존(쟁점)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row8_col0\" class=\"data row8 col0\" >36 수혜(+) 산업(유가 카드 ③)</td>\n",
       "      <td id=\"T_776fa_row8_col1\" class=\"data row8 col1\" >기계·운송·전기장비</td>\n",
       "      <td id=\"T_776fa_row8_col2\" class=\"data row8 col2\" >상위(+) 2–5위: 기계 및 장비 제조업(+14.0) · 예술, 스포츠 및 여가관련 서비스업(+12.1) · 운송장비 제조업(+10.8) · 전기장비 제조업(+9.9)</td>\n",
       "      <td id=\"T_776fa_row8_col3\" class=\"data row8 col3\" >선택 표기(예술·스포츠 생략)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row9_col0\" class=\"data row9 col0\" >69 피해(−) 산업(유가 카드 ③)</td>\n",
       "      <td id=\"T_776fa_row9_col1\" class=\"data row9 col1\" >토목·통신·전기가스</td>\n",
       "      <td id=\"T_776fa_row9_col2\" class=\"data row9 col2\" >하위(−) 1–4위: 토목건설(-5.1) · 건물건설건축보수(-4.8) · 43.통신업(-4.2) · 29.전기ㆍ가스ㆍ증기ㆍ공기조절공급업(잔여)(-3.8)</td>\n",
       "      <td id=\"T_776fa_row9_col3\" class=\"data row9 col3\" >선택 표기(건물건설 생략)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row10_col0\" class=\"data row10 col0\" >69 이차전지(유가 카드 ③)</td>\n",
       "      <td id=\"T_776fa_row10_col1\" class=\"data row10 col1\" >모산업(전기장비) 계수를 IO 몫으로 물려받은 결과</td>\n",
       "      <td id=\"T_776fa_row10_col2\" class=\"data row10 col2\" >별도 방정식으로 추정; 유가 기여 +15.8 vs 전기장비(잔여) +10.0, 분기 성장률 상관 0.12</td>\n",
       "      <td id=\"T_776fa_row10_col3\" class=\"data row10 col3\" >근거 미확인(쟁점)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_776fa_row11_col0\" class=\"data row11 col0\" >실질금리 비중(외생 카드 ②)</td>\n",
       "      <td id=\"T_776fa_row11_col1\" class=\"data row11 col1\" >실질금리 하나가 바텀업 성장(연 4.88%)의 약 1/4</td>\n",
       "      <td id=\"T_776fa_row11_col2\" class=\"data row11 col2\" >1.11 / 4.88 = 0.23</td>\n",
       "      <td id=\"T_776fa_row11_col3\" class=\"data row11 col3\" >부합</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce676bcb0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = eff\n",
    "rr_tot, rr_bu = e.loc[\"실질금리\", \"총량_효과_조원\"] / e.loc[\"유가\", \"총량_효과_조원\"], e.loc[\"실질금리\", \"바텀업_효과_조원\"] / e.loc[\"유가\", \"바텀업_효과_조원\"]\n",
    "amp_oil, amp_rr = e.loc[\"유가\", \"바텀업_효과_조원\"] / e.loc[\"유가\", \"총량_효과_조원\"], e.loc[\"실질금리\", \"바텀업_효과_조원\"] / e.loc[\"실질금리\", \"총량_효과_조원\"]\n",
    "g36 = orank[(orank[\"분류\"] == \"36\") & (orank[\"구분\"] == \"상위(+)\")]\n",
    "g69 = orank[(orank[\"분류\"] == \"69\") & (orank[\"구분\"] == \"하위(−)\")]\n",
    "b_full, b_25 = bm.loc[\"원 시산 표본(파일 전체)\"], bm.loc[\"2000Q1–2025Q4\"]\n",
    "claims = pd.DataFrame([\n",
    "    (\"유가 원천(유가 카드 ①·각주, 그림 축)\", \"유가 = LOIL_us = FRED POILWTIUSDM(WTI)\",\n",
    "     f\"VARX 유가 과거값 = 100·ln(두바이유), 최대 차이 {src_chk.loc['VARX 유가 − 100·ln(두바이)', '최대 |차이|']:.4f}; WTI와는 {src_chk.loc['VARX 유가 − 100·ln(WTI)', '최소']:+.1f}~{src_chk.loc['VARX 유가 − 100·ln(WTI)', '최대']:+.1f}(100·ln) 차이\",\n",
    "     \"불일치(정정)\"),\n",
    "    (\"두바이유 교체 권고(유가 카드 ⑧-5)\", \"POILWTIUSDM → POILDUBUSDM 한 줄 교체\",\n",
    "     f\"추정 입력이 이미 두바이유이다. WTI로 추정하면 총량 유가효과 {var.iloc[0]['총량 유가효과(조원)']:+.1f} → {var.iloc[2]['총량 유가효과(조원)']:+.1f}조원\",\n",
    "     \"전제 불성립(정정)\"),\n",
    "    (\"유가 과거 수준(유가 카드 ② 부제)\", \"2008년 $109 → 2025년 $69\",\n",
    "     f\"연평균 2008년 ${dollars.loc[2008, 'S1']:.1f}, $109는 2012년(${dollars.loc[2012, 'S1']:.1f}); 2025년 ${dollars.loc[2025, 'S1']:.1f}\", \"불일치(연도 표기)\"),\n",
    "    (\"바텀업 증폭(외생 카드 ⑤)\", \"유가·실질금리는 바텀업이 총량의 약 2배(유가 65 vs 15조)\",\n",
    "     f\"실질금리 {amp_rr:.1f}배, 유가 {amp_oil:.1f}배\", \"유가 부분 불일치\"),\n",
    "    (\"바텀업 증폭(유가 카드 ④)\", \"바텀업이 유가 효과를 약 4배 증폭\", f\"{amp_oil:.2f}배\", \"부합\"),\n",
    "    (\"실질금리 대 유가(외생 카드 ②)\", \"실질금리가 유가의 열 배\", f\"총량 {rr_tot:.1f}배, 바텀업 {rr_bu:.1f}배\", \"총량 기준 부합\"),\n",
    "    (\"WTI·두바이 상관(유가 카드 ⑥)\", \"로그성장 상관 0.954\",\n",
    "     f\"{b_full['corr']:.4f}({b_full['첫분기']}–{b_full['끝분기']}, 끝 분기는 7월 한 달); 원 출력 표기 기간 2000Q1–2025Q4로는 {b_25['corr']:.4f}\",\n",
    "     \"값 재현, 기간 표기 쟁점\"),\n",
    "    (\"스프레드(유가 카드 ⑥)\", \"최근 스프레드 약 $3\",\n",
    "     f\"2026년 7월 WTI ${b_full['WTI_$']:.1f} − 두바이 ${b_full['DUB_$']:.1f} = ${b_full['WTI_$'] - b_full['DUB_$']:.1f}; 2025Q4는 두바이가 ${b_25['DUB_$'] - b_25['WTI_$']:.1f} 높음\",\n",
    "     \"시점 의존(쟁점)\"),\n",
    "    (\"36 수혜(+) 산업(유가 카드 ③)\", \"기계·운송·전기장비\",\n",
    "     \"상위(+) 2–5위: \" + \" · \".join(f\"{a}({v:+.1f})\" for a, v in zip(g36[\"산업\"].iloc[1:], g36[\"유가 기여(%p)\"].iloc[1:])), \"선택 표기(예술·스포츠 생략)\"),\n",
    "    (\"69 피해(−) 산업(유가 카드 ③)\", \"토목·통신·전기가스\",\n",
    "     \"하위(−) 1–4위: \" + \" · \".join(f\"{a}({v:+.1f})\" for a, v in zip(g69[\"산업\"].iloc[:4], g69[\"유가 기여(%p)\"].iloc[:4])), \"선택 표기(건물건설 생략)\"),\n",
    "    (\"69 이차전지(유가 카드 ③)\", \"모산업(전기장비) 계수를 IO 몫으로 물려받은 결과\",\n",
    "     f\"별도 방정식으로 추정; 유가 기여 {bat.loc['이차전지', '유가 기여(%p)']:+.1f} vs 전기장비(잔여) {bat.loc['24.전기장비(잔여)', '유가 기여(%p)']:+.1f}, 분기 성장률 상관 {bat.loc['24.전기장비(잔여)', '분기 성장률 상관(이차전지와)']:.2f}\",\n",
    "     \"근거 미확인(쟁점)\"),\n",
    "    (\"실질금리 비중(외생 카드 ②)\", \"실질금리 하나가 바텀업 성장(연 4.88%)의 약 1/4\",\n",
    "     f\"{e.loc['실질금리', '바텀업_CAGR기여_%p']:.2f} / {tot.loc['62', 'CAGR(%)']:.2f} = {e.loc['실질금리', '바텀업_CAGR기여_%p'] / tot.loc['62', 'CAGR(%)']:.2f}\", \"부합\"),\n",
    "], columns=[\"항목\", \"공표 문구\", \"계산 결과\", \"판정\"])\n",
    "claims.to_csv(os.path.join(C.OUTPUT, \"claims_check.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "claims.style.hide(axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb6f39f",
   "metadata": {},
   "source": [
    "## 표 7. 정비 전후의 외생 기여(참고, 검증 대상 아님)\n",
    "\n",
    "02 9절의 결과이다. 2035년 S1 순효과(조 원)로, 이전 판은 공표 진단(표 1)과 같고 정비판은 2026-10 정비 외생으로 다시 계산한 값이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28f74bc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.904335Z",
     "iopub.status.busy": "2026-10-08T08:04:40.904335Z",
     "iopub.status.idle": "2026-10-08T08:04:40.910104Z",
     "shell.execute_reply": "2026-10-08T08:04:40.910104Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b2982\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b2982_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">이전 판</th>\n",
       "      <th id=\"T_b2982_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">정비판</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_b2982_level1_col0\" class=\"col_heading level1 col0\" >총량_효과_조원</th>\n",
       "      <th id=\"T_b2982_level1_col1\" class=\"col_heading level1 col1\" >바텀업_효과_조원</th>\n",
       "      <th id=\"T_b2982_level1_col2\" class=\"col_heading level1 col2\" >총량_효과_조원</th>\n",
       "      <th id=\"T_b2982_level1_col3\" class=\"col_heading level1 col3\" >바텀업_효과_조원</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >외생</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b2982_level0_row0\" class=\"row_heading level0 row0\" >유가</th>\n",
       "      <td id=\"T_b2982_row0_col0\" class=\"data row0 col0\" >+14.6</td>\n",
       "      <td id=\"T_b2982_row0_col1\" class=\"data row0 col1\" >+65.5</td>\n",
       "      <td id=\"T_b2982_row0_col2\" class=\"data row0 col2\" >+1.1</td>\n",
       "      <td id=\"T_b2982_row0_col3\" class=\"data row0 col3\" >+4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b2982_level0_row1\" class=\"row_heading level0 row1\" >세계수요</th>\n",
       "      <td id=\"T_b2982_row1_col0\" class=\"data row1 col0\" >+1.6</td>\n",
       "      <td id=\"T_b2982_row1_col1\" class=\"data row1 col1\" >-12.0</td>\n",
       "      <td id=\"T_b2982_row1_col2\" class=\"data row1 col2\" >+3.2</td>\n",
       "      <td id=\"T_b2982_row1_col3\" class=\"data row1 col3\" >-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b2982_level0_row2\" class=\"row_heading level0 row2\" >실질금리</th>\n",
       "      <td id=\"T_b2982_row2_col0\" class=\"data row2 col0\" >+162.5</td>\n",
       "      <td id=\"T_b2982_row2_col1\" class=\"data row2 col1\" >+354.2</td>\n",
       "      <td id=\"T_b2982_row2_col2\" class=\"data row2 col2\" >+0.4</td>\n",
       "      <td id=\"T_b2982_row2_col3\" class=\"data row2 col3\" >+1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b2982_level0_row3\" class=\"row_heading level0 row3\" >실질환율</th>\n",
       "      <td id=\"T_b2982_row3_col0\" class=\"data row3 col0\" >+4.8</td>\n",
       "      <td id=\"T_b2982_row3_col1\" class=\"data row3 col1\" >+9.9</td>\n",
       "      <td id=\"T_b2982_row3_col2\" class=\"data row3 col2\" >-4.5</td>\n",
       "      <td id=\"T_b2982_row3_col3\" class=\"data row3 col3\" >-13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b2982_level0_row4\" class=\"row_heading level0 row4\" >고용_취업자</th>\n",
       "      <td id=\"T_b2982_row4_col0\" class=\"data row4 col0\" >+62.3</td>\n",
       "      <td id=\"T_b2982_row4_col1\" class=\"data row4 col1\" >+31.8</td>\n",
       "      <td id=\"T_b2982_row4_col2\" class=\"data row4 col2\" >-0.9</td>\n",
       "      <td id=\"T_b2982_row4_col3\" class=\"data row4 col3\" >-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b2982_level0_row5\" class=\"row_heading level0 row5\" >우리경기</th>\n",
       "      <td id=\"T_b2982_row5_col0\" class=\"data row5 col0\" >-0.8</td>\n",
       "      <td id=\"T_b2982_row5_col1\" class=\"data row5 col1\" >+7.6</td>\n",
       "      <td id=\"T_b2982_row5_col2\" class=\"data row5 col2\" >-3.5</td>\n",
       "      <td id=\"T_b2982_row5_col3\" class=\"data row5 col3\" >+0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce55d0590>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t7 = pd.read_csv(os.path.join(C.OUTPUT, \"exog_effects_before_after.csv\"), header=[0, 1], index_col=0, encoding=\"utf-8-sig\")\n",
    "t7.style.format(\"{:+.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f0ff76",
   "metadata": {},
   "source": [
    "## 표 8. 정비판 선형 SHAP 요약 — 36·62·69분류(S1, 10년 로그성장 기여 %p)\n",
    "\n",
    "02 10절의 결과이다. 정비판은 정비 외생(실질금리 1차 차분 등)과 드리프트 재설정 계수로 분해하였고, 이전 판은 4절(표 4·그림 3의 근거)이다.\n",
    "평균 절대 기여는 산업별 기여의 절댓값 평균이며, 외생은 여섯 외생 기여의 절댓값 합의 산업 평균이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0683c297",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.911108Z",
     "iopub.status.busy": "2026-10-08T08:04:40.911108Z",
     "iopub.status.idle": "2026-10-08T08:04:40.927188Z",
     "shell.execute_reply": "2026-10-08T08:04:40.927188Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62분류 정비판 분해 = 5장 분해(전망 갱신 카드): 최대 |차이| 1.4e-14\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_b4ae8\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >판</th>\n",
