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TESTING FOR STRUCTURAL CHANGES IN FACTOR MODELS VIA A NONPARAMETRIC REGRESSION

Published online by Cambridge University Press:  12 May 2020

Liangjun Su  and
Xia Wang
Liangjun Su
Affiliation:
Tsinghua University and Singapore Management University
Xia Wang*
Affiliation:
Renmin University of China and Sun Yat-Sen University
*
Address correspondence to Xia Wang, School of Economics, Renmin University of China, Beijing 100872, China; e-mail: wxia820@163.com.
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Abstract

We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and unknown break dates in the factor loadings. No restrictions on the form of alternatives or trimming of boundary regions near the beginning or end of the sample period is required for the test. The test has power to detect the usual nonparametric rate of local alternatives. Monte Carlo studies demonstrate excellent power of the test in detecting both smooth and sudden structural changes in the factor loadings. In an application using U.S. asset returns, we find significant evidence against time-invariant factor loadings.

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Type
ARTICLES
Information
Econometric Theory , Volume 36 , Issue 6 , December 2020 , pp. 1127 - 1158
Copyright
© Cambridge University Press 2020

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Footnotes

The authors sincerely thank the Editor Peter C.B. Phillips, the Co-Editor Patrik Guggenberger, and three anonymous referees for their constructive comments and suggestions on the early versions of the article. Su gratefully acknowledges the Singapore Ministry of Education for Tier-2 Academic Research Fund (AcRF) under Grant No. MOE2012-T2-2-021 and the funding support provided by the Lee Kong Chian Fund for Excellence. Wang acknowledges financial support from the National Science Foundation of China (No. 71873151, No. 71401160).

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