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Moment Restrictions and Identification in Linear Dynamic Panel Data Models

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  • Tue Gørgens
  • Chirok Han
  • Sen Xue

Abstract

This paper investigates the relationship between moment restrictions and identification in simple linear AR(1) dynamic panel data models with fixed effects under standard minimal assumptions. The number of time periods is assumed to be small. The assumptions imply linear and quadratic moment restrictions which can be used for GMM estimation. The paper makes three points. First, contrary to common belief, the linear moment restrictions may fail to identify the autoregressive parameter even when it is known to be less than 1. Second, the quadratic moment restrictions provide full or partial identification in many of the cases where the linear moment restrictions do not. Third, the first moment restrictions can also be important for identification. Practical implications of the findings are illustrated using Monte Carlo simulations.

Suggested Citation

  • Tue Gørgens & Chirok Han & Sen Xue, 2019. "Moment Restrictions and Identification in Linear Dynamic Panel Data Models," Annals of Economics and Statistics, GENES, issue 134, pages 149-176.
  • Handle: RePEc:adr:anecst:y:2019:i:134:p:149-176
    DOI: 10.15609/annaeconstat2009.134.0149
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    File URL: https://www.jstor.org/stable/10.15609/annaeconstat2009.134.0149
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    More about this item

    Keywords

    Dynamic Panel Data Models; Fixed Effects; Identification; Generalized Method of Moments; Arellano-Bond Estimator;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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