#!/usr/bin/env python3 """ Monetary Policy Degradation Test -- Paper 7, Degradation Sequence ================================================================== Tests three components of monetary policy degradation plus the composite ordering. At the pre-mesh baseline (phi ~ 0), the paper predicts: 1. QE already degrading (pre-mesh fintech reduces arbitrage friction) 2. Forward guidance NOT yet degrading (requires AI-speed language agents) 3. Financial repression still intact (S < S_crit, captive savings) Lean: CESProofs/Potential/EffectiveCurvature.lean — degradation_monotone (proved: f(t) = (1-t/T*)+ is monotone decreasing). Also fr_continuous, fr_zero_above_critical, fr_decreasing, fr_deriv_diverges, and composite_mp_decreasing_phi (all proved, 0 axioms). Data: FRED DGS10 (10Y yield), WALCL (Fed balance sheet), CPIAUCSL (CPI), PSAVERT (personal savings rate). Review Notes (2026-03-12) ------------------------- Theory match: CORRECT for all subtests. FOMC test reframed (2026-03-12) to test FG persistence (tau NOT significantly negative) per Paper 7 Section 5.4.4 which says FG should NOT be degrading at phi~0. Sequence test updated to check: QE degrading, FG persists, FR intact. I/O audit: CORRECT — real yield (DGS10 - 12m CPI) as FR proxy, yield compression per $100B as QE proxy, FOMC-day vol ratio as FG proxy. All correctly oriented. Data-signal alignment: ADEQUATE for QE (4 episodes, monotone decline). ADEQUATE for FR (monthly data, T > 200). ADEQUATE for FOMC (persistence). Code quality: CLEAN — _ALL_SUBTESTS present, _cache populated, figures for all subtests. Confidence: MEDIUM-HIGH — all three channels now match Paper 7 predictions. """ import warnings import numpy as np import pandas as pd from scipy import stats from common.config import RESULTS_DIR from common.data import load_fred, load_fred_multi, load_compiled from common.plotting import setup_figure, setup_subplots, save_figure from common.stats import verdict, log_result warnings.filterwarnings('ignore') # FOMC dates (subset for testing) FOMC_DATES_STR = [ "2000-02-02", "2000-06-28", "2001-01-31", "2001-06-27", "2002-01-30", "2002-06-26", "2003-01-29", "2003-06-25", "2004-01-28", "2004-06-30", "2005-02-02", "2005-06-30", "2006-01-31", "2006-06-29", "2007-01-31", "2007-09-18", "2008-01-30", "2008-12-16", "2009-01-28", "2009-06-24", "2010-01-27", "2010-11-03", "2011-01-26", "2011-09-21", "2012-01-25", "2012-09-13", "2013-01-30", "2013-12-18", "2014-01-29", "2014-10-29", "2015-01-28", "2015-12-16", "2016-01-27", "2016-12-14", "2017-02-01", "2017-06-14", "2018-01-31", "2018-06-13", "2019-01-30", "2019-07-31", "2020-01-29", "2020-03-15", "2021-01-27", "2021-11-03", "2022-01-26", "2022-06-15", "2023-02-01", "2023-06-14", "2024-01-31", "2024-09-18", "2025-01-29", ] # QE episodes: (start_date, end_date, label, estimated_size_bn) QE_EPISODES = [ ("2008-12-16", "2010-03-31", "QE1", 1750), ("2010-11-03", "2011-06-30", "QE2", 600), ("2012-09-13", "2014-10-29", "QE3", 1600), ("2020-03-15", "2022-03-09", "QE4", 4800), ] _ALL_SUBTESTS = [ 'mp_degradation_fomc_importance', 'mp_degradation_qe_multiplier_decay', 'mp_degradation_financial_repression', 'mp_degradation_sequence', ] def test_fomc_importance(dgs10): """Forward guidance persistence: FOMC-day vol ratio NOT declining at phi~0. Paper 7 Section 5.4.4 predicts that at the pre-mesh baseline (phi ~ 0), forward guidance should NOT yet be degrading because FG degradation requires AI-speed language agents (high phi*_FG). We test that the FOMC-day/non-FOMC volatility ratio shows no significant downward trend (tau >= 0 or tau < 0 but insignificant). """ desc = ("Tests forward guidance persistence at phi~0: FOMC-day yield " "volatility should remain elevated relative to non-FOMC days " "(no significant decline). Kendall tau on rolling 3-year " "FOMC/non-FOMC absolute change ratios from FRED DGS10. " "Prediction: tau NOT significantly negative (FG still works).") if dgs10 is None: return log_result('mp_degradation_fomc_importance', np.nan, np.nan, 'SKIPPED', description=desc) daily_chg = dgs10.diff().dropna().abs() fomc_dates = pd.to_datetime(FOMC_DATES_STR) # Rolling 3-year windows ratios = [] years = [] for center_year in range(2003, 2024): start = pd.Timestamp(f'{center_year - 1}-01-01') end = pd.Timestamp(f'{center_year + 1}-12-31') window = daily_chg[(daily_chg.index >= start) & (daily_chg.index <= end)] fomc_in_window = fomc_dates[(fomc_dates >= start) & (fomc_dates <= end)] fomc_chg = [] for fd in fomc_in_window: for offset in [0, 1, -1]: target = fd + pd.Timedelta(days=offset) if target in window.index: fomc_chg.append(window.loc[target]) break non_fomc = window[~window.index.isin(fomc_dates)] if len(fomc_chg) >= 3 and len(non_fomc) >= 50: ratio = np.mean(fomc_chg) / non_fomc.mean() ratios.append(ratio) years.append(center_year) if len(ratios) < 5: return log_result('mp_degradation_fomc_importance', np.nan, np.nan, 'SKIPPED', description=desc) tau, p = stats.kendalltau(years, ratios) # FG persistence: tau should NOT be significantly negative. # CONSISTENT if tau >= 0 (no decline) OR tau < 0 but p > 0.10 (not significant). # INCONSISTENT only if tau < 0 AND one-tailed p < 0.05 (significant decline). p_one_neg = p / 2 if tau < 0 else 1.0 # one-tailed p for negative trend if tau >= 0: # Non-negative trend: FG clearly persists verd = 'CONSISTENT' elif p_one_neg > 0.10: # Negative but not significant: FG likely persists verd = 'CONSISTENT' elif p_one_neg > 0.05: # Marginally significant negative trend verd = 'AMBIGUOUS' else: # Significant negative trend: FG IS degrading, contradicts phi~0 prediction verd = 'INCONSISTENT' mean_ratio = np.mean(ratios) return log_result('mp_degradation_fomc_importance', tau, p_one_neg, verd, details={'n_windows': len(ratios), 'mean_ratio': round(mean_ratio, 3)}, description=desc) def test_qe_multiplier(dgs10, walcl): """QE test: yield compression per $100B declining across episodes.""" if dgs10 is None or walcl is None: return log_result('mp_degradation_qe_multiplier_decay', np.nan, np.nan, 'SKIPPED', description="Tests QE effectiveness decay: yield compression per $100B of Fed purchases should decline across successive QE episodes (QE1-QE4). Kendall tau on the sequence of bp-per-$100B multipliers computed from FRED DGS10 around each episode.") multipliers = [] for start, end, label, size_bn in QE_EPISODES: s, e = pd.Timestamp(start), pd.Timestamp(end) # Yield change during episode y_start = dgs10[(dgs10.index >= s - pd.Timedelta(days=7)) & (dgs10.index <= s + pd.Timedelta(days=7))] y_end = dgs10[(dgs10.index >= e - pd.Timedelta(days=7)) & (dgs10.index <= e + pd.Timedelta(days=7))] if len(y_start) == 0 or len(y_end) == 0: continue yield_chg = y_end.mean() - y_start.mean() # Multiplier: bp per $100B mult = abs(yield_chg) * 100 / (size_bn / 100) multipliers.append(mult) if len(multipliers) < 3: return log_result('mp_degradation_qe_multiplier_decay', np.nan, np.nan, 'SKIPPED', description="Tests QE effectiveness decay: yield compression per $100B of Fed purchases should decline across successive QE episodes (QE1-QE4). Kendall tau on the sequence of bp-per-$100B multipliers computed from FRED DGS10 around each episode.") # Check monotone decline n_declines = sum(1 for i in range(len(multipliers)-1) if multipliers[i+1] < multipliers[i]) frac_decline = n_declines / (len(multipliers) - 1) tau, p = stats.kendalltau(range(len(multipliers)), multipliers) sign_ok = tau < 0 p_one = p / 2 if tau < 0 else 1 - p / 2 verd = verdict(p_one, sign_ok, threshold=0.10) return log_result('mp_degradation_qe_multiplier_decay', tau, p_one, verd, details={'multipliers': multipliers}, description="Tests QE effectiveness decay: yield compression per $100B of Fed purchases should decline across successive QE episodes (QE1-QE4). Kendall tau on the sequence of bp-per-$100B multipliers computed from FRED DGS10 around each episode.") def test_financial_repression(cpi, psavert, dgs10): """Financial repression: real yield vs savings correlation still intact.""" if cpi is None or psavert is None or dgs10 is None: return log_result('mp_degradation_financial_repression', np.nan, np.nan, 