Monitoring Structural Breaks in Dynamic Regression Models with Bayesian Sequential Probability Test

Abstract

Structural breaks are pervasive among macroeconomic and financial time series; consequently, forecasts may lose accuracy out of sample, which renders monitoring structural breaks a critical practice. We develop a structural break monitoring scheme, Bayesian Sequential Probability Test (BSPT), for dynamic regression models, which consists of two components: probabilistic detecting statistics, and a sequential stopping procedure. We demonstrate the finite sample property and effectiveness of the BSPT by comparing its performance with that of CUSUM under a variety of DGPs and in a few economic applications.