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A note on the asymptotic properties of least squares estimation in high dimensional constrained factor models

Author

Listed:
  • Xiang, Jingjie
  • Li, Kunpeng
  • Cui, Guowei

Abstract

Constrained factor models proposed by Tsai and Tsay (2010) have wide potential applications. The existing asymptotic theory of the least squares estimator, however, falls short of asymptotic representations and limiting distributions, which limits the applicabilities. This paper fills this gap by explicitly giving the asymptotic representations and associated limiting distributions. Theoretical analysis indicates that the least square estimates are asymptotically biased. Bias-corrected estimators are therefore proposed. Monte Carlo simulations confirm our theoretical results.

Suggested Citation

  • Xiang, Jingjie & Li, Kunpeng & Cui, Guowei, 2018. "A note on the asymptotic properties of least squares estimation in high dimensional constrained factor models," Economics Letters, Elsevier, vol. 171(C), pages 144-148.
    • Handle: RePEc:eee:ecolet:v:171:y:2018:i:c:p:144-148
    DOI: 10.1016/j.econlet.2018.07.029
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    References listed on IDEAS

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    More about this item

    Keywords

    Constrained factor models; Least squares estimation; Asymptotic distribution; Bias-corrected estimator;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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