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Generalized method of moments estimation of linear dynamic panel-data models

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  • Sebastian Kripfganz

    (University of Exeter Business School)

Abstract

In dynamic models with unobserved group-specific effects, the lagged dependent variable is an endogenous regressor by construction. The conventional fixed-effects estimator is biased and inconsistent under fixed-T asymptotics. To deal with this problem, "difference GMM" and "system GMM" estimators in the spirit of Arellano and Bond (1991, Review of Economic Studies), Arellano and Bover (1995, Journal of Econometrics), and Blundell and Bond (1998, Journal of Econometrics) are predominantly applied in practice. While Stata has the official commands xtabond and xtdpdsys—both are wrappers for xtdpd—the Stata community widely associates these methods with the xtabond2 command provided by Roodman (2009, Stata Journal). 10 years after Roodman's award winning Stata Journal article, this presentation revisits the GMM estimation of dynamic panel-data models in Stata. I present the new command, xtdpdgmm, that addresses some shortcomings of xtabond2 and adds further flexibility to the specification of the estimators. In particular, it allows to incorporate the Ahn and Schmidt (1995, Journal of Econometrics) nonlinear moment conditions that can improve the efficiency and robustness of the estimation. Besides the familiar one-step and two-step estimators, xtdpdgmm also provides the Hansen, Heaton, and Yaron (1996, Journal of Business & Economic Statistics) iterated GMM estimator. While it can be pedagogically useful to think about "system GMM" as a system of a level equation and an equation in first differences or forward-orthogonal deviations, I explain that the resulting estimator can still be regarded as a "level GMM" estimator with a set of transformed instruments. These transformed instruments can be obtained as a postestimation feature and used for subsequent specification tests, for example with the ivreg2 command suite of Baum, Schaffer, and Stillman (2003 and 2007, Stata Journal). I further address common pitfalls and frequently asked questions about the estimation of linear dynamic panel-data models.

Suggested Citation

  • Sebastian Kripfganz, 2019. "Generalized method of moments estimation of linear dynamic panel-data models," London Stata Conference 2019 17, Stata Users Group.
  • Handle: RePEc:boc:usug19:17
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    References listed on IDEAS

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    1. Frank Windmeijer, 2018. "Testing Over- and Underidentification in Linear Models, with Applications to Dynamic Panel Data and Asset-Pricing Models," Bristol Economics Discussion Papers 18/696, School of Economics, University of Bristol, UK.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Sanderson, Eleanor & Windmeijer, Frank, 2016. "A weak instrument F-test in linear IV models with multiple endogenous variables," Journal of Econometrics, Elsevier, vol. 190(2), pages 212-221.
    4. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
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    6. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    7. Kiviet, Jan F., 2020. "Microeconometric dynamic panel data methods: Model specification and selection issues," Econometrics and Statistics, Elsevier, vol. 13(C), pages 16-45.
    8. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
    9. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    10. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    11. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Cited by:

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    3. 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.
    4. 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.
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    7. Fritsch, Markus & Pua, Andrew Adrian Yu & Schnurbus, Joachim, 2019. "Revisiting habits and heterogeneity in demands," Passauer Diskussionspapiere, Volkswirtschaftliche Reihe V-78-19, University of Passau, Faculty of Business and Economics.
    8. 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.
    9. Goodell, John W. & Goyal, Abhinav & Hasan, Iftekhar, 2020. "Comparing financial transparency between for-profit and nonprofit suppliers of public goods: Evidence from microfinance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 64(C).
    10. Gupta, C.P. & Bedi, Prateek, 2020. "Corporate cash holdings and promoter ownership," Emerging Markets Review, Elsevier, vol. 44(C).
    11. Ms. Anja Baum & Paulo Medas & Clay Hackney & Mouhamadou Sy, 2019. "Governance and State-Owned Enterprises: How Costly is Corruption?," IMF Working Papers 2019/253, International Monetary Fund.
    12. 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.

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