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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.
    2. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    3. 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.
    4. 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.
    5. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    6. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    7. Han, Chirok & Kim, Hyoungjong, 2014. "The role of constant instruments in dynamic panel estimation," Economics Letters, Elsevier, vol. 124(3), pages 500-503.
    8. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    10. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    11. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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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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