Document de travail

Measuring business cycles using vars

Patrick Fève et Alban Moura

Résumé

We propose to measure business cycles using vector autoregressions (VARs). Our method builds on two insights: VARs automatically decompose the data into stable and unstable components, and variance-based shock identfication can extract meaningful cycles from the stable part. This method has appealing properties: (1) it isolates a well-defined component associated with typical fluctuations; (2) it ensures stationarity by construction; (3) it targets movements at business-cycle frequencies; and (4) it is backward-looking, ensuring that cycles at each date only depend on current and past shocks. Since most existing filters lack one or more of these features, our method offers a valuable alternative. In an empirical application, we show that the two shocks with the largest cyclical impact effectively capture postwar U.S. business cycles and we find a tighter link between real activity and inflation than previously recognized. We compare our method with standard alternatives and document the plausibility and robustness of our results.

Mots-clés

business cycles; detrending; filtering; shocks; vector autoregressions;

Codes JEL

  • C32: Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes
  • E32: Business Fluctuations • Cycles

Référence

Patrick Fève et Alban Moura, « Measuring business cycles using vars », TSE Working Paper, n° 25-1673, octobre 2025.

Publié dans

TSE Working Paper, n° 25-1673, octobre 2025