       "      <th id=\"T_b4ae8_level0_col0\" class=\"col_heading level0 col0\" >정비판</th>\n",
       "      <th id=\"T_b4ae8_level0_col1\" class=\"col_heading level0 col1\" colspan=\"6\">이전 판</th>\n",
       "      <th id=\"T_b4ae8_level0_col7\" class=\"col_heading level0 col7\" colspan=\"6\">정비판</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_b4ae8_level1_col0\" class=\"col_heading level1 col0\" >δ(연 %p)</th>\n",
       "      <th id=\"T_b4ae8_level1_col1\" class=\"col_heading level1 col1\" >합계 산업 평균</th>\n",
       "      <th id=\"T_b4ae8_level1_col2\" class=\"col_heading level1 col2\" >과거추세</th>\n",
       "      <th id=\"T_b4ae8_level1_col3\" class=\"col_heading level1 col3\" >산업 동학</th>\n",
       "      <th id=\"T_b4ae8_level1_col4\" class=\"col_heading level1 col4\" >외생(6종 절댓값 합)</th>\n",
       "      <th id=\"T_b4ae8_level1_col5\" class=\"col_heading level1 col5\" >실질금리 최대 |기여|</th>\n",
       "      <th id=\"T_b4ae8_level1_col6\" class=\"col_heading level1 col6\" >유가 최대 |기여|</th>\n",
       "      <th id=\"T_b4ae8_level1_col7\" class=\"col_heading level1 col7\" >합계 산업 평균</th>\n",
       "      <th id=\"T_b4ae8_level1_col8\" class=\"col_heading level1 col8\" >과거추세</th>\n",
       "      <th id=\"T_b4ae8_level1_col9\" class=\"col_heading level1 col9\" >산업 동학</th>\n",
       "      <th id=\"T_b4ae8_level1_col10\" class=\"col_heading level1 col10\" >외생(6종 절댓값 합)</th>\n",
       "      <th id=\"T_b4ae8_level1_col11\" class=\"col_heading level1 col11\" >실질금리 최대 |기여|</th>\n",
       "      <th id=\"T_b4ae8_level1_col12\" class=\"col_heading level1 col12\" >유가 최대 |기여|</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >분류</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b4ae8_level0_row0\" class=\"row_heading level0 row0\" >36</th>\n",
       "      <td id=\"T_b4ae8_row0_col0\" class=\"data row0 col0\" >1.52</td>\n",
       "      <td id=\"T_b4ae8_row0_col1\" class=\"data row0 col1\" >29.08</td>\n",
       "      <td id=\"T_b4ae8_row0_col2\" class=\"data row0 col2\" >24.61</td>\n",
       "      <td id=\"T_b4ae8_row0_col3\" class=\"data row0 col3\" >8.27</td>\n",
       "      <td id=\"T_b4ae8_row0_col4\" class=\"data row0 col4\" >34.62</td>\n",
       "      <td id=\"T_b4ae8_row0_col5\" class=\"data row0 col5\" >56.84</td>\n",
       "      <td id=\"T_b4ae8_row0_col6\" class=\"data row0 col6\" >41.06</td>\n",
       "      <td id=\"T_b4ae8_row0_col7\" class=\"data row0 col7\" >4.14</td>\n",
       "      <td id=\"T_b4ae8_row0_col8\" class=\"data row0 col8\" >21.04</td>\n",
       "      <td id=\"T_b4ae8_row0_col9\" class=\"data row0 col9\" >5.00</td>\n",
       "      <td id=\"T_b4ae8_row0_col10\" class=\"data row0 col10\" >3.88</td>\n",
       "      <td id=\"T_b4ae8_row0_col11\" class=\"data row0 col11\" >0.25</td>\n",
       "      <td id=\"T_b4ae8_row0_col12\" class=\"data row0 col12\" >8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b4ae8_level0_row1\" class=\"row_heading level0 row1\" >62</th>\n",
       "      <td id=\"T_b4ae8_row1_col0\" class=\"data row1 col0\" >1.52</td>\n",
       "      <td id=\"T_b4ae8_row1_col1\" class=\"data row1 col1\" >34.79</td>\n",
       "      <td id=\"T_b4ae8_row1_col2\" class=\"data row1 col2\" >29.02</td>\n",
       "      <td id=\"T_b4ae8_row1_col3\" class=\"data row1 col3\" >18.61</td>\n",
       "      <td id=\"T_b4ae8_row1_col4\" class=\"data row1 col4\" >33.42</td>\n",
       "      <td id=\"T_b4ae8_row1_col5\" class=\"data row1 col5\" >53.81</td>\n",
       "      <td id=\"T_b4ae8_row1_col6\" class=\"data row1 col6\" >31.36</td>\n",
       "      <td id=\"T_b4ae8_row1_col7\" class=\"data row1 col7\" >10.37</td>\n",
       "      <td id=\"T_b4ae8_row1_col8\" class=\"data row1 col8\" >23.53</td>\n",
       "      <td id=\"T_b4ae8_row1_col9\" class=\"data row1 col9\" >14.21</td>\n",
       "      <td id=\"T_b4ae8_row1_col10\" class=\"data row1 col10\" >3.30</td>\n",
       "      <td id=\"T_b4ae8_row1_col11\" class=\"data row1 col11\" >0.16</td>\n",
       "      <td id=\"T_b4ae8_row1_col12\" class=\"data row1 col12\" >6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b4ae8_level0_row2\" class=\"row_heading level0 row2\" >69</th>\n",
       "      <td id=\"T_b4ae8_row2_col0\" class=\"data row2 col0\" >1.52</td>\n",
       "      <td id=\"T_b4ae8_row2_col1\" class=\"data row2 col1\" >37.15</td>\n",
       "      <td id=\"T_b4ae8_row2_col2\" class=\"data row2 col2\" >30.76</td>\n",
       "      <td id=\"T_b4ae8_row2_col3\" class=\"data row2 col3\" >22.48</td>\n",
       "      <td id=\"T_b4ae8_row2_col4\" class=\"data row2 col4\" >33.23</td>\n",
       "      <td id=\"T_b4ae8_row2_col5\" class=\"data row2 col5\" >54.02</td>\n",
       "      <td id=\"T_b4ae8_row2_col6\" class=\"data row2 col6\" >30.83</td>\n",
       "      <td id=\"T_b4ae8_row2_col7\" class=\"data row2 col7\" >12.57</td>\n",
       "      <td id=\"T_b4ae8_row2_col8\" class=\"data row2 col8\" >25.75</td>\n",
       "      <td id=\"T_b4ae8_row2_col9\" class=\"data row2 col9\" >15.74</td>\n",
       "      <td id=\"T_b4ae8_row2_col10\" class=\"data row2 col10\" >3.39</td>\n",
       "      <td id=\"T_b4ae8_row2_col11\" class=\"data row2 col11\" >0.16</td>\n",
       "      <td id=\"T_b4ae8_row2_col12\" class=\"data row2 col12\" >6.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce5614230>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ancj = rd(\"drift_anchor_jeongbi.csv\", index_col=0)\n",
    "scj = rd(\"shap_check_jeongbi.csv\", index_col=0, dtype={\"분류\": str})\n",
    "ssj = rd(\"shap_summary_jeongbi.csv\", index_col=[0, 1], dtype={\"분류\": str})\n",
    "orj = rd(\"oil_rank_jeongbi.csv\", dtype={\"분류\": str})\n",
    "ofj = rd(\"oil_factor_jeongbi.csv\", index_col=0, dtype={\"분류\": str})\n",
    "svj = rd(\"oil_shap_vs_cf_62_jeongbi.csv\", index_col=0)\n",
    "ocj = rd(\"oil_effect_compare_jeongbi.csv\", index_col=0)\n",
    "SHJ = {r: rd(f\"shap_{r}_jeongbi.csv\", index_col=0) for r in (\"36\", \"62\", \"69\")}\n",
    "t8 = ssj.unstack(\"판\").swaplevel(axis=1)[[\"이전 판\", \"정비판\"]]\n",
    "t8.insert(0, (\"정비판\", \"δ(연 %p)\"), ancj.loc[[\"36\", \"62\", \"69\"], \"δ(연 %p)\"].values)\n",
    "t8.to_csv(os.path.join(C.OUTPUT, \"table8_shap_jeongbi.csv\"), encoding=\"utf-8-sig\")\n",
    "print(f\"62분류 정비판 분해 = 5장 분해(전망 갱신 카드): 최대 |차이| {scj.loc['62', '(라) 5장 기록과 최대 |차이|']:.1e}\")\n",
    "t8.style.format(\"{:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d726974c",
   "metadata": {},
   "source": [
    "## 표 9. 정비판 유가에 가장 민감한 산업 — 36·62·69분류(10년 로그성장 기여, %p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b046d47a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.928193Z",
     "iopub.status.busy": "2026-10-08T08:04:40.928193Z",
     "iopub.status.idle": "2026-10-08T08:04:40.936784Z",
     "shell.execute_reply": "2026-10-08T08:04:40.936280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S1 유가 누적 변화(100·ln) 98.05 → 22.43, 석유제품(62) 유가 계수 합 0.337 → 0.339\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_7d94a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7d94a_level0_col0\" class=\"col_heading level0 col0\" >유가 영향 1위</th>\n",
       "      <th id=\"T_7d94a_level0_col1\" class=\"col_heading level0 col1\" >기여(%p)</th>\n",
       "      <th id=\"T_7d94a_level0_col2\" class=\"col_heading level0 col2\" >이전 판 기여(%p)</th>\n",
       "      <th id=\"T_7d94a_level0_col3\" class=\"col_heading level0 col3\" >수혜(+) 상위 3</th>\n",
       "      <th id=\"T_7d94a_level0_col4\" class=\"col_heading level0 col4\" >피해(−) 상위 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >분류</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7d94a_level0_row0\" class=\"row_heading level0 row0\" >36</th>\n",
       "      <td id=\"T_7d94a_row0_col0\" class=\"data row0 col0\" >코크스 및 석유정제품 제조업</td>\n",
       "      <td id=\"T_7d94a_row0_col1\" class=\"data row0 col1\" >+8.47</td>\n",
       "      <td id=\"T_7d94a_row0_col2\" class=\"data row0 col2\" >+41.06</td>\n",
       "      <td id=\"T_7d94a_row0_col3\" class=\"data row0 col3\" >기계 및 장비 제조업(+2.69) · 예술, 스포츠 및 여가관련 서비스업(+2.31) · 운송장비 제조업(+2.25)</td>\n",
       "      <td id=\"T_7d94a_row0_col4\" class=\"data row0 col4\" >토목건설업(-1.31) · 통신업(-0.92) · 주거용 건물 건설업(-0.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d94a_level0_row1\" class=\"row_heading level0 row1\" >62</th>\n",
       "      <td id=\"T_7d94a_row1_col0\" class=\"data row1 col0\" >14.석탄ㆍ석유제품</td>\n",
       "      <td id=\"T_7d94a_row1_col1\" class=\"data row1 col1\" >+6.21</td>\n",
       "      <td id=\"T_7d94a_row1_col2\" class=\"data row1 col2\" >+31.36</td>\n",
       "      <td id=\"T_7d94a_row1_col3\" class=\"data row1 col3\" >25.기타기계ㆍ장비(+2.28) · 27.기타 운송장비(+2.17) · 26.자동차(+1.95)</td>\n",
       "      <td id=\"T_7d94a_row1_col4\" class=\"data row1 col4\" >43.통신업(-0.90) · 52.사업관련전문서비스업(-0.83) · 53.과학기술관련전문서비스업(-0.68)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d94a_level0_row2\" class=\"row_heading level0 row2\" >69</th>\n",
       "      <td id=\"T_7d94a_row2_col0\" class=\"data row2 col0\" >14.석탄ㆍ석유제품</td>\n",
       "      <td id=\"T_7d94a_row2_col1\" class=\"data row2 col1\" >+6.08</td>\n",
       "      <td id=\"T_7d94a_row2_col2\" class=\"data row2 col2\" >+30.83</td>\n",
       "      <td id=\"T_7d94a_row2_col3\" class=\"data row2 col3\" >25.기타기계ㆍ장비(+2.43) · 이차전지(+2.26) · 27.기타 운송장비(잔여)(+2.14)</td>\n",
       "      <td id=\"T_7d94a_row2_col4\" class=\"data row2 col4\" >43.통신업(-0.87) · 52.사업관련전문서비스업(-0.87) · 토목건설(-0.76)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce5618b90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "for r in (\"36\", \"62\", \"69\"):\n",
    "    g = orj[orj[\"분류\"] == r]\n",
    "    top = g[g[\"|기여| 최대\"]].iloc[0]\n",
    "    plus = g[(g[\"구분\"] == \"상위(+)\") & ~g[\"|기여| 최대\"]].head(3)\n",
    "    minus = g[g[\"구분\"] == \"하위(−)\"].head(3)\n",
    "    rows.append({\"분류\": r, \"유가 영향 1위\": top[\"산업\"], \"기여(%p)\": top[\"유가 기여(%p)\"], \"이전 판 기여(%p)\": ofj.loc[r, \"이전 판 유가 기여(%p)\"],\n",
    "                 \"수혜(+) 상위 3\": \" · \".join(f\"{a}({v:+.2f})\" for a, v in zip(plus[\"산업\"], plus[\"유가 기여(%p)\"])),\n",
    "                 \"피해(−) 상위 3\": \" · \".join(f\"{a}({v:+.2f})\" for a, v in zip(minus[\"산업\"], minus[\"유가 기여(%p)\"]))})\n",
    "t9 = pd.DataFrame(rows).set_index(\"분류\")\n",
