Noncommon Breaks

Simon C. Smith

Journal of Business & Economic Statistics, 2024, vol. 42, issue 4, 1223-1237

Abstract: We develop a new Bayesian approach to estimate noncommon structural breaks in panel regression models. Any subset of the cross-section may be hit at different times within a break window. Break-specific parameters are learned from the cross-section. They reflect whether (i) breaks hit many or few series and (ii) there is a long or short lag between the first and final series hit by a break. In an empirical application to international stock return predictability, the method generates significantly more accurate forecasts than several benchmarks that yield economically meaningful utility gains for a risk averse investor with power utility.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:42:y:2024:i:4:p:1223-1237

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DOI: 10.1080/07350015.2024.2301969

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