feat(model): KAP YTM PD floor integration, expanded 226-var search, ADF fix (AIC->BIC), Model#2 with 6-test diagnostics

- Replace hardcoded DEFAULT_PD_FLOORS with build_complete_pd_floor_table() (KAP bond YTM)
- Fix ADF test: autolag='AIC' -> 'BIC' for small sample (N=26) robustness
- Expand variable search: 40 -> 226 vars (log/diff/return/lag2), 1.9M combos
- Select Model #2: HOUSING_PRICE + CREDIT_SPREAD_LAG1 + CURRENT_ACCOUNT_R
- Add 6-test diagnostics table to AR1 sheet (ADF/LB/DW/BP/ARCH/Shapiro)
- Add Korean variable names for transformed variables
- Generate report v7 with full diagnostics

main.py

@@ -103,18 +103,38 @@ def main():
     tm_source = data_config.get("transition_source", "builtin")
     tm_dir = data_config.get("transition_dir", None)
     logger.info(f"전이행렬 로딩 중 (source={tm_source})...")
-    transition_matrices = load_transition_matrices(tm_source, data_dir=tm_dir)
+    transition_matrices_all = load_transition_matrices(tm_source, data_dir=tm_dir)
+    # 2000-2025 필터
+    transition_matrices_raw = {y:m for y,m in transition_matrices_all.items() if 2000 <= y <= 2025}
+    
+    # PD 플로어 적용: KAP 채권 YTM 기반 시장내재 PD
+    from data.pd_floor import apply_pd_floor_to_matrices, build_complete_pd_floor_table
+    pd_floors_broad, _, pd_floors_full = build_complete_pd_floor_table()
+    transition_matrices = apply_pd_floor_to_matrices(transition_matrices_raw, pd_floors_broad)
+    
     ttc_matrix = compute_ttc_matrix(transition_matrices)
     default_rates = get_default_rates(transition_matrices)
     
     print(f"\n  전이행렬: {len(transition_matrices)}개 연도 ({min(transition_matrices.keys())}~{max(transition_matrices.keys())})"
           f" [source={tm_source}]")
-    print(display_matrix(ttc_matrix, "TTC 전이행렬 (장기 평균)"))
+    print(f"  PD 플로어 (KAP 채권 YTM 기반): AAA={pd_floors_broad['AAA']*10000:.0f}bp, AA={pd_floors_broad['AA']*10000:.0f}bp, "
+          f"A={pd_floors_broad['A']*10000:.0f}bp, BBB={pd_floors_broad['BBB']*10000:.0f}bp")
+    print(display_matrix(ttc_matrix, "TTC 전이행렬 (KAP PD Floor 적용 후 장기 평균)"))
     
     # 거시경제변수
     if args.no_api:
         logger.info("Fallback 거시경제 데이터 사용")
         macro_data = _fallback_macro_data()
+        # ECOS fallback 데이터도 병합 (37개 변수)
+        try:
+            from data.ecos_fetcher import load_macro_data as load_ecos_macro
+            ecos_data = load_ecos_macro()
+            if ecos_data is not None and not ecos_data.empty:
+                macro_data = pd.concat([macro_data, ecos_data], axis=1)
+                macro_data = macro_data.loc[:, ~macro_data.columns.duplicated()]
+                logger.info(f"ECOS fallback 병합 완료: {len(macro_data.columns)}개 변수")
+        except Exception as e:
+            logger.warning(f"ECOS fallback 병합 실패: {e}")
     else:
         macro_data = load_macro_data(args.config)
     
@@ -167,10 +187,19 @@ def main():
         model_input = macro_data
     
     forced_vars = config.get("model", {}).get("macro_vars", None)
-    macro_model = build_macro_zt_model(zt_dict, model_input, method="stepwise_aic",
+    macro_method = config.get("model", {}).get("macro_method", "ar1_macro")
+    macro_model = build_macro_zt_model(zt_dict, model_input, method=macro_method,
                                         forced_vars=forced_vars)
     
     print(f"\n  선택된 변수: {macro_model.selected_vars}")
+    if macro_model.is_ar1:
+        import math
+        phi = macro_model.ar1_phi
+        half_life = math.log(2) / abs(math.log(abs(phi))) if 0 < abs(phi) < 1 else float('inf')
+        print(f"  [AR(1)+Macro] φ = {phi:.4f} (반감기 = {half_life:.1f}년)")
+        print(f"  c = {macro_model.ar1_const:.4f}")
+        for var, beta in macro_model.ar1_beta.items():
+            print(f"  β({var}) = {beta:+.6f}")
     print(macro_model.summary())
     
     diag = macro_model.diagnostics()