    "t9.to_csv(os.path.join(C.OUTPUT, \"table9_oil_sensitive_jeongbi.csv\"), encoding=\"utf-8-sig\")\n",
    "r0 = ofj.loc[\"62\"]\n",
    "print(f\"S1 유가 누적 변화(100·ln) {r0['이전 판 유가 누적(100·ln)']:.2f} → {r0['정비판 유가 누적(100·ln)']:.2f}, \"\n",
    "      f\"석유제품(62) 유가 계수 합 {r0['이전 판 유가 계수 합']:.3f} → {r0['정비판 유가 계수 합']:.3f}\")\n",
    "t9.style.format({\"기여(%p)\": \"{:+.2f}\", \"이전 판 기여(%p)\": \"{:+.2f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4725bf1",
   "metadata": {},
   "source": [
    "## 그림 4. 62개 산업의 유가 기여 — 정비판(S1, 10년 로그성장 %p, 상·하위 10개)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e000643d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:40.937285Z",
     "iopub.status.busy": "2026-10-08T08:04:40.937285Z",
     "iopub.status.idle": "2026-10-08T08:04:41.144341Z",
     "shell.execute_reply": "2026-10-08T08:04:41.143838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x620 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = SHJ[\"62\"][\"유가\"].sort_values()\n",
    "s = pd.concat([s.head(10), s.tail(10)])\n",
    "fig, ax = plt.subplots(figsize=(8, 6.2))\n",
    "ax.barh(range(len(s)), s.values, color=[S1C if v > 0 else S2C for v in s.values], height=0.7)\n",
    "ax.set_yticks(range(len(s)), [i.split(\".\", 1)[1] for i in s.index], fontsize=8, color=INK)\n",
    "for i, v in enumerate(s.values):\n",
    "    ax.text(v + (0.08 if v > 0 else -0.08), i, f\"{v:+.2f}\", va=\"center\", ha=\"left\" if v > 0 else \"right\", fontsize=7.5, color=MUTED)\n",
    "ax.axvline(0, color=\"#9aa3ad\", lw=0.8)\n",
    "ax.set_xlim(s.min() - 0.8, s.max() + 1.0)\n",
    "ax.set_xlabel(\"유가 기여(10년 로그성장, %p) — 파랑: 상승 기여, 주황: 하락 기여\", color=MUTED)\n",
    "ax.grid(axis=\"x\", alpha=.25); ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
    "ax.set_title(\"62분류 산업별 유가 기여(선형 SHAP, S1, 정비판)\", loc=\"left\", color=INK)\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig4_oil_shap_62_jeongbi.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72931c36",
   "metadata": {},
   "source": [
    "## 표 10. 정비판 유가의 실질부가가치 순효과(2035년, S1) — 총량 모형과 62 바텀업\n",
    "\n",
    "반사실(유가 고정 재전망)으로 계산한다. 정비판 62 바텀업은 드리프트 재설정 모형이며, 재설정 전 모형(02 9절)과 이전 판(표 3)을 함께 둔다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48f0259c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:41.144842Z",
     "iopub.status.busy": "2026-10-08T08:04:41.144842Z",
     "iopub.status.idle": "2026-10-08T08:04:41.152541Z",
     "shell.execute_reply": "2026-10-08T08:04:41.152041Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62 유가 SHAP(직접)·반사실(총효과) 산업 간 상관: 정비판 0.898\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_0d73a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0d73a_level0_col0\" class=\"col_heading level0 col0\" >2025(조원)</th>\n",
       "      <th id=\"T_0d73a_level0_col1\" class=\"col_heading level0 col1\" >2035(조원)</th>\n",
       "      <th id=\"T_0d73a_level0_col2\" class=\"col_heading level0 col2\" >유가 고정 2035(조원)</th>\n",
       "      <th id=\"T_0d73a_level0_col3\" class=\"col_heading level0 col3\" >유가 효과(조원)</th>\n",
       "      <th id=\"T_0d73a_level0_col4\" class=\"col_heading level0 col4\" >유가 효과(%)</th>\n",
       "      <th id=\"T_0d73a_level0_col5\" class=\"col_heading level0 col5\" >CAGR(%)</th>\n",
       "      <th id=\"T_0d73a_level0_col6\" class=\"col_heading level0 col6\" >유가 고정 CAGR(%)</th>\n",
       "      <th id=\"T_0d73a_level0_col7\" class=\"col_heading level0 col7\" >CAGR 기여(%p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >모형</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0d73a_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">정비판</th>\n",
       "      <th id=\"T_0d73a_level1_row0\" class=\"row_heading level1 row0\" >총량 예측모형</th>\n",
       "      <td id=\"T_0d73a_row0_col0\" class=\"data row0 col0\" >2,326.3</td>\n",
       "      <td id=\"T_0d73a_row0_col1\" class=\"data row0 col1\" >2,671.4</td>\n",
       "      <td id=\"T_0d73a_row0_col2\" class=\"data row0 col2\" >2,670.3</td>\n",
       "      <td id=\"T_0d73a_row0_col3\" class=\"data row0 col3\" >+1.07</td>\n",
       "      <td id=\"T_0d73a_row0_col4\" class=\"data row0 col4\" >+0.040</td>\n",
       "      <td id=\"T_0d73a_row0_col5\" class=\"data row0 col5\" >1.39</td>\n",
       "      <td id=\"T_0d73a_row0_col6\" class=\"data row0 col6\" >1.39</td>\n",
       "      <td id=\"T_0d73a_row0_col7\" class=\"data row0 col7\" >+0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d73a_level1_row1\" class=\"row_heading level1 row1\" >62 바텀업(드리프트 재설정)</th>\n",
       "      <td id=\"T_0d73a_row1_col0\" class=\"data row1 col0\" >2,178.5</td>\n",
       "      <td id=\"T_0d73a_row1_col1\" class=\"data row1 col1\" >2,793.7</td>\n",
       "      <td id=\"T_0d73a_row1_col2\" class=\"data row1 col2\" >2,789.8</td>\n",
       "      <td id=\"T_0d73a_row1_col3\" class=\"data row1 col3\" >+3.97</td>\n",
       "      <td id=\"T_0d73a_row1_col4\" class=\"data row1 col4\" >+0.142</td>\n",
       "      <td id=\"T_0d73a_row1_col5\" class=\"data row1 col5\" >2.52</td>\n",
       "      <td id=\"T_0d73a_row1_col6\" class=\"data row1 col6\" >2.50</td>\n",
       "      <td id=\"T_0d73a_row1_col7\" class=\"data row1 col7\" >+0.0146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d73a_level1_row2\" class=\"row_heading level1 row2\" >62 바텀업(재설정 전, 9절)</th>\n",
       "      <td id=\"T_0d73a_row2_col0\" class=\"data row2 col0\" >2,178.5</td>\n",
       "      <td id=\"T_0d73a_row2_col1\" class=\"data row2 col1\" >3,223.4</td>\n",
       "      <td id=\"T_0d73a_row2_col2\" class=\"data row2 col2\" >3,218.7</td>\n",
       "      <td id=\"T_0d73a_row2_col3\" class=\"data row2 col3\" >+4.67</td>\n",
       "      <td id=\"T_0d73a_row2_col4\" class=\"data row2 col4\" >+0.145</td>\n",
       "      <td id=\"T_0d73a_row2_col5\" class=\"data row2 col5\" >4.00</td>\n",
       "      <td id=\"T_0d73a_row2_col6\" class=\"data row2 col6\" >3.98</td>\n",
       "      <td id=\"T_0d73a_row2_col7\" class=\"data row2 col7\" >+0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d73a_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"2\">이전 판</th>\n",
       "      <th id=\"T_0d73a_level1_row3\" class=\"row_heading level1 row3\" >총량 예측모형</th>\n",
       "      <td id=\"T_0d73a_row3_col0\" class=\"data row3 col0\" >2,326.3</td>\n",
       "      <td id=\"T_0d73a_row3_col1\" class=\"data row3 col1\" >2,970.4</td>\n",
       "      <td id=\"T_0d73a_row3_col2\" class=\"data row3 col2\" >2,955.8</td>\n",
       "      <td id=\"T_0d73a_row3_col3\" class=\"data row3 col3\" >+14.60</td>\n",
       "      <td id=\"T_0d73a_row3_col4\" class=\"data row3 col4\" >+0.491</td>\n",
       "      <td id=\"T_0d73a_row3_col5\" class=\"data row3 col5\" >2.47</td>\n",
       "      <td id=\"T_0d73a_row3_col6\" class=\"data row3 col6\" >2.42</td>\n",
       "      <td id=\"T_0d73a_row3_col7\" class=\"data row3 col7\" >+0.0505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0d73a_level1_row4\" class=\"row_heading level1 row4\" >62 바텀업</th>\n",
       "      <td id=\"T_0d73a_row4_col0\" class=\"data row4 col0\" >2,178.5</td>\n",
       "      <td id=\"T_0d73a_row4_col1\" class=\"data row4 col1\" >3,507.0</td>\n",
       "      <td id=\"T_0d73a_row4_col2\" class=\"data row4 col2\" >3,441.5</td>\n",
       "      <td id=\"T_0d73a_row4_col3\" class=\"data row4 col3\" >+65.47</td>\n",
       "      <td id=\"T_0d73a_row4_col4\" class=\"data row4 col4\" >+1.867</td>\n",
       "      <td id=\"T_0d73a_row4_col5\" class=\"data row4 col5\" >4.88</td>\n",
       "      <td id=\"T_0d73a_row4_col6\" class=\"data row4 col6\" >4.68</td>\n",
       "      <td id=\"T_0d73a_row4_col7\" class=\"data row4 col7\" >+0.1975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce32c4a70>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t10 = pd.concat({\"정비판\": ocj, \"이전 판\": oil_cmp})\n",
    "t10.to_csv(os.path.join(C.OUTPUT, \"table10_oil_effect_jeongbi.csv\"), encoding=\"utf-8-sig\")\n",
    "print(f\"62 유가 SHAP(직접)·반사실(총효과) 산업 간 상관: 정비판 {svj.iloc[:, 0].corr(svj.iloc[:, 1]):.3f}\")\n",
    "t10.style.format({c: \"{:,.1f}\" for c in [\"2025(조원)\", \"2035(조원)\", \"유가 고정 2035(조원)\"]} |\n",
    "                 {\"유가 효과(조원)\": \"{:+,.2f}\", \"유가 효과(%)\": \"{:+.3f}\", \"CAGR(%)\": \"{:.2f}\", \"유가 고정 CAGR(%)\": \"{:.2f}\", \"CAGR 기여(%p)\": \"{:+.4f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf0983e",
   "metadata": {},
   "source": [
    "## 표 11. 정비판 시나리오 폭(max−min, 연평균)과 유가 경로 — 2026년과 2035년"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5f7adda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:41.153545Z",
     "iopub.status.busy": "2026-10-08T08:04:41.153545Z",
     "iopub.status.idle": "2026-10-08T08:04:41.162896Z",
     "shell.execute_reply": "2026-10-08T08:04:41.162896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e7a4a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e7a4a_level0_col0\" class=\"col_heading level0 col0\" >폭_2026</th>\n",
       "      <th id=\"T_e7a4a_level0_col1\" class=\"col_heading level0 col1\" >폭_2035</th>\n",
       "      <th id=\"T_e7a4a_level0_col2\" class=\"col_heading level0 col2\" >판정</th>\n",
       "      <th id=\"T_e7a4a_level0_col3\" class=\"col_heading level0 col3\" >이전 판 폭_2026</th>\n",
       "      <th id=\"T_e7a4a_level0_col4\" class=\"col_heading level0 col4\" >이전 판 폭_2035</th>\n",
       "      <th id=\"T_e7a4a_level0_col5\" class=\"col_heading level0 col5\" >이전 판 판정</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a4a_level0_row0\" class=\"row_heading level0 row0\" >유가</th>\n",
       "      <td id=\"T_e7a4a_row0_col0\" class=\"data row0 col0\" >25.16</td>\n",
       "      <td id=\"T_e7a4a_row0_col1\" class=\"data row0 col1\" >51.750</td>\n",
       "      <td id=\"T_e7a4a_row0_col2\" class=\"data row0 col2\" >영구</td>\n",
       "      <td id=\"T_e7a4a_row0_col3\" class=\"data row0 col3\" >25.16</td>\n",
       "      <td id=\"T_e7a4a_row0_col4\" class=\"data row0 col4\" >51.750</td>\n",
       "      <td id=\"T_e7a4a_row0_col5\" class=\"data row0 col5\" >영구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a4a_level0_row1\" class=\"row_heading level0 row1\" >세계수요</th>\n",
       "      <td id=\"T_e7a4a_row1_col0\" class=\"data row1 col0\" >1.79</td>\n",
