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A Robust Residual-Based Test for Structural Changes in Factor Models

Author

Listed:
  • Bin Peng
  • Liangjun Su
  • Yayi Yan

Abstract

In this paper, we propose an easy-to-implement residual-based specification testing procedure for detecting structural changes in factor models, which is powerful against both smooth and abrupt structural changes with unknown break dates. The proposed test is robust against the over-specified number of factors, and serially and cross sectionally correlated error processes. A new central limit theorem is given for the quadratic forms of panel data with dependence over both dimensions, thereby filling a gap in the literature. We establish the asymptotic properties of the proposed test statistic, and accordingly develop a simulation-based scheme to select critical value in order to improve finite sample performance. Through extensive simulations and a real-world application, we confirm our theoretical results and demonstrate that the proposed test exhibits desirable size and power in practice.

Suggested Citation

  • Bin Peng & Liangjun Su & Yayi Yan, 2024. "A Robust Residual-Based Test for Structural Changes in Factor Models," Monash Econometrics and Business Statistics Working Papers 10/24, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2024-10
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/2023/wp10-2024.pdf
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    More about this item

    Keywords

    Factor Model; Structural Change; Residual Test; Serial Correlation; Cross Sectional Dependence;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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