2021/05/28 by ADAM CHECK, Adam Check, JEREMY PIGER +1 · 16 citations
Economics, Econometrics and Finance · Mathematics · #Artificial intelligence #Autoregressive model #Bayesian inference #Bayesian probability #Computer science #Econometrics #Economics #Financial Risk and Volatility Modeling #Inference #Inflation (cosmology) #Market Dynamics and Volatility #Mathematics #Model selection #Monetary Policy and Economic Impact #Series (stratigraphy) #Statistics #Structural break #Time series #Variance (accounting)
paper · doi:10.1111/jmcb.12822
published in Journal of money credit and banking 53(8), 1999-2036 (Wiley)
openalex publication_date 2021/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Abstract We investigate the evidence for structural breaks in autoregressive models of U.S. macroeconomic time series. There is substantial model uncertainty associated with such models, including uncertainty related to lag selection, the number of structural breaks, and the specific parameters that break. We develop a feasible approach to Bayesian model averaging, where the model space encompasses these sources of uncertainty. We find pervasive evidence for breaks in variance parameters, and for price inflation series, we find strong evidence of changes in persistence. We also find evidence for reductions in trend growth rates of production series. For most series, there is substantial model uncertainty, calling into question the common practice of basing inference on one selected structural break model.