       "      <td id=\"T_e7a4a_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
       "      <td id=\"T_e7a4a_row1_col2\" class=\"data row1 col2\" >일시</td>\n",
       "      <td id=\"T_e7a4a_row1_col3\" class=\"data row1 col3\" >1.79</td>\n",
       "      <td id=\"T_e7a4a_row1_col4\" class=\"data row1 col4\" >0.000</td>\n",
       "      <td id=\"T_e7a4a_row1_col5\" class=\"data row1 col5\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a4a_level0_row2\" class=\"row_heading level0 row2\" >실질금리</th>\n",
       "      <td id=\"T_e7a4a_row2_col0\" class=\"data row2 col0\" >5.63</td>\n",
       "      <td id=\"T_e7a4a_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
       "      <td id=\"T_e7a4a_row2_col2\" class=\"data row2 col2\" >일시</td>\n",
       "      <td id=\"T_e7a4a_row2_col3\" class=\"data row2 col3\" >4.80</td>\n",
       "      <td id=\"T_e7a4a_row2_col4\" class=\"data row2 col4\" >0.000</td>\n",
       "      <td id=\"T_e7a4a_row2_col5\" class=\"data row2 col5\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a4a_level0_row3\" class=\"row_heading level0 row3\" >실질환율</th>\n",
       "      <td id=\"T_e7a4a_row3_col0\" class=\"data row3 col0\" >13.65</td>\n",
       "      <td id=\"T_e7a4a_row3_col1\" class=\"data row3 col1\" >0.004</td>\n",
       "      <td id=\"T_e7a4a_row3_col2\" class=\"data row3 col2\" >일시</td>\n",
       "      <td id=\"T_e7a4a_row3_col3\" class=\"data row3 col3\" >11.87</td>\n",
       "      <td id=\"T_e7a4a_row3_col4\" class=\"data row3 col4\" >0.004</td>\n",
       "      <td id=\"T_e7a4a_row3_col5\" class=\"data row3 col5\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a4a_level0_row4\" class=\"row_heading level0 row4\" >고용(취업자)</th>\n",
       "      <td id=\"T_e7a4a_row4_col0\" class=\"data row4 col0\" >681.87</td>\n",
       "      <td id=\"T_e7a4a_row4_col1\" class=\"data row4 col1\" >943.421</td>\n",
       "      <td id=\"T_e7a4a_row4_col2\" class=\"data row4 col2\" >영구</td>\n",
       "      <td id=\"T_e7a4a_row4_col3\" class=\"data row4 col3\" >531.61</td>\n",
       "      <td id=\"T_e7a4a_row4_col4\" class=\"data row4 col4\" >8.630</td>\n",
       "      <td id=\"T_e7a4a_row4_col5\" class=\"data row4 col5\" >일시</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a4a_level0_row5\" class=\"row_heading level0 row5\" >우리경기</th>\n",
       "      <td id=\"T_e7a4a_row5_col0\" class=\"data row5 col0\" >3.44</td>\n",
       "      <td id=\"T_e7a4a_row5_col1\" class=\"data row5 col1\" >0.000</td>\n",
       "      <td id=\"T_e7a4a_row5_col2\" class=\"data row5 col2\" >일시</td>\n",
       "      <td id=\"T_e7a4a_row5_col3\" class=\"data row5 col3\" >2.94</td>\n",
       "      <td id=\"T_e7a4a_row5_col4\" class=\"data row5 col4\" >0.000</td>\n",
       "      <td id=\"T_e7a4a_row5_col5\" class=\"data row5 col5\" >일시</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce6b0c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_f1ccb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f1ccb_level0_col0\" class=\"col_heading level0 col0\" colspan=\"5\">정비판</th>\n",
       "      <th id=\"T_f1ccb_level0_col5\" class=\"col_heading level0 col5\" colspan=\"5\">이전 판</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_f1ccb_level1_col0\" class=\"col_heading level1 col0\" >S1</th>\n",
       "      <th id=\"T_f1ccb_level1_col1\" class=\"col_heading level1 col1\" >S2</th>\n",
       "      <th id=\"T_f1ccb_level1_col2\" class=\"col_heading level1 col2\" >S3</th>\n",
       "      <th id=\"T_f1ccb_level1_col3\" class=\"col_heading level1 col3\" >S4</th>\n",
       "      <th id=\"T_f1ccb_level1_col4\" class=\"col_heading level1 col4\" >S5</th>\n",
       "      <th id=\"T_f1ccb_level1_col5\" class=\"col_heading level1 col5\" >S1</th>\n",
       "      <th id=\"T_f1ccb_level1_col6\" class=\"col_heading level1 col6\" >S2</th>\n",
       "      <th id=\"T_f1ccb_level1_col7\" class=\"col_heading level1 col7\" >S3</th>\n",
       "      <th id=\"T_f1ccb_level1_col8\" class=\"col_heading level1 col8\" >S4</th>\n",
       "      <th id=\"T_f1ccb_level1_col9\" class=\"col_heading level1 col9\" >S5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >연도</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f1ccb_level0_row0\" class=\"row_heading level0 row0\" >2025</th>\n",
       "      <td id=\"T_f1ccb_row0_col0\" class=\"data row0 col0\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col1\" class=\"data row0 col1\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col2\" class=\"data row0 col2\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col3\" class=\"data row0 col3\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col4\" class=\"data row0 col4\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col5\" class=\"data row0 col5\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col6\" class=\"data row0 col6\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col7\" class=\"data row0 col7\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col8\" class=\"data row0 col8\" >69.2</td>\n",
       "      <td id=\"T_f1ccb_row0_col9\" class=\"data row0 col9\" >69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1ccb_level0_row1\" class=\"row_heading level0 row1\" >2030</th>\n",
       "      <td id=\"T_f1ccb_row1_col0\" class=\"data row1 col0\" >70.4</td>\n",
       "      <td id=\"T_f1ccb_row1_col1\" class=\"data row1 col1\" >58.8</td>\n",
       "      <td id=\"T_f1ccb_row1_col2\" class=\"data row1 col2\" >65.8</td>\n",
       "      <td id=\"T_f1ccb_row1_col3\" class=\"data row1 col3\" >70.4</td>\n",
       "      <td id=\"T_f1ccb_row1_col4\" class=\"data row1 col4\" >98.6</td>\n",
       "      <td id=\"T_f1ccb_row1_col5\" class=\"data row1 col5\" >99.7</td>\n",
       "      <td id=\"T_f1ccb_row1_col6\" class=\"data row1 col6\" >83.3</td>\n",
       "      <td id=\"T_f1ccb_row1_col7\" class=\"data row1 col7\" >93.2</td>\n",
       "      <td id=\"T_f1ccb_row1_col8\" class=\"data row1 col8\" >99.7</td>\n",
       "      <td id=\"T_f1ccb_row1_col9\" class=\"data row1 col9\" >139.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1ccb_level0_row2\" class=\"row_heading level0 row2\" >2035</th>\n",
       "      <td id=\"T_f1ccb_row2_col0\" class=\"data row2 col0\" >78.9</td>\n",
       "      <td id=\"T_f1ccb_row2_col1\" class=\"data row2 col1\" >65.9</td>\n",
       "      <td id=\"T_f1ccb_row2_col2\" class=\"data row2 col2\" >73.8</td>\n",
       "      <td id=\"T_f1ccb_row2_col3\" class=\"data row2 col3\" >78.9</td>\n",
       "      <td id=\"T_f1ccb_row2_col4\" class=\"data row2 col4\" >110.6</td>\n",
       "      <td id=\"T_f1ccb_row2_col5\" class=\"data row2 col5\" >163.4</td>\n",
       "      <td id=\"T_f1ccb_row2_col6\" class=\"data row2 col6\" >136.5</td>\n",
       "      <td id=\"T_f1ccb_row2_col7\" class=\"data row2 col7\" >152.7</td>\n",
       "      <td id=\"T_f1ccb_row2_col8\" class=\"data row2 col8\" >163.4</td>\n",
       "      <td id=\"T_f1ccb_row2_col9\" class=\"data row2 col9\" >228.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23ce6b0c080>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_j = rd(\"exog_persistence_jeongbi.csv\", index_col=0)\n",
    "dol_j = rd(\"oil_dollar_paths_jeongbi.csv\", index_col=0)\n",
    "pth_j = rd(\"exog_paths_summary_jeongbi.csv\", index_col=0)\n",
    "t11 = per_j.join(per[[\"폭_2026\", \"폭_2035\", \"판정\"]].add_prefix(\"이전 판 \"))\n",
    "t11.index = [NAME[k] for k in t11.index]\n",
    "t11.to_csv(os.path.join(C.OUTPUT, \"table11_persistence_jeongbi.csv\"), encoding=\"utf-8-sig\")\n",
    "display(t11.style.format({\"폭_2026\": \"{:,.2f}\", \"폭_2035\": \"{:,.3f}\", \"이전 판 폭_2026\": \"{:,.2f}\", \"이전 판 폭_2035\": \"{:,.3f}\"}))\n",
    "pd.concat({\"정비판\": dol_j.loc[[2025, 2030, 2035]], \"이전 판\": dollars.loc[[2025, 2030, 2035]]}, axis=1).style.format(\"{:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e696d1b",
   "metadata": {},
   "source": [
    "## 검증 — 공표 수치와 대조\n",
    "\n",
    "외생 카드의 표·본문 수치, 유가 카드의 표·본문 수치, 공표 SHAP 산출물(`shap_36/62/69.json`)을 계산 결과와 대조한다(이전 판).\n",
    "이어서 정비판(02 10절)의 분해 점검, 5장 기록(62분류 분해·드리프트 재설정 δ·바텀업 연평균)과의 대조, 정비판 진단 기록 수치(`config.JEONGBI`)를 대조한다.\n",
    "허용오차는 공표·기록 자릿수의 반올림 폭이다(정수 조원 ±0.5, 소수 둘째 자리 ±0.005 등). 범주 항목은 일치 여부를 본다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a3d0d3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T08:04:41.164400Z",
     "iopub.status.busy": "2026-10-08T08:04:41.163903Z",
     "iopub.status.idle": "2026-10-08T08:04:41.187946Z",
     "shell.execute_reply": "2026-10-08T08:04:41.187441Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>항목</th>\n",
       "      <th>계산</th>\n",
       "      <th>공표</th>\n",
       "      <th>허용오차</th>\n",
       "      <th>통과</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[외생] 총량 순효과 유가(조원)</td>\n",
       "      <td>14.5957</td>\n",
       "      <td>15</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[외생] 총량 순효과 세계수요(조원)</td>\n",
       "      <td>1.5975</td>\n",
       "      <td>2</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[외생] 총량 순효과 실질금리(조원)</td>\n",
       "      <td>162.4946</td>\n",
       "      <td>162</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[외생] 총량 순효과 실질환율(조원)</td>\n",
       "      <td>4.7973</td>\n",
       "      <td>5</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[외생] 총량 순효과 고용(취업자)(조원)</td>\n",
       "      <td>62.3091</td>\n",
       "      <td>62</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[외생] 총량 순효과 우리경기(조원)</td>\n",
       "      <td>-0.8160</td>\n",
       "      <td>-1</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[외생] 총량 CAGR 기여 유가(%p)</td>\n",
       "      <td>0.0505</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[외생] 총량 CAGR 기여 세계수요(%p)</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[외생] 총량 CAGR 기여 실질금리(%p)</td>\n",
       "      <td>0.5749</td>\n",
       "      <td>0.5700</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[외생] 총량 CAGR 기여 실질환율(%p)</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[외생] 총량 CAGR 기여 고용(취업자)(%p)</td>\n",
       "      <td>0.2170</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[외생] 총량 CAGR 기여 우리경기(%p)</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[외생] 바텀업 순효과 유가(조원)</td>\n",
       "      <td>65.4734</td>\n",