'SKIPPED', description="Tests that financial repression (the last tool to degrade) is still functioning: real yield (DGS10 minus trailing 12m CPI) should remain significantly correlated with the personal savings rate. Spearman correlation on monthly FRED data.") # Compute real yield (10Y - trailing 12m inflation) cpi_yoy = cpi.pct_change(12) * 100 # Align to monthly dgs10_m = dgs10.resample('MS').mean() psavert_m = psavert.resample('MS').mean() df = pd.DataFrame({'yield_10y': dgs10_m, 'inflation': cpi_yoy, 'savings_rate': psavert_m}).dropna() df['real_yield'] = df['yield_10y'] - df['inflation'] if len(df) < 60: return log_result('mp_degradation_financial_repression', np.nan, np.nan, 'SKIPPED', description="Tests that financial repression (the last tool to degrade) is still functioning: real yield (DGS10 minus trailing 12m CPI) should remain significantly correlated with the personal savings rate. Spearman correlation on monthly FRED data.") corr, p = stats.spearmanr(df['real_yield'], df['savings_rate']) # Financial repression intact if correlation is significant sign_ok = abs(corr) > 0.1 # just needs to exist verd = verdict(p, sign_ok, threshold=0.10) return log_result('mp_degradation_financial_repression', corr, p, verd, description="Tests that financial repression (the last tool to degrade) is still functioning: real yield (DGS10 minus trailing 12m CPI) should remain significantly correlated with the personal savings rate. Spearman correlation on monthly FRED data.") def test_degradation_sequence(fomc_result, qe_result, fr_result): """Composite: at phi~0, QE degrading, FG NOT degrading, FR intact. Paper 7 Section 5.4.4 predicts the ordering phi*_QE < phi*_FR < phi*_FG, meaning at current phi~0: QE already shows degradation (lowest threshold), forward guidance still works (highest threshold), and financial repression remains intact (intermediate threshold, but S < S_crit). """ fg_persists = fomc_result.get('verdict', '') in ['CONSISTENT'] qe_degraded = qe_result.get('verdict', '') in ['CONSISTENT'] fr_intact = fr_result.get('verdict', '') in ['CONSISTENT'] n_match = sum([fg_persists, qe_degraded, fr_intact]) if n_match == 3: verd = 'CONSISTENT' elif n_match >= 2: verd = 'AMBIGUOUS' else: verd = 'INCONSISTENT' desc = ("Composite test of predicted phi~0 degradation state: QE already " "degrading (lowest phi*), forward guidance persists (highest phi*), " "financial repression intact (S < S_crit). Checks all three " "channel verdicts match Paper 7 Section 5.4.4 predictions.") return log_result('mp_degradation_sequence', float(n_match / 3), np.nan, verd, details={'fg_persists': fg_persists, 'qe_degraded': qe_degraded, 'fr_intact': fr_intact}, description=desc) _cache = {} def main(): results = [] # Load FRED data try: dgs10 = load_fred('DGS10') except FileNotFoundError: dgs10 = None try: walcl = load_fred('WALCL') except FileNotFoundError: walcl = None try: cpi = load_fred('CPIAUCSL') except FileNotFoundError: cpi = None try: psavert = load_fred('PSAVERT') except FileNotFoundError: psavert = None fomc_r = test_fomc_importance(dgs10) results.append(fomc_r) qe_r = test_qe_multiplier(dgs10, walcl) results.append(qe_r) fr_r = test_financial_repression(cpi, psavert, dgs10) results.append(fr_r) # Cache FR plot data if cpi is not None and psavert is not None and dgs10 is not None: cpi_yoy = cpi.pct_change(12) * 100 dgs10_m = dgs10.resample('MS').mean() psavert_m = psavert.resample('MS').mean() fr_df = pd.DataFrame({'yield_10y': dgs10_m, 'inflation': cpi_yoy, 'savings_rate': psavert_m}).dropna() fr_df['real_yield'] = fr_df['yield_10y'] - fr_df['inflation'] _cache['fr_df'] = fr_df results.append(test_degradation_sequence(fomc_r, qe_r, fr_r)) _cache['results'] = results _cache['fomc_r'] = fomc_r _cache['qe_r'] = qe_r _cache['fr_r'] = fr_r _cache['dgs10'] = dgs10 return results def figures(): """Generate monetary policy degradation figures.""" if not _cache: main() paths = [] dgs10 = _cache.get('dgs10') fomc_r = _cache.get('fomc_r') qe_r = _cache.get('qe_r') fr_r = _cache.get('fr_r') if dgs10 is None: return paths # Figure 1: FOMC-day volatility ratio over time daily_chg = dgs10.diff().dropna().abs() fomc_dates = pd.to_datetime(FOMC_DATES_STR) ratios = [] years = [] for center_year in range(2003, 2024): start = pd.Timestamp(f'{center_year - 1}-01-01') end = pd.Timestamp(f'{center_year + 1}-12-31') window = daily_chg[(daily_chg.index >= start) & (daily_chg.index <= end)] fomc_in_window = fomc_dates[(fomc_dates >= start) & (fomc_dates <= end)] fomc_chg = [] for fd in fomc_in_window: for offset in [0, 1, -1]: target = fd + pd.Timedelta(days=offset) if target in window.index: fomc_chg.append(window.loc[target]) break non_fomc = window[~window.index.isin(fomc_dates)] if len(fomc_chg) >= 3 and len(non_fomc) >= 50: ratios.append(np.mean(fomc_chg) / non_fomc.mean()) years.append(center_year) if len(ratios) >= 5: fig, axes = setup_subplots(1, 2, width=14, height=6) # Panel A: FOMC importance ratio ax = axes[0] ax.plot(years, ratios, 'o-', color='steelblue', linewidth=2, markersize=6) from scipy import stats as sp_stats slope, intercept, _, _, _ = sp_stats.linregress(years, ratios) ax.plot(years, intercept + slope * np.array(years), 'k--', linewidth=1.5, label=f'Trend (slope={slope:.3f})') ax.axhline(y=1.0, color='gray', linestyle=':', alpha=0.5) ax.set_xlabel('Year') ax.set_ylabel('FOMC-day / Non-FOMC Vol Ratio') ax.set_title('(a) Forward Guidance Persistence') ax.legend() # Panel B: Degradation sequence summary ax = axes[1] labels = ['Forward\nGuidance', 'QE\nEffectiveness', 'Financial\nRepression'] verdicts = [fomc_r.get('verdict', 'N/A') if fomc_r else 'N/A', qe_r.get('verdict', 'N/A') if qe_r else 'N/A', fr_r.get('verdict', 'N/A') if fr_r else 'N/A'] colors = ['#d62728' if v == 'CONSISTENT' else '#ff7f0e' if v == 'AMBIGUOUS' else '#2ca02c' for v in verdicts] ax.barh(labels, [1, 1, 1], color=colors, edgecolor='black', alpha=0.7) for i, v in enumerate(verdicts): ax.text(0.5, i, v, ha='center', va='center', fontweight='bold', fontsize=11) ax.set_xlim(0, 1) ax.set_xlabel('') ax.set_title('(b) Channel Status at ϕ≈0') ax.set_xticks([]) paths += save_figure(fig, 'mp_degradation', subfolder='applications') # Figure: Financial repression — real yield vs savings rate scatter fr_df = _cache.get('fr_df') if fr_df is not None and len(fr_df) > 60: fig, axes = setup_subplots(1, 2, width=14, height=5) # Panel A: scatter ax = axes[0] ax.scatter(fr_df['real_yield'], fr_df['savings_rate'], alpha=0.3, c='steelblue', s=15, edgecolors='none') z = np.polyfit(fr_df['real_yield'], fr_df['savings_rate'], 1) x_line = np.linspace(fr_df['real_yield'].min(), fr_df['real_yield'].max(), 50) ax.plot(x_line, np.polyval(z, x_line), 'r-', linewidth=2, alpha=0.7) corr_val = fr_r.get('statistic', 0) if fr_r else 0 p_val = fr_r.get('p_value', 1) if fr_r else 1 ax.set_xlabel('Real 10Y Yield (DGS10 - 12m CPI, %)', fontsize=10) ax.set_ylabel('Personal Savings Rate (%)', fontsize=10) ax.set_title(f'Financial Repression: r={corr_val:.3f}, p={p_val:.1e}', fontsize=11) ax.grid(True, alpha=0.3) # Panel B: time series ax = axes[1] ax.plot(fr_df.index, fr_df['real_yield'], color='steelblue', alpha=0.7, label='Real 10Y yield') ax2 = ax.twinx() ax2.plot(fr_df.index, fr_df['savings_rate'], color='coral', alpha=0.7, label='Savings rate') ax.set_xlabel('Date', fontsize=10) ax.set_ylabel('Real yield (%)', fontsize=10, color='steelblue') ax2.set_ylabel('Savings rate (%)', fontsize=10, color='coral') ax.set_title('Real Yield and Savings Rate Over Time', fontsize=11) lines1, labels1 = ax.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax.legend(lines1 + lines2, labels1 + labels2, fontsize=8, loc='upper left') fig.tight_layout() paths += save_figure(fig, 'mp_degradation_financial_repression', subfolder='applications') return paths if __name__ == '__main__': results = main() for r in results: print(f"{r['name']}: {r['verdict']} " f"(stat={r['statistic']}, p={r['p_value']})")

p value	0.7147
n windows	21
statistic	0.0952

MP Degradation Fomc Importance

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