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Identification and inference in moments based analysis of linear dynamic panel data models

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  • Maurice J.G. Bun
  • Frank Kleibergen

Abstract

We show that Dif(ference), see Arellano and Bond (1991), Lev(el), see Arellano and Bover (1995) and Blundell and Bond (1998), or the N(on-)L(inear) moment conditions of Ahn and Schmidt (1995) do not identify the parameters of a first-order autoregressive panel data model when the autoregressive parameter is equal to one. Combinations of the Dif and Lev, resulting in Sys(tem), moment conditions and the Dif and NL, resulting in A(hn-)S(chmidt), moment conditions identify the parameters when there are four or more time periods. The behaviour of one step and two step GMM estimators, however, remains non-standard. We therefore use size correct GMM statistics, like, the GMM-AR, GMM-LM or KLM statistic, to conduct inference. We compare their worst case large sample distributions with the power envelope to determine the optimal statistic. The power envelope involves a quartic root convergence rate which further indicates the non-standard identification issues. The worst case large sample distribution of the KLM statistic coincides with the power envelope whilst the one of the GMM-LM statistic only does so when there are four time periods. It shows that the KLM statistic is efficient both when the autoregressive parameter is one or less than one. The power envelopes for the AS and Sys moment conditons are identical so assuming mean stationarity does not help for identification.

Suggested Citation

  • 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.
    • Handle: RePEc:ame:wpaper:1307
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    References listed on IDEAS

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    Cited by:

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    2. Mehmet Caner & Xu Han & Yoonseok Lee, 2018. "Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 24-46, January.
    3. Piccoli, Luca & Tiezzi, Silvia, 2021. "Rational addiction and time-consistency: An empirical test," Journal of Health Economics, Elsevier, vol. 80(C).
    4. 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.
    5. 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.
    6. Tue Gorgens & Chirok Han & Sen Xue, 2016. "Asymptotic distributions of the quadratic GMM estimator in linear dynamic panel data models," ANU Working Papers in Economics and Econometrics 2016-635, Australian National University, College of Business and Economics, School of Economics.
    7. Owen Davis & Siavash Radpour, 2021. "Dissecting the Pandemic Retirement Surge," SCEPA publication series. 2021-05, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    8. 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.
    9. 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.
    10. Sentana, Enrique, 2024. "Finite underidentification," Journal of Econometrics, Elsevier, vol. 240(1).
    11. Feridoon Koohi-Kamali & Amit Roy, 2021. "Environmental Shocks and Child Labor: A Panel Data Ethiopia & India," SCEPA working paper series. 2021-05, Schwartz Center for Economic Policy Analysis (SCEPA), The New School.
    12. 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.
    13. 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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