       "      <td>65</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[외생] 바텀업 순효과 세계수요(조원)</td>\n",
       "      <td>-12.0228</td>\n",
       "      <td>-12</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[외생] 바텀업 순효과 실질금리(조원)</td>\n",
       "      <td>354.2112</td>\n",
       "      <td>354</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[외생] 바텀업 순효과 실질환율(조원)</td>\n",
       "      <td>9.9489</td>\n",
       "      <td>10</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[외생] 바텀업 순효과 고용(취업자)(조원)</td>\n",
       "      <td>31.7926</td>\n",
       "      <td>32</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[외생] 바텀업 순효과 우리경기(조원)</td>\n",
       "      <td>7.6240</td>\n",
       "      <td>8</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[외생] 바텀업 CAGR 기여 유가(%p)</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[외생] 바텀업 CAGR 기여 세계수요(%p)</td>\n",
       "      <td>-0.0359</td>\n",
       "      <td>-0.0400</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[외생] 바텀업 CAGR 기여 실질금리(%p)</td>\n",
       "      <td>1.1107</td>\n",
       "      <td>1.1100</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[외생] 바텀업 CAGR 기여 실질환율(%p)</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[외생] 바텀업 CAGR 기여 고용(취업자)(%p)</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[외생] 바텀업 CAGR 기여 우리경기(%p)</td>\n",
       "      <td>0.0228</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[외생] 지배 변수(총량·바텀업 모두 1위)</td>\n",
       "      <td>실질금리</td>\n",
       "      <td>실질금리</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[외생] 총량 CAGR 기여 순위</td>\n",
       "      <td>실질금리 &gt; 고용_취업자 &gt; 유가 &gt; 실질환율 &gt; 세계수요 &gt; 우리경기</td>\n",
       "      <td>실질금리 &gt; 고용_취업자 &gt; 유가 &gt; 실질환율 &gt; 세계수요 &gt; 우리경기</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[외생] 실질금리 2025 연평균(%)</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>0.9900</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[외생] 실질금리 2035 연평균(S1, %)</td>\n",
       "      <td>2.7760</td>\n",
       "      <td>2.7800</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[외생] 바텀업 CAGR 2025–2035(%)</td>\n",
       "      <td>4.8765</td>\n",
       "      <td>4.8800</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[외생] 시나리오 폭 2026 유가</td>\n",
       "      <td>25.1560</td>\n",
       "      <td>25.2000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>[외생] 시나리오 폭 2026 고용(취업자)</td>\n",
       "      <td>531.6052</td>\n",
       "      <td>531.6000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>[외생] 시나리오 폭 2026 실질환율</td>\n",
       "      <td>11.8690</td>\n",
       "      <td>11.9000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>[외생] 시나리오 폭 2026 실질금리</td>\n",
       "      <td>4.8030</td>\n",
       "      <td>4.8000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>[외생] 시나리오 폭 2026 우리경기</td>\n",
       "      <td>2.9428</td>\n",
       "      <td>2.9000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>[외생] 시나리오 폭 2026 세계수요</td>\n",
       "      <td>1.7900</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>[외생] 시나리오 폭 2035 유가</td>\n",
       "      <td>51.7500</td>\n",
       "      <td>51.8000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>[외생] 시나리오 폭 2035 고용(취업자)</td>\n",
       "      <td>8.6302</td>\n",
       "      <td>8.6000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>[외생] 시나리오 폭 2035 실질금리</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>[외생] 시나리오 폭 2035 우리경기</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>[외생] 시나리오 폭 2035 세계수요</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>[외생] 시나리오 폭 2035 실질환율</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>±0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>[외생] 영구 수준차 외생</td>\n",
       "      <td>유가</td>\n",
       "      <td>유가</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>[유가] 총량 2035(조원)</td>\n",
       "      <td>2,970.3786</td>\n",
       "      <td>2970</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>[유가] 총량 유가 고정 2035(조원)</td>\n",
       "      <td>2,955.7830</td>\n",
       "      <td>2956</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>[유가] 총량 유가 효과(%)</td>\n",
       "      <td>0.4914</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>[유가] 총량 CAGR 기여(%p)</td>\n",
       "      <td>0.0505</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>[유가] 바텀업 2035(조원)</td>\n",
       "      <td>3,506.9957</td>\n",
       "      <td>3507</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>[유가] 바텀업 유가 고정 2035(조원)</td>\n",
       "      <td>3,441.5224</td>\n",
       "      <td>3442</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>[유가] 바텀업 유가 효과(%)</td>\n",
       "      <td>1.8669</td>\n",
       "      <td>1.9000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>[유가] 바텀업 CAGR 기여(%p)</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>[유가] 36분류 유가 영향 1위 산업</td>\n",
       "      <td>코크스 및 석유정제품 제조업</td>\n",
       "      <td>코크스·석유정제품</td>\n",
       "      <td>포함</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>[유가] 36분류 1위 기여(%p)</td>\n",
       "      <td>41.0629</td>\n",
       "      <td>41.1000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>[유가] 62분류 유가 영향 1위 산업</td>\n",
       "      <td>14.석탄ㆍ석유제품</td>\n",
       "      <td>석탄·석유제품</td>\n",
       "      <td>포함</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>[유가] 62분류 1위 기여(%p)</td>\n",
       "      <td>31.3593</td>\n",
       "      <td>31.4000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>[유가] 69분류 유가 영향 1위 산업</td>\n",
       "      <td>14.석탄ㆍ석유제품</td>\n",
       "      <td>석탄·석유제품</td>\n",
       "      <td>포함</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>[유가] 69분류 1위 기여(%p)</td>\n",
       "      <td>30.8334</td>\n",
       "      <td>30.8000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>[유가] 2025 연평균($/배럴)</td>\n",
       "      <td>69.2274</td>\n",
       "      <td>69</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>[유가] S1 2035 연평균($/배럴)</td>\n",
       "      <td>163.3638</td>\n",
       "      <td>163</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>[유가] 2035 시나리오 최저($/배럴)</td>\n",
       "      <td>136.4529</td>\n",
       "      <td>136</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>[유가] 2035 시나리오 최고($/배럴)</td>\n",
       "      <td>228.9445</td>\n",
       "      <td>229</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>[두바이] WTI·두바이 로그차분 상관(원 시산 표본)</td>\n",
       "      <td>0.9540</td>\n",
       "      <td>0.9540</td>\n",
       "      <td>±0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>[두바이] 최근 스프레드($, 2026-07)</td>\n",
       "      <td>2.5777</td>\n",
       "      <td>3</td>\n",
       "      <td>±0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>[두바이] 석유제품 기여 변화(%p)</td>\n",
       "      <td>1.5852</td>\n",
       "      <td>1.6000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>[두바이] 나머지 5개 산업 |변화| 최대(%p)</td>\n",
       "      <td>0.7853</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>이하</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>[SHAP] shap_36.json 324개 값 최대 |차이|</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>[SHAP] shap_62.json 558개 값 최대 |차이|</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>[SHAP] shap_69.json 621개 값 최대 |차이|</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>[정비판] 36분류 SHAP 점검 (가)–(다) 최대 오차</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>이하</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>[정비판] 62분류 SHAP 점검 (가)–(다) 최대 오차</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>이하</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>[정비판] 69분류 SHAP 점검 (가)–(다) 최대 오차</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>이하</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>[정비판] 62분류 SHAP − 5장 분해(shap_62_s5.csv) 최대 |차이|</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>이하</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>[정비판] 36분류 δ(연 %p) — 5장 drift_anchor</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>±1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>[정비판] 36분류 바텀업 S1 연평균(%) — 5장 drift_anchor</td>\n",
       "      <td>2.4570</td>\n",
       "      <td>2.4570</td>\n",
       "      <td>±1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>[정비판] 62분류 δ(연 %p) — 5장 drift_anchor</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>±1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>[정비판] 62분류 바텀업 S1 연평균(%) — 5장 drift_anchor</td>\n",
       "      <td>2.5187</td>\n",
       "      <td>2.5187</td>\n",
       "      <td>±1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>[정비판] 69분류 δ(연 %p) — 5장 drift_anchor</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>1.5240</td>\n",
       "      <td>±1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>[정비판] 69분류 바텀업 S1 연평균(%) — 5장 drift_anchor</td>\n",
       "      <td>2.5250</td>\n",
       "      <td>2.5250</td>\n",
       "      <td>±1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>[정비판] S1 유가 누적 변화 2025Q4→2035Q4(100·ln)</td>\n",
       "      <td>22.4299</td>\n",
       "      <td>22.4300</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>[정비판] 36분류 유가 영향 1위 산업</td>\n",
       "      <td>코크스 및 석유정제품 제조업</td>\n",
       "      <td>코크스·석유정제품</td>\n",
       "      <td>포함</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>[정비판] 36분류 1위 유가 기여(%p)</td>\n",
       "      <td>8.4737</td>\n",
       "      <td>8.4700</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>[정비판] 36분류 평균 절대 기여 과거추세(%p)</td>\n",
       "      <td>21.0409</td>\n",
       "      <td>21.0400</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>[정비판] 36분류 평균 절대 기여 산업 동학(%p)</td>\n",
       "      <td>4.9958</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>[정비판] 36분류 평균 절대 기여 외생(%p)</td>\n",
       "      <td>3.8793</td>\n",
       "      <td>3.8800</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>[정비판] 36분류 실질금리 최대 |기여|(%p)</td>\n",
       "      <td>0.2493</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>[정비판] 62분류 유가 영향 1위 산업</td>\n",
       "      <td>14.석탄ㆍ석유제품</td>\n",
       "      <td>석탄·석유제품</td>\n",
       "      <td>포함</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>[정비판] 62분류 1위 유가 기여(%p)</td>\n",
       "      <td>6.2084</td>\n",
       "      <td>6.2100</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>[정비판] 62분류 평균 절대 기여 과거추세(%p)</td>\n",
       "      <td>23.5288</td>\n",
       "      <td>23.5300</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>[정비판] 62분류 평균 절대 기여 산업 동학(%p)</td>\n",
       "      <td>14.2144</td>\n",
       "      <td>14.2100</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>[정비판] 62분류 평균 절대 기여 외생(%p)</td>\n",
       "      <td>3.2986</td>\n",
       "      <td>3.3000</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>[정비판] 62분류 실질금리 최대 |기여|(%p)</td>\n",
       "      <td>0.1572</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>[정비판] 69분류 유가 영향 1위 산업</td>\n",
       "      <td>14.석탄ㆍ석유제품</td>\n",
       "      <td>석탄·석유제품</td>\n",
       "      <td>포함</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>[정비판] 69분류 1위 유가 기여(%p)</td>\n",
       "      <td>6.0811</td>\n",
       "      <td>6.0800</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>[정비판] 69분류 평균 절대 기여 과거추세(%p)</td>\n",
       "      <td>25.7494</td>\n",
       "      <td>25.7500</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>[정비판] 69분류 평균 절대 기여 산업 동학(%p)</td>\n",
       "      <td>15.7439</td>\n",
       "      <td>15.7400</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>[정비판] 69분류 평균 절대 기여 외생(%p)</td>\n",
       "      <td>3.3945</td>\n",
       "      <td>3.3900</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>[정비판] 69분류 실질금리 최대 |기여|(%p)</td>\n",
       "      <td>0.1557</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>[정비판] 62 유가 SHAP·반사실 산업 간 상관</td>\n",
       "      <td>0.8979</td>\n",
       "      <td>0.8980</td>\n",
       "      <td>±0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>[정비판] 총량 2035(조원)</td>\n",
       "      <td>2,671.4229</td>\n",
       "      <td>2,671.4200</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>[정비판] 총량 유가 효과(조원)</td>\n",
       "      <td>1.0742</td>\n",
       "      <td>1.0700</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>[정비판] 총량 유가 효과(%)</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>±0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>[정비판] 총량 유가 CAGR 기여(%p)</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>±5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>[정비판] 바텀업 2035(조원)</td>\n",
       "      <td>2,793.7447</td>\n",
       "      <td>2,793.7400</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>[정비판] 바텀업 유가 효과(조원)</td>\n",
       "      <td>3.9658</td>\n",
       "      <td>3.9700</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>[정비판] 바텀업 유가 효과(%)</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>±0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>[정비판] 바텀업 유가 CAGR 기여(%p)</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>±5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>[정비판] 바텀업/총량 유가 효과 배율</td>\n",
       "      <td>3.6917</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>[정비판] 영구 수준차 외생(2035 시나리오 폭)</td>\n",
       "      <td>유가 · 고용_취업자</td>\n",
       "      <td>유가 · 고용_취업자</td>\n",
       "      <td>일치</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>[정비판] 시나리오 폭 2035 유가(100·ln)</td>\n",
       "      <td>51.7500</td>\n",
       "      <td>51.7500</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>[정비판] 유가 S1 2025 연평균($/배럴)</td>\n",
       "      <td>69.2274</td>\n",
       "      <td>69.2000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>[정비판] 유가 S1 2035 연평균($/배럴)</td>\n",
       "      <td>78.9022</td>\n",
       "      <td>78.9000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>[정비판] 유가 2035 시나리오 최저($/배럴)</td>\n",
       "      <td>65.9046</td>\n",
       "      <td>65.9000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>[정비판] 유가 2035 시나리오 최고($/배럴)</td>\n",
       "      <td>110.5766</td>\n",
       "      <td>110.6000</td>\n",
       "      <td>±0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>[정비판] 실질금리 S1 2035 연평균(%)</td>\n",
       "      <td>1.3119</td>\n",
       "      <td>1.3100</td>\n",
       "      <td>±0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  항목                                       계산                                       공표     허용오차    통과\n",
       "0                                 [외생] 총량 순효과 유가(조원)                                  14.5957                                       15     ±0.5  True\n",
       "1                               [외생] 총량 순효과 세계수요(조원)                                   1.5975                                        2     ±0.5  True\n",
       "2                               [외생] 총량 순효과 실질금리(조원)                                 162.4946                                      162     ±0.5  True\n",
       "3                               [외생] 총량 순효과 실질환율(조원)                                   4.7973                                        5     ±0.5  True\n",
       "4                            [외생] 총량 순효과 고용(취업자)(조원)                                  62.3091                                       62     ±0.5  True\n",
       "5                               [외생] 총량 순효과 우리경기(조원)                                  -0.8160                                       -1     ±0.5  True\n",
       "6                             [외생] 총량 CAGR 기여 유가(%p)                                   0.0505                                   0.0500   ±0.005  True\n",
       "7                           [외생] 총량 CAGR 기여 세계수요(%p)                                   0.0055                                   0.0100   ±0.005  True\n",
       "8                           [외생] 총량 CAGR 기여 실질금리(%p)                                   0.5749                                   0.5700   ±0.005  True\n",
       "9                           [외생] 총량 CAGR 기여 실질환율(%p)                                   0.0166                                   0.0200   ±0.005  True\n",
       "10                       [외생] 총량 CAGR 기여 고용(취업자)(%p)                                   0.2170                                   0.2200   ±0.005  True\n",
       "11                          [외생] 총량 CAGR 기여 우리경기(%p)                                  -0.0028                                  -0.0000   ±0.005  True\n",
       "12                               [외생] 바텀업 순효과 유가(조원)                                  65.4734                                       65     ±0.5  True\n",
       "13                             [외생] 바텀업 순효과 세계수요(조원)                                 -12.0228                                      -12     ±0.5  True\n",
       "14                             [외생] 바텀업 순효과 실질금리(조원)                                 354.2112                                      354     ±0.5  True\n",
       "15                             [외생] 바텀업 순효과 실질환율(조원)                                   9.9489                                       10     ±0.5  True\n",
       "16                          [외생] 바텀업 순효과 고용(취업자)(조원)                                  31.7926                                       32     ±0.5  True\n",
       "17                             [외생] 바텀업 순효과 우리경기(조원)                                   7.6240                                        8     ±0.5  True\n",
       "18                           [외생] 바텀업 CAGR 기여 유가(%p)                                   0.1975                                   0.2000   ±0.005  True\n",
       "19                         [외생] 바텀업 CAGR 기여 세계수요(%p)                                  -0.0359                                  -0.0400   ±0.005  True\n",
       "20                         [외생] 바텀업 CAGR 기여 실질금리(%p)                                   1.1107                                   1.1100   ±0.005  True\n",
       "21                         [외생] 바텀업 CAGR 기여 실질환율(%p)                                   0.0298                                   0.0300   ±0.005  True\n",
       "22                      [외생] 바텀업 CAGR 기여 고용(취업자)(%p)                                   0.0955                                   0.1000   ±0.005  True\n",
       "23                         [외생] 바텀업 CAGR 기여 우리경기(%p)                                   0.0228                                   0.0200   ±0.005  True\n",
       "24                          [외생] 지배 변수(총량·바텀업 모두 1위)                                     실질금리                                     실질금리       일치  True\n",
       "25                                [외생] 총량 CAGR 기여 순위  실질금리 > 고용_취업자 > 유가 > 실질환율 > 세계수요 > 우리경기  실질금리 > 고용_취업자 > 유가 > 실질환율 > 세계수요 > 우리경기       일치  True\n",
       "26                             [외생] 실질금리 2025 연평균(%)                                   0.9945                                   0.9900   ±0.005  True\n",
       "27                         [외생] 실질금리 2035 연평균(S1, %)                                   2.7760                                   2.7800   ±0.005  True\n",
       "28                        [외생] 바텀업 CAGR 2025–2035(%)                                   4.8765                                   4.8800   ±0.005  True\n",
       "29                               [외생] 시나리오 폭 2026 유가                                  25.1560                                  25.2000    ±0.05  True\n",
       "30                          [외생] 시나리오 폭 2026 고용(취업자)                                 531.6052                                 531.6000    ±0.05  True\n",
       "31                             [외생] 시나리오 폭 2026 실질환율                                  11.8690                                  11.9000    ±0.05  True\n",
       "32                             [외생] 시나리오 폭 2026 실질금리                                   4.8030                                   4.8000    ±0.05  True\n",
       "33                             [외생] 시나리오 폭 2026 우리경기                                   2.9428                                   2.9000    ±0.05  True\n",
       "34                             [외생] 시나리오 폭 2026 세계수요                                   1.7900                                   1.8000    ±0.05  True\n",
       "35                               [외생] 시나리오 폭 2035 유가                                  51.7500                                  51.8000    ±0.05  True\n",
       "36                          [외생] 시나리오 폭 2035 고용(취업자)                                   8.6302                                   8.6000    ±0.05  True\n",
       "37                             [외생] 시나리오 폭 2035 실질금리                                   0.0000                                   0.0000    ±0.05  True\n",
       "38                             [외생] 시나리오 폭 2035 우리경기                                   0.0000                                   0.0000    ±0.05  True\n",
       "39                             [외생] 시나리오 폭 2035 세계수요                                   0.0000                                   0.0000    ±0.05  True\n",
       "40                             [외생] 시나리오 폭 2035 실질환율                                   0.0043                                   0.0040  ±0.0005  True\n",
       "41                                    [외생] 영구 수준차 외생                                       유가                                       유가       일치  True\n",
       "42                                  [유가] 총량 2035(조원)                               2,970.3786                                     2970     ±0.5  True\n",
       "43                            [유가] 총량 유가 고정 2035(조원)                               2,955.7830                                     2956     ±0.5  True\n",
       "44                                  [유가] 총량 유가 효과(%)                                   0.4914                                   0.5000    ±0.05  True\n",
       "45                               [유가] 총량 CAGR 기여(%p)                                   0.0505                                   0.0500   ±0.005  True\n",
       "46                                 [유가] 바텀업 2035(조원)                               3,506.9957                                     3507     ±0.5  True\n",
       "47                           [유가] 바텀업 유가 고정 2035(조원)                               3,441.5224                                     3442     ±0.5  True\n",
       "48                                 [유가] 바텀업 유가 효과(%)                                   1.8669                                   1.9000    ±0.05  True\n",
       "49                              [유가] 바텀업 CAGR 기여(%p)                                   0.1975                                   0.2000   ±0.005  True\n",
       "50                             [유가] 36분류 유가 영향 1위 산업                          코크스 및 석유정제품 제조업                                코크스·석유정제품       포함  True\n",
       "51                               [유가] 36분류 1위 기여(%p)                                  41.0629                                  41.1000    ±0.05  True\n",
       "52                             [유가] 62분류 유가 영향 1위 산업                               14.석탄ㆍ석유제품                                  석탄·석유제품       포함  True\n",
       "53                               [유가] 62분류 1위 기여(%p)                                  31.3593                                  31.4000    ±0.05  True\n",
       "54                             [유가] 69분류 유가 영향 1위 산업                               14.석탄ㆍ석유제품                                  석탄·석유제품       포함  True\n",
       "55                               [유가] 69분류 1위 기여(%p)                                  30.8334                                  30.8000    ±0.05  True\n",
       "56                               [유가] 2025 연평균($/배럴)                                  69.2274                                       69     ±0.5  True\n",
       "57                            [유가] S1 2035 연평균($/배럴)                                 163.3638                                      163     ±0.5  True\n",
       "58                           [유가] 2035 시나리오 최저($/배럴)                                 136.4529                                      136     ±0.5  True\n",
       "59                           [유가] 2035 시나리오 최고($/배럴)                                 228.9445                                      229     ±0.5  True\n",
       "60                    [두바이] WTI·두바이 로그차분 상관(원 시산 표본)                                   0.9540                                   0.9540  ±0.0005  True\n",
       "61                         [두바이] 최근 스프레드($, 2026-07)                                   2.5777                                        3     ±0.5  True\n",
       "62                              [두바이] 석유제품 기여 변화(%p)                                   1.5852                                   1.6000    ±0.05  True\n",
       "63                       [두바이] 나머지 5개 산업 |변화| 최대(%p)                                   0.7853                                   1.0000       이하  True\n",
       "64                [SHAP] shap_36.json 324개 값 최대 |차이|                                   0.0050                                   0.0000   ±0.005  True\n",
       "65                [SHAP] shap_62.json 558개 값 최대 |차이|                                   0.0050                                   0.0000   ±0.005  True\n",
       "66                [SHAP] shap_69.json 621개 값 최대 |차이|                                   0.0050                                   0.0000   ±0.005  True\n",
       "67                  [정비판] 36분류 SHAP 점검 (가)–(다) 최대 오차                                   0.0000                                   0.0000       이하  True\n",
       "68                  [정비판] 62분류 SHAP 점검 (가)–(다) 최대 오차                                   0.0000                                   0.0000       이하  True\n",
       "69                  [정비판] 69분류 SHAP 점검 (가)–(다) 최대 오차                                   0.0000                                   0.0000       이하  True\n",
       "70   [정비판] 62분류 SHAP − 5장 분해(shap_62_s5.csv) 최대 |차이|                                   0.0000                                   0.0000       이하  True\n",
       "71              [정비판] 36분류 δ(연 %p) — 5장 drift_anchor                                   1.5240                                   1.5240   ±1e-09  True\n",
       "72        [정비판] 36분류 바텀업 S1 연평균(%) — 5장 drift_anchor                                   2.4570                                   2.4570   ±1e-06  True\n",
       "73              [정비판] 62분류 δ(연 %p) — 5장 drift_anchor                                   1.5240                                   1.5240   ±1e-09  True\n",
       "74        [정비판] 62분류 바텀업 S1 연평균(%) — 5장 drift_anchor                                   2.5187                                   2.5187   ±1e-06  True\n",
       "75              [정비판] 69분류 δ(연 %p) — 5장 drift_anchor                                   1.5240                                   1.5240   ±1e-09  True\n",
       "76        [정비판] 69분류 바텀업 S1 연평균(%) — 5장 drift_anchor                                   2.5250                                   2.5250   ±1e-06  True\n",
       "77           [정비판] S1 유가 누적 변화 2025Q4→2035Q4(100·ln)                                  22.4299                                  22.4300   ±0.005  True\n",
       "78                            [정비판] 36분류 유가 영향 1위 산업                          코크스 및 석유정제품 제조업                                코크스·석유정제품       포함  True\n",
       "79                           [정비판] 36분류 1위 유가 기여(%p)                                   8.4737                                   8.4700   ±0.005  True\n",
       "80                      [정비판] 36분류 평균 절대 기여 과거추세(%p)                                  21.0409                                  21.0400   ±0.005  True\n",
       "81                     [정비판] 36분류 평균 절대 기여 산업 동학(%p)                                   4.9958                                   5.0000   ±0.005  True\n",
       "82                        [정비판] 36분류 평균 절대 기여 외생(%p)                                   3.8793                                   3.8800   ±0.005  True\n",
       "83                       [정비판] 36분류 실질금리 최대 |기여|(%p)                                   0.2493                                   0.2500   ±0.005  True\n",
       "84                            [정비판] 62분류 유가 영향 1위 산업                               14.석탄ㆍ석유제품                                  석탄·석유제품       포함  True\n",
       "85                           [정비판] 62분류 1위 유가 기여(%p)                                   6.2084                                   6.2100   ±0.005  True\n",
       "86                      [정비판] 62분류 평균 절대 기여 과거추세(%p)                                  23.5288                                  23.5300   ±0.005  True\n",
       "87                     [정비판] 62분류 평균 절대 기여 산업 동학(%p)                                  14.2144                                  14.2100   ±0.005  True\n",
       "88                        [정비판] 62분류 평균 절대 기여 외생(%p)                                   3.2986                                   3.3000   ±0.005  True\n",
       "89                       [정비판] 62분류 실질금리 최대 |기여|(%p)                                   0.1572                                   0.1600   ±0.005  True\n",
       "90                            [정비판] 69분류 유가 영향 1위 산업                               14.석탄ㆍ석유제품                                  석탄·석유제품       포함  True\n",
       "91                           [정비판] 69분류 1위 유가 기여(%p)                                   6.0811                                   6.0800   ±0.005  True\n",
       "92                      [정비판] 69분류 평균 절대 기여 과거추세(%p)                                  25.7494                                  25.7500   ±0.005  True\n",
       "93                     [정비판] 69분류 평균 절대 기여 산업 동학(%p)                                  15.7439                                  15.7400   ±0.005  True\n",
       "94                        [정비판] 69분류 평균 절대 기여 외생(%p)                                   3.3945                                   3.3900   ±0.005  True\n",
       "95                       [정비판] 69분류 실질금리 최대 |기여|(%p)                                   0.1557                                   0.1600   ±0.005  True\n",
       "96                      [정비판] 62 유가 SHAP·반사실 산업 간 상관                                   0.8979                                   0.8980  ±0.0005  True\n",
       "97                                 [정비판] 총량 2035(조원)                               2,671.4229                               2,671.4200   ±0.005  True\n",
       "98                                [정비판] 총량 유가 효과(조원)                                   1.0742                                   1.0700   ±0.005  True\n",
       "99                                 [정비판] 총량 유가 효과(%)                                   0.0402                                   0.0400  ±0.0005  True\n",
       "100                          [정비판] 총량 유가 CAGR 기여(%p)                                   0.0041                                   0.0041   ±5e-05  True\n",
