Factor-Driven Two-Regime Regression

Sokbae (Simon) Lee, Yuan Liao, Myung Hwan Seo and Youngki Shin

Papers from arXiv.org

Abstract: We propose a novel two-regime regression model where regime switching is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the principal component analysis of a panel data set. We show that the optimization problem can be reformulated as mixed integer optimization, and we present two alternative computational algorithms. We derive the asymptotic distribution of the resulting estimator under the scheme that the threshold effect shrinks to zero. In particular, we establish a phase transition that describes the effect of first-stage factor estimation as the cross-sectional dimension of panel data increases relative to the time-series dimension. Moreover, we develop bootstrap inference and illustrate our methods via numerical studies.

Date: 2018-10, Revised 2020-09
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Citations: View citations in EconPapers (1)

Published in Annals of Statistics, 49(3), 2021, pp. 1656-1678

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http://arxiv.org/pdf/1810.11109 Latest version (application/pdf)

Related works:
Working Paper: Factor-Driven Two-Regime Regression (2019)
Working Paper: Factor-Driven Two-Regime Regression (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1810.11109

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