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The Asymptotic Properties Of The System Gmm Estimator In Dynamic Panel Data Models When Both N And T Are Large

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  • Hayakawa, Kazuhiko

Abstract

In this paper, we derive the asymptotic properties of the system generalized method of moments (GMM) estimator in dynamic panel data models with individual and time effects when both N and T, the dimensions of cross-section and time series, are large. Specifically, we show that the two-step system GMM estimator is consistent when a suboptimal weighting matrix where off-diagonal blocks are set to zero is used. Such consistency results theoretically support the use of the system GMM estimator in large N and T contexts even though it was originally developed for large N and small T panels. Simulation results indicate that the large N and large T asymptotic results approximate the finite sample behavior reasonably well unless persistency of data is strong and/or the variance ratio of individual effects to the disturbances is large.

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  • Hayakawa, Kazuhiko, 2015. "The Asymptotic Properties Of The System Gmm Estimator In Dynamic Panel Data Models When Both N And T Are Large," Econometric Theory, Cambridge University Press, vol. 31(3), pages 647-667, June.
    • Handle: RePEc:cup:etheor:v:31:y:2015:i:03:p:647-667_00
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    Cited by:

    1. Hayakawa, Kazuhiko, 2019. "Alternative over-identifying restriction test in the GMM estimation of panel data models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 71-95.
    2. Jan Janků, 2016. "Podmíněný politicko-rozpočtový cyklus v zemích OECD [Conditional Political Budget Cycle in the OECD Countries]," Politická ekonomie, Prague University of Economics and Business, vol. 2016(1), pages 65-82.
    3. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    4. Trabelsi, Emna & Hichri, Walid, 2021. "Central Bank Transparency with (semi-)public Information: Laboratory Experiments," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    5. Tzu-Ming Liu, 2020. "Habit formation or word of mouth: What does lagged dependent variable in tourism demand models imply?," Tourism Economics, , vol. 26(3), pages 461-474, May.
    6. 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.
    7. U. Michael Bergman & Michael Hutchison, 2020. "Fiscal procyclicality in emerging markets: The role of institutions and economic conditions," International Finance, Wiley Blackwell, vol. 23(2), pages 196-214, August.
    8. Vaclav Broz & Michal Hlavacek, 2018. "What Drives the Distributional Dynamics of Client Interest Rates on Consumer Loