Structural Breaks in Grouped Heterogeneity

Simon C. Smith

Journal of Business & Economic Statistics, 2023, vol. 41, issue 3, 752-764

Abstract: Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved regime-specific grouped heterogeneity. In an empirical application to forecasting inflation rates across 20 U.S. industries, our method generates significantly more accurate forecasts relative to a range of popular methods.

Date: 2023
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DOI: 10.1080/07350015.2022.2063132

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