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Gmm Estimation And Inference In Dynamic Panel Data Models With Persistent Data

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  • Kruiniger, Hugo

Abstract

In this paper we consider generalized method of moments–based (GMM-based) estimation and inference for the panel AR(1) model when the data are persistent and the time dimension of the panel is fixed. We find that the nature of the weak instruments problem of the Arellano–Bond (Arellano and Bond, 1991, Review of Economic Studies 58, 277–297) estimator depends on the distributional properties of the initial observations. Subsequently, we derive local asymptotic approximations to the finite-sample distributions of the Arellano–Bond estimator and the System estimator, respectively, under a variety of distributional assumptions about the initial observations and discuss the implications of the results we obtain for doing inference. We also propose two Lagrange multiplier–type (LM-type) panel unit root tests.

Suggested Citation

  • Kruiniger, Hugo, 2009. "Gmm Estimation And Inference In Dynamic Panel Data Models With Persistent Data," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1348-1391, October.
    • Handle: RePEc:cup:etheor:v:25:y:2009:i:05:p:1348-1391_09
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    Cited by:

    1. In Choi, 2014. "Unit root tests for dependent and heterogeneous micropanels," Working Papers 1404, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    2. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    3. Kruiniger, Hugo, 2018. "A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions," MPRA Paper 110375, University Library of Munich, Germany, revised 15 Aug 2021.
    4. Peter C.B. Phillips, 2014. "Dynamic Panel GMM with Near Unity," Cowles Foundation Discussion Papers 1962, Cowles Foundation for Research in Economics, Yale University.
    5. Ferdi Celikay, 2020. "Dimensions of tax burden: a review on OECD countries," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, vol. 25(49), pages 27-43, March.
    6. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    7. Hugo Kruiniger, 2025. "Uniform Quasi ML based inference for the panel AR(1) model," Papers 2508.20855, arXiv.org, revised Dec 2025.
    8. Ioana Octavia Popescu, 2023. "Fallacy of floating? Reconsidering the ability of flexible exchange rates to offset terms-of-trade volatility in developing countries," CSAE Working Paper Series 2023-01, Centre for the Study of African Economies, University of Oxford.
    9. 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.
    10. Peter C. B. Phillips, 2020. "Dynamic Panel Modeling of Climate Change," Econometrics, MDPI, vol. 8(3), pages 1-28, July.
    11. Hugo Kruiniger, 2025. "A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions," Papers 2508.20753, arXiv.org, revised Jan 2026.
    12. Daniel Avdic & Martin Karlsson, 2017. "Growth in Earnings and Health: Nothing is as Practical as a Good Theory," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 777-787, December.
    13. Joakim Westerlund & Jörg Breitung, 2013. "Lessons from a Decade of IPS and LLC," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 547-591, August.
    14. Robertson, Donald & Sarafidis, Vasilis & Westerlund, Joakim, 2014. "GMM Unit Root Inference in Generally Trending and Cross-Correlated Dynamic Panels," MPRA Paper 53419, University Library of Munich, Germany.
    15. Hayakawa, Kazuhiko & Nagata, Shuichi, 2016. "On the behaviour of the GMM estimator in persistent dynamic panel data models with unrestricted initial conditions," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 265-303.
    16. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    17. Natalya Ketenci, 2015. "Capital mobility in the panel GMM framework: Evidence from EU members," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 12(1), pages 3-19, July.
    18. Sarafidis, Vasilis, 2016. "Neighbourhood GMM estimation of dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 526-544.
    19. Kruiniger, Hugo, 2009. "Gmm Estimation And Inference In Dynamic Panel Data Models With Persistent Data," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1348-1391, October.
    20. 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.
    21. Bun, Maurice J.G. & Kleibergen, Frank, 2022. "Identification Robust Inference For Moments-Based Analysis Of Linear Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 38(4), pages 689-751, August.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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