Determining the Number of Factors in Static Approximate Factor Models Using Discrete Fourier Transforms and Pseudo-Eigenvalues

Ruan Weihua () and Hou Qian ()
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Ruan Weihua: Department of Mathematics, Statistics, and Computer Science, Purdue University Northwest, 2200 169th St., Hammond, IN 46323-2094, USA
Hou Qian: School of Mathematics and Science, Shanghai Normal University, Shanghai, Shanghai, China

Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 2021, vol. 241, issue 1, 71-117

Abstract: This paper is concerned with the applications of discrete Fourier transforms in identification of the number of common factors of static approximate factor models. We report and explain the effects of discrete Fourier transforms to matrices, and show how the effects can be used to improve the performance of a number of eigenvalue-based estimators. In addition, we develop a set of pseudo-eigenvalues using cross-sectional discrete Fourier transforms, and use them to develop new estimators. Mathematical proofs of the consistency of the new estimators are provided, and Monte Carlo experiments are conducted to compare the new estimators with some existing ones in the literature.

Keywords: discrete Fourier transforms; common factors; idiosyncratic errors; pseudo-eigenvalues; static approximate factor models (search for similar items in EconPapers)
JEL-codes: C01 C13 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:jns:jbstat:v:241:y:2021:i:1:p:71-117:n:1

DOI: 10.1515/jbnst-2019-0056

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