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An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels

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  • Alexander Chudik
  • M. Hashem Pesaran

Abstract

This paper introduces the idea of self-instrumenting endogenous regressors in settings when the correlation between these regressors and the errors can be derived and used to bias-correct the moment conditions. The resulting bias-corrected moment conditions are less likely to be subject to the weak instrument problem and can be used on their own or in conjunction with other available moment conditions to obtain more efficient estimators. This approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. This paper focuses on the latter, and proposes a new estimator for short T dynamic panels by augmenting Anderson and Hsiao (AAH) estimator with bias-corrected quadratic moment conditions in first differences which substantially improve the small sample performance of the AH estimator without sacrificing on the generality of its underlying assumptions regarding the fixed effects, initial values and heteroskedasticity of error terms. Using Monte Carlo experiments it is shown that AAH estimator represents a substantial improvement over the AH estimator and more importantly it performs well even when compared to Arellano and Bond and Blundell and Bond (BB) estimators that are based on more restrictive assumptions, and continues to have satisfactory performance in cases where the standard GMM estimators are inconsistent. Finally, to decide between AAH and BB estimators we also propose a Hausman type test which is shown to work well when T is small and n sufficiently large.

Suggested Citation

  • Alexander Chudik & M. Hashem Pesaran, 2017. "An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels," Globalization Institute Working Papers 327, Federal Reserve Bank of Dallas, revised 27 Mar 2021.
  • Handle: RePEc:fip:feddgw:327
    DOI: 10.24149/gwp327r2
    Note: A previous version of this paper was circulated under the title "A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels," Federal Reserve Bank of Dallas, Globalization and Monetary Policy Working Paper No. 327, https://doi.org/10.24149/gwp327
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    References listed on IDEAS

    as
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    Cited by:

    1. Pesaran, M. Hashem & Yang, Cynthia Fan, 2021. "Estimation and inference in spatial models with dominant units," Journal of Econometrics, Elsevier, vol. 221(2), pages 591-615.

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    More about this item

    Keywords

    Short-T Dynamic Panels; GMM; Bias-Corrected Moment Conditions; BMM; Self-Instrumenting; Nonlinear Moment Conditions; Panel VARs; Hausman Test; Monte Carlo Evidence;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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