Structural Breaks in U.S. Macroeconomic Time Series: A Bayesian Model Averaging Approach

Abstract

We investigate the evidence for structural breaks in the parameters of autoregressive models of U.S. post-war macroeconomic time series. There is substantial model uncertainty associated with such models, including uncertainty related to lag selection, the number of structural changes, and the specific parameters that change at each break date. We develop a feasible approach to Bayesian Model Averaging (BMA), where the model space encompasses each of these sources of uncertainty. This BMA procedure performs very well in Monte Carlo simulations calibrated to match relevant macroeconomic time series. We then apply the BMA approach to a cross-section of U.S. macroeconomic variables measuring inflation, production growth, and labor market conditions, finding substantial evidence for structural breaks in all of these series. For most series there are multiple structural breaks detected. We find pervasive evidence for at least one, and often multiple, breaks in conditional variance parameters, and for price inflation series we find strong evidence of changes in persistence. We find little evidence for changes in trend growth rates of production series, or in the natural rate of unemployment. For most series there is substantial uncertainty along one or more dimension of model specification, calling into question the common practice of basing inference on a single selected structural break model.