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Backward Mean Transformation in Panel Data with Predetermined Regressors

In: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology

Author

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  • Artūras Juodis

Abstract

This chapter analyzes the properties of an alternative least-squares based estimator for linear panel data models with general predetermined regressors. This approach uses backward means of regressors to approximate individual specific fixed effects (FE). The author analyzes sufficient conditions for this estimator to be asymptotically efficient, and argue that, in comparison with the FE estimator, the use of backward means leads to a non-trivial bias-variance tradeoff. The author complements theoretical analysis with an extensive Monte Carlo study, where the author finds that some of the currently available results for restricted AR(1) model cannot be easily generalized, and should be extrapolated with caution.

Suggested Citation

  • Artūras Juodis, 2022. "Backward Mean Transformation in Panel Data with Predetermined Regressors," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 103-143, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-90532021000043b006
    DOI: 10.1108/S0731-90532021000043B006
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    More about this item

    Keywords

    Dynamic panel data; predetermined regressors; bias correction; backward mean transformation; nonstationarity; autoregression; C13; C23;
    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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