       "101                               [정비판] 바텀업 2035(조원)                               2,793.7447                               2,793.7400   ±0.005  True\n",
       "102                              [정비판] 바텀업 유가 효과(조원)                                   3.9658                                   3.9700   ±0.005  True\n",
       "103                               [정비판] 바텀업 유가 효과(%)                                   0.1420                                   0.1420  ±0.0005  True\n",
       "104                         [정비판] 바텀업 유가 CAGR 기여(%p)                                   0.0146                                   0.0146   ±5e-05  True\n",
       "105                            [정비판] 바텀업/총량 유가 효과 배율                                   3.6917                                   3.6900   ±0.005  True\n",
       "106                     [정비판] 영구 수준차 외생(2035 시나리오 폭)                              유가 · 고용_취업자                              유가 · 고용_취업자       일치  True\n",
       "107                     [정비판] 시나리오 폭 2035 유가(100·ln)                                  51.7500                                  51.7500   ±0.005  True\n",
       "108                       [정비판] 유가 S1 2025 연평균($/배럴)                                  69.2274                                  69.2000    ±0.05  True\n",
       "109                       [정비판] 유가 S1 2035 연평균($/배럴)                                  78.9022                                  78.9000    ±0.05  True\n",
       "110                      [정비판] 유가 2035 시나리오 최저($/배럴)                                  65.9046                                  65.9000    ±0.05  True\n",
       "111                      [정비판] 유가 2035 시나리오 최고($/배럴)                                 110.5766                                 110.6000    ±0.05  True\n",
       "112                        [정비판] 실질금리 S1 2035 연평균(%)                                   1.3119                                   1.3100   ±0.005  True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "검증 통과: 이전 판 공표 수치 67개(외생 카드·유가 카드·SHAP 산출물)와 정비판 항목 46개(5장 분해 대조·정비판 기록 수치)를 반올림 범위 안에서 재현했다(합계 113개).\n"
     ]
    }
   ],
   "source": [
    "P = C.PUBLISHED\n",
    "norm = lambda s: str(s).replace(\" \", \"\").replace(\"·\", \"\").replace(\"ㆍ\", \"\").replace(\"및\", \"\")   # noqa: E731\n",
    "checks = []\n",
    "def num(item, calc, pub, tol):\n",
    "    checks.append({\"항목\": item, \"계산\": round(float(calc), 6), \"공표\": pub, \"허용오차\": f\"±{tol:g}\", \"통과\": abs(float(calc) - pub) <= tol + 1e-12})\n",
    "def same(item, calc, pub, how=\"일치\"):\n",
    "    ok = (norm(pub) in norm(calc)) if how == \"포함\" else (calc == pub)\n",
    "    checks.append({\"항목\": item, \"계산\": calc, \"공표\": pub, \"허용오차\": how, \"통과\": bool(ok)})\n",
    "def le(item, calc, pub):\n",
    "    checks.append({\"항목\": item, \"계산\": round(float(calc), 6), \"공표\": pub, \"허용오차\": \"이하\", \"통과\": float(calc) <= pub})\n",
    "\n",
    "for k in F.EXOG:\n",
    "    num(f\"[외생] 총량 순효과 {NAME[k]}(조원)\", eff.loc[k, \"총량_효과_조원\"], P[\"총량_조원\"][k], 0.5)\n",
    "for k in F.EXOG:\n",
    "    num(f\"[외생] 총량 CAGR 기여 {NAME[k]}(%p)\", eff.loc[k, \"총량_CAGR기여_%p\"], P[\"총량_%p\"][k], 0.005)\n",
    "for k in F.EXOG:\n",
    "    num(f\"[외생] 바텀업 순효과 {NAME[k]}(조원)\", eff.loc[k, \"바텀업_효과_조원\"], P[\"바텀업_조원\"][k], 0.5)\n",
    "for k in F.EXOG:\n",
    "    num(f\"[외생] 바텀업 CAGR 기여 {NAME[k]}(%p)\", eff.loc[k, \"바텀업_CAGR기여_%p\"], P[\"바텀업_%p\"][k], 0.005)\n",
    "same(\"[외생] 지배 변수(총량·바텀업 모두 1위)\",\n",
    "     eff[\"총량_효과_조원\"].abs().idxmax() if eff[\"총량_효과_조원\"].abs().idxmax() == eff[\"바텀업_효과_조원\"].abs().idxmax() else \"불일치\", P[\"지배_외생\"])\n",
    "same(\"[외생] 총량 CAGR 기여 순위\", \" > \".join(eff.sort_values(\"총량_CAGR기여_%p\", ascending=False).index), \" > \".join(P[\"총량_순위\"]))\n",
    "num(\"[외생] 실질금리 2025 연평균(%)\", paths.loc[\"실질금리\", \"2025 연평균\"], P[\"실질금리_2025\"], 0.005)\n",
    "num(\"[외생] 실질금리 2035 연평균(S1, %)\", paths.loc[\"실질금리\", \"2035 연평균(S1)\"], P[\"실질금리_2035\"], 0.005)\n",
    "num(\"[외생] 바텀업 CAGR 2025–2035(%)\", tot.loc[\"62\", \"CAGR(%)\"], P[\"바텀업_CAGR\"], 0.005)\n",
    "for k in [\"유가\", \"고용_취업자\", \"실질환율\", \"실질금리\", \"우리경기\", \"세계수요\"]:\n",
    "    num(f\"[외생] 시나리오 폭 2026 {NAME[k]}\", per.loc[k, \"폭_2026\"], P[\"폭_2026\"][k], 0.05)\n",
    "for k in [\"유가\", \"고용_취업자\", \"실질금리\", \"우리경기\", \"세계수요\"]:\n",
    "    num(f\"[외생] 시나리오 폭 2035 {NAME[k]}\", per.loc[k, \"폭_2035\"], P[\"폭_2035\"][k], 0.05)\n",
    "num(\"[외생] 시나리오 폭 2035 실질환율\", per.loc[\"실질환율\", \"폭_2035\"], P[\"폭_2035\"][\"실질환율\"], 0.0005)\n",
    "same(\"[외생] 영구 수준차 외생\", \" · \".join(per.index[per[\"판정\"] == \"영구\"]), P[\"영구_외생\"])\n",
    "\n",
    "o_agg, o_bu = oil_cmp.loc[\"총량 예측모형\"], oil_cmp.loc[\"62 바텀업\"]\n",
    "num(\"[유가] 총량 2035(조원)\", o_agg[\"2035(조원)\"], P[\"총량_2035_조\"], 0.5)\n",
    "num(\"[유가] 총량 유가 고정 2035(조원)\", o_agg[\"유가 고정 2035(조원)\"], P[\"총량_유가없음_조\"], 0.5)\n",
    "num(\"[유가] 총량 유가 효과(%)\", o_agg[\"유가 효과(%)\"], P[\"총량_유가효과_%\"], 0.05)\n",
    "num(\"[유가] 총량 CAGR 기여(%p)\", o_agg[\"CAGR 기여(%p)\"], P[\"총량_유가_%p\"], 0.005)\n",
    "num(\"[유가] 바텀업 2035(조원)\", o_bu[\"2035(조원)\"], P[\"바텀업_2035_조\"], 0.5)\n",
    "num(\"[유가] 바텀업 유가 고정 2035(조원)\", o_bu[\"유가 고정 2035(조원)\"], P[\"바텀업_유가없음_조\"], 0.5)\n",
    "num(\"[유가] 바텀업 유가 효과(%)\", o_bu[\"유가 효과(%)\"], P[\"바텀업_유가효과_%\"], 0.05)\n",
    "num(\"[유가] 바텀업 CAGR 기여(%p)\", o_bu[\"CAGR 기여(%p)\"], P[\"바텀업_유가_%p\"], 0.005)\n",
    "for r in (\"36\", \"62\", \"69\"):\n",
    "    g = orank[(orank[\"분류\"] == r) & orank[\"|기여| 최대\"]].iloc[0]\n",
    "    same(f\"[유가] {r}분류 유가 영향 1위 산업\", g[\"산업\"], P[\"유가1위\"][r][0], \"포함\")\n",
    "    num(f\"[유가] {r}분류 1위 기여(%p)\", g[\"유가 기여(%p)\"], P[\"유가1위\"][r][1], 0.05)\n",
    "num(\"[유가] 2025 연평균($/배럴)\", dollars.loc[2025, \"S1\"], P[\"유가_2025_$\"], 0.5)\n",
    "num(\"[유가] S1 2035 연평균($/배럴)\", dollars.loc[2035, \"S1\"], P[\"유가_S1_2035_$\"], 0.5)\n",
    "num(\"[유가] 2035 시나리오 최저($/배럴)\", dollars.loc[2035].min(), P[\"유가_2035_최저_$\"], 0.5)\n",
    "num(\"[유가] 2035 시나리오 최고($/배럴)\", dollars.loc[2035].max(), P[\"유가_2035_최고_$\"], 0.5)\n",
    "num(\"[두바이] WTI·두바이 로그차분 상관(원 시산 표본)\", bm.loc[\"원 시산 표본(파일 전체)\", \"corr\"], P[\"WTI_두바이_상관\"], 0.0005)\n",
    "num(\"[두바이] 최근 스프레드($, 2026-07)\", bm.loc[\"원 시산 표본(파일 전체)\", \"WTI_$\"] - bm.loc[\"원 시산 표본(파일 전체)\", \"DUB_$\"], P[\"스프레드_$\"], 0.5)\n",
    "num(\"[두바이] 석유제품 기여 변화(%p)\", dub.loc[\"14.석탄ㆍ석유제품\", \"변화\"], P[\"정유_변화_%p\"], 0.05)\n",
    "le(\"[두바이] 나머지 5개 산업 |변화| 최대(%p)\", dub.drop(index=\"14.석탄ㆍ석유제품\")[\"변화\"].abs().max(), P[\"나머지_변화_한도_%p\"])\n",
    "for r in (\"36\", \"62\", \"69\"):\n",
    "    num(f\"[SHAP] shap_{r}.json {int(shap_chk.loc[int(r), '공표 대조 값 수'])}개 값 최대 |차이|\", shap_chk.loc[int(r), \"반올림 전 최대 |차이|\"], 0.0, 0.005)\n",
    "n_prev = len(checks)\n",
    "\n",
    "# 정비판(02 10절): 5장 기록과의 대조, 정비판 진단 기록 수치(C.JEONGBI)\n",
    "J = C.JEONGBI\n",
    "da5 = pd.read_csv(C.S5_DRIFT, index_col=0, encoding=\"utf-8-sig\")\n",
    "da5.index = da5.index.astype(str)\n",
    "for r in (\"36\", \"62\", \"69\"):\n",
    "    le(f\"[정비판] {r}분류 SHAP 점검 (가)–(다) 최대 오차\", scj.loc[r, [\"(가) 성분 합 − 합계\", \"(나) 합계 − 전망 로그성장\", \"(다) 재설정분 − (−10(1−ΣA)δ)\"]].max(), 1e-8)\n",
    "le(\"[정비판] 62분류 SHAP − 5장 분해(shap_62_s5.csv) 최대 |차이|\", scj.loc[\"62\", \"(라) 5장 기록과 최대 |차이|\"], 1e-8)\n",
    "for r in (\"36\", \"62\", \"69\"):\n",
    "    num(f\"[정비판] {r}분류 δ(연 %p) — 5장 drift_anchor\", ancj.loc[r, \"δ(연 %p)\"], float(da5.loc[r, \"δ(연 %p)\"]), 1e-9)\n",
    "    num(f\"[정비판] {r}분류 바텀업 S1 연평균(%) — 5장 drift_anchor\", ancj.loc[r, \"S1 연평균 재설정 후(%)\"], float(da5.loc[r, \"바텀업 S1 연평균 — 재설정 후(%)\"]), 1e-6)\n",
    "num(\"[정비판] S1 유가 누적 변화 2025Q4→2035Q4(100·ln)\", ofj.loc[\"62\", \"정비판 유가 누적(100·ln)\"], J[\"유가_누적_S1\"], 0.005)\n",
    "for r in (\"36\", \"62\", \"69\"):\n",
    "    g = orj[(orj[\"분류\"] == r) & orj[\"|기여| 최대\"]].iloc[0]\n",
    "    same(f\"[정비판] {r}분류 유가 영향 1위 산업\", g[\"산업\"], J[\"유가1위\"][r][0], \"포함\")\n",
    "    num(f\"[정비판] {r}분류 1위 유가 기여(%p)\", g[\"유가 기여(%p)\"], J[\"유가1위\"][r][1], 0.005)\n",
    "    for c, key in ((\"과거추세\", \"과거추세\"), (\"산업 동학\", \"산업 동학\"), (\"외생(6종 절댓값 합)\", \"외생\")):\n",
    "        num(f\"[정비판] {r}분류 평균 절대 기여 {key}(%p)\", ssj.loc[(r, \"정비판\"), c], J[\"평균절대\"][r][key], 0.005)\n",
    "    num(f\"[정비판] {r}분류 실질금리 최대 |기여|(%p)\", ssj.loc[(r, \"정비판\"), \"실질금리 최대 |기여|\"], J[\"실질금리_최대절대\"][r], 0.005)\n",
    "num(\"[정비판] 62 유가 SHAP·반사실 산업 간 상관\", svj.iloc[:, 0].corr(svj.iloc[:, 1]), J[\"유가_SHAP_반사실_상관\"], 0.0005)\n",
    "o_agg_j, o_bu_j = ocj.loc[\"총량 예측모형\"], ocj.loc[\"62 바텀업(드리프트 재설정)\"]\n",
    "for tag, o in ((\"총량\", o_agg_j), (\"바텀업\", o_bu_j)):\n",
    "    num(f\"[정비판] {tag} 2035(조원)\", o[\"2035(조원)\"], J[f\"{tag}_2035_조\"], 0.005)\n",
    "    num(f\"[정비판] {tag} 유가 효과(조원)\", o[\"유가 효과(조원)\"], J[f\"{tag}_유가효과_조\"], 0.005)\n",
    "    num(f\"[정비판] {tag} 유가 효과(%)\", o[\"유가 효과(%)\"], J[f\"{tag}_유가효과_%\"], 0.0005)\n",
    "    num(f\"[정비판] {tag} 유가 CAGR 기여(%p)\", o[\"CAGR 기여(%p)\"], J[f\"{tag}_유가_%p\"], 0.00005)\n",
    "num(\"[정비판] 바텀업/총량 유가 효과 배율\", o_bu_j[\"유가 효과(조원)\"] / o_agg_j[\"유가 효과(조원)\"], J[\"배율\"], 0.005)\n",
    "same(\"[정비판] 영구 수준차 외생(2035 시나리오 폭)\", \" · \".join(per_j.index[per_j[\"판정\"] == \"영구\"]), J[\"영구_외생\"])\n",
    "num(\"[정비판] 시나리오 폭 2035 유가(100·ln)\", per_j.loc[\"유가\", \"폭_2035\"], J[\"유가_폭_2035\"], 0.005)\n",
    "num(\"[정비판] 유가 S1 2025 연평균($/배럴)\", dol_j.loc[2025, \"S1\"], J[\"유가_2025_$\"], 0.05)\n",
    "num(\"[정비판] 유가 S1 2035 연평균($/배럴)\", dol_j.loc[2035, \"S1\"], J[\"유가_S1_2035_$\"], 0.05)\n",
    "num(\"[정비판] 유가 2035 시나리오 최저($/배럴)\", dol_j.loc[2035].min(), J[\"유가_2035_최저_$\"], 0.05)\n",
    "num(\"[정비판] 유가 2035 시나리오 최고($/배럴)\", dol_j.loc[2035].max(), J[\"유가_2035_최고_$\"], 0.05)\n",
    "num(\"[정비판] 실질금리 S1 2035 연평균(%)\", pth_j.loc[\"실질금리\", \"2035 연평균(S1)\"], J[\"실질금리_2035\"], 0.005)\n",
    "\n",
    "res = pd.DataFrame(checks)\n",
    "res.to_csv(os.path.join(C.OUTPUT, \"verification.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "with pd.option_context(\"display.max_rows\", 200, \"display.float_format\", lambda v: f\"{v:,.4f}\"):\n",
    "    display(res)\n",
    "assert res[\"통과\"].all(), \"공표·기록 수치와 계산 결과가 다르다 — 위 표에서 통과=False 항목을 확인한다\"\n",
    "print(f\"검증 통과: 이전 판 공표 수치 {n_prev}개(외생 카드·유가 카드·SHAP 산출물)와 정비판 항목 {len(res) - n_prev}개\"\n",
    "      f\"(5장 분해 대조·정비판 기록 수치)를 반올림 범위 안에서 재현했다(합계 {len(res)}개).\")"
   ]
  }
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