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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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    1. Maurice J.G. Bun & Frank Kleibergen, 2013. "Identification and inference in moments based analysis of linear dynamic panel data models," UvA-Econometrics Working Papers 13-07, Universiteit van Amsterdam, Dept. of Econometrics.
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    5. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    6. Giorgio Calzolari & Laura Magazzini, 2013. "A powerful test of mean stationarity in dynamic models for panel data: Monte Carlo evidence," Working Papers 14/2013, University of Verona, Department of Economics.
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    Cited by:

    1. Gørgens, Tue & Han, Chirok & Xue, Sen, 2020. "On the asymptotic distribution of the quadratic GMM estimator of a dynamic panel data model under a unit root," Economics Letters, Elsevier, vol. 197(C).
    2. Pua, Andrew Adrian Yu & Fritsch, Markus & Schnurbus, Joachim, 2019. "Practical aspects of using quadratic moment conditions in linear dynamic panel data models," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-38-19, University of Passau, Faculty of Business and Economics.
    3. Fritsch, Markus, 2019. "On GMM estimation of linear dynamic panel data models," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-36-19, University of Passau, Faculty of Business and Economics.
    4. Fritsch, Markus & Pua, Andrew Adrian Yu & Schnurbus, Joachim, 2019. "Pdynmc - An R-package for estimating linear dynamic panel data models based on linear and nonlinear moment conditions," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-39-19, University of Passau, Faculty of Business and Economics.
    5. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    6. Pua, Andrew Adrian Yu & Fritsch, Markus & Schnurbus, Joachim, 2019. "Large sample properties of an IV estimator based on the Ahn and Schmidt moment conditions," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-37-19, University of Passau, Faculty of Business and Economics.
    7. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.

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    More about this item

    Keywords

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

    JEL classification:

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

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