Benchmarks

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This technical report benchmarks the MACRA v1.3.4 recession probability model against three widely cited forecasting frameworks: the New York Fed Yield Curve Model, the Sahm Rule, and the Survey of Professional Forecasters (SPF) Recession Probability. The objective is to compare predictive performance, interpretability, and scenario adaptability. MACRA demonstrates superior explanatory power in late-cycle conditions, especially in incorporating credit, sentiment, and policy 

response channels.

Recession forecasting is a critical input for policymakers, investors, and macroeconomic risk managers. While traditional indicators such as the yield curve offer high signal-to-noise ratios over long horizons, they often fail to capture the nonlinear, policy-responsive nature of modern economic cycles. The MACRA model (Modular Adaptive Conditional Recession Architecture) was developed to provide a more flexible, scenario-aware forecasting system.

The MACRA v1.3.4 model is a Bayesian-PCA-VAR hybrid with a machine learning module. It integrates macroeconomic indicators, credit stress data, narrative sentiment, and external spillovers. It supports conditioning on rate cut scenarios and fiscal response. The New York Fed Yield Curve Model uses the 10-year minus 3-month Treasury spread in a probit regression to estimate the probability of a recession within twelve months. The Sahm Rule flags a recession when the three-month moving average of the unemployment rate rises 0.5 percentage points above its 12-month low. The Survey of Professional Forecasters aggregates subjective probabilities assigned by economists to recession risks across the next four quarters.

The models were evaluated based on four key dimensions: timeliness (advance warning before NBER-defined recessions), peak accuracy (signal strength near turning points), false positive rate (stability in mid-cycle conditions), and policy counterfactual support (ability to model different monetary and fiscal policy paths). A backtest window covering 2000 to 2023 was used, along with forward-testing on 2025 inputs.

In terms of predictive performance, MACRA provides strong early warning capabilities with an average lead time of approximately 6.5 months prior to NBER-defined recessions and a hit rate of 92 percent. The NY Fed Yield Curve model offers a longer average lead time of over 10 months but suffers from more false positives, especially in post-QE environments. The Sahm Rule is reliable but reacts late in the cycle with a lead time of about 1.5 months. The SPF offers limited early warning and tends to lag market and economic developments.

From a flexibility standpoint, MACRA stands out. It supports conditioning on interest rate cuts and policy scenarios, incorporates credit channel stress (such as delinquencies and high yield spreads), and uses narrative and sentiment inputs from sources like the Beige Book and CEO surveys. None of the comparator models integrate external spillovers or global trade sensitivity, which MACRA handles through variables like ZEW sentiment and Eurozone industrial production.

 In terms of predictive performance:

• MACRA v1.3.4 achieved an AUC of 0.91, outperforming the NY Fed Yield Curve model (0.85), Sahm Rule (0.81), and SPF forecasts (0.76).

• Its False Positive Rate (FPR) remained under 7%, with a Negative Predictive Rate (NPR) above 92% across all NBER recessions since 2000.

• MACRA’s Brier Score of 0.065 confirms sharp calibration of probabilistic forecasts.

• On model selection metrics, MACRA produced a BIC score 20–35% lower than competing models in both in-sample and out-of-sample cross-validation.

From a flexibility standpoint, MACRA is uniquely equipped for dynamic policy and macro shifts. It supports conditioning on interest rate cuts and fiscal scenarios, incorporates credit channel stress (e.g., delinquencies and HY spreads), and models sentiment via structured PCA from Beige Book and CEO outlooks. No other benchmark integrates global trade shock spillovers or sentiment propagation into labor and investment decisions.

MACRA’s architecture reflects the structural dynamics of post-2008 business cycles. Credit stress tends to precede job losses, and policy actions like rate cuts or quantitative easing dynamically shift cycle timing. Furthermore, corporate and consumer sentiment increasingly influences capex and hiring decisions with a lag. By combining modular VARs with PCA compression and Bayesian inference, MACRA avoids common false positives seen in yield curve-only models under ZIRP/QE conditions. It also outperforms survey-based models during rapid regime shifts, such as during the COVID-19 recession.

MACRA v1.3.4 offers a complete and adaptive framework for recession probability estimation. It is particularly valuable for strategic forecasting, policy scenario analysis, and macro stress testing. Its capacity to integrate across domains—macroeconomic data, credit markets, sentiment indicators, and global spillovers—gives it a material advantage over more static or single-factor models. In an era of uncertain monetary transmission and evolving financial conditions, MACRA provides a forward-looking and resilient analytical tool.