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

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  • ADAM CHECK
  • JEREMY PIGER

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.

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  • Adam Check & Jeremy Piger, 2021. "Structural Breaks in U.S. Macroeconomic Time Series: A Bayesian Model Averaging Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(8), pages 1999-2036, December.
    • Handle: RePEc:wly:jmoncb:v:53:y:2021:i:8:p:1999-2036
    DOI: 10.1111/jmcb.12822
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    3. Donayre, Luiggi & Panovska, Irina, 2021. "Recession-specific recoveries: L’s, U’s and everything in between," Economics Letters, Elsevier, vol. 209(C).
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