FACTOR-DRIVEN TWO-REGIME REGRESSION

Sokbae Lee; Yuan Liao; Myung Hwan Seo; Youngki Shin

doi:10.1214/20-AOS2017

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FACTOR-DRIVEN TWO-REGIME REGRESSION

Sokbae Lee, Yuan Liao, Myung Hwan Seo and Youngki Shin

The Annals of statistics, Vol.49(3), pp.1656-1678

06/01/2021

DOI: 10.1214/20-AOS2017

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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.

Mathematics

Physical Sciences

Science & Technology

Statistics & Probability

Details

Title: Subtitle: FACTOR-DRIVEN TWO-REGIME REGRESSION
Creators: Sokbae Lee - Columbia University
Yuan Liao - Rutgers, The State University of New Jersey
Myung Hwan Seo - Seoul National University
Youngki Shin - McMaster University
Resource Type: Journal article
Publication Details: The Annals of statistics, Vol.49(3), pp.1656-1678
DOI: 10.1214/20-AOS2017
ISSN: 0090-5364
eISSN: 2168-8966
Publisher: INST MATHEMATICAL STATISTICS-IMS
Number of pages: 23
Grant note: SSHRC-435-2018-0275 / Social Sciences and Humanities Research Council of Canada; Social Sciences & Humanities Research Council of Canada (SSHRC) ES/P008909/1 / UK Economic and Social Research Council; UK Research & Innovation (UKRI); Economic & Social Research Council (ESRC) ERC-2014-CoG-646917-ROMIA / European Research Council; European Research Council (ERC) Ministry of Education of the Republic of Korea; Ministry of Education (MOE), Republic of Korea NRF-2018S1A5A2A01033487 / National Research Foundation of Korea
Language: English
Date published: 06/01/2021
Academic Unit: Economics
Record Identifier: 9984936819302771

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