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Eigenvalue difference test for the number of common factors in the approximate factor models

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  • Wu, Jianhong

Abstract

This paper proposes a new method for determining the number of common factors in the approximate factor models. Firstly, we construct a nonlinear and monotonous function of eigenvalues such that the function values of the first r largest eigenvalues are close to one and the rest are close to zero when both the number of cross-section units (N) and time series length (T) go to infinity, where r is the real value of the number of common factors. Secondly, we obtain the estimator of the number of common factors by maximizing the difference of function values of two adjacent eigenvalues arranged in descending order. Under some mild conditions, the resulting estimator can be proved to be consistent. Monte Carlo simulation study shows that the new estimator has desired performance.

Suggested Citation

  • Wu, Jianhong, 2018. "Eigenvalue difference test for the number of common factors in the approximate factor models," Economics Letters, Elsevier, vol. 169(C), pages 63-67.
    • Handle: RePEc:eee:ecolet:v:169:y:2018:i:c:p:63-67
    DOI: 10.1016/j.econlet.2018.05.009
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    Cited by:

    1. Wu, Jianhong, 2019. "Detecting irrelevant variables in possible proxies for the latent factors in macroeconomics and finance," Economics Letters, Elsevier, vol. 176(C), pages 60-63.
    2. Shuquan Yang & Nengxiang Ling & Yulin Gong, 2022. "Robust estimation of the number of factors for the pair-elliptical factor models," Computational Statistics, Springer, vol. 37(3), pages 1495-1522, July.
    3. Ruan Weihua & Hou Qian, 2021. "Determining the Number of Factors in Static Approximate Factor Models Using Discrete Fourier Transforms and Pseudo-Eigenvalues," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(1), pages 71-117, February.

    More about this item

    Keywords

    Approximate factor model; Dominate factors; Eigenvalue difference test; Number of common factors;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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