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Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models

Author

Listed:
  • Haruo Iwakura

    (Graduate School of Economics, Kyoto University)

  • Ryo Okui

    (Institute of Economic Research, Kyoto University)

Abstract

This paper studies the asymptotic efficiency in factor models with serially correlated errors and dynamic panel data models with interactive effects. We derive the efficiency bound for the estimation of factors, factor loadings and common parameters that describe the dynamic structure. We use double asymptotics under which both the cross-sectional sample size and the length of the time series tend to in nity. The results show that the efficiency bound for factors is not affected by the presence of unknown factor loadings and common parameters, and analogous results hold for the bounds for factor loadings and common parameters. The efficiency bound is derived by using an in nite-dimensional con- volution theorem. Perturbation to the in nite-dimensional parameters, which consists in an important step of the derivation of the efficiency bound, is nontrivial and is discussed in detail.

Suggested Citation

  • Haruo Iwakura & Ryo Okui, 2014. "Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models," KIER Working Papers 887, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:887
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    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP887.pdf
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    References listed on IDEAS

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    3. Bai, Jushan & Liao, Yuan, 2017. "Inferences in panel data with interactive effects using large covariance matrices," Journal of Econometrics, Elsevier, vol. 200(1), pages 59-78.

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    More about this item

    Keywords

    asymptotic efficiency; convolution theorem; double asymptotics; dynamic panel data model; factor model; interactive effects.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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