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Structural Breaks in Grouped Heterogeneity

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  • Simon C. Smith

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.

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  • Simon C. Smith, 2023. "Structural Breaks in Grouped Heterogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 752-764, July.
    • Handle: RePEc:taf:jnlbes:v:41:y:2023:i:3:p:752-764
    DOI: 10.1080/07350015.2022.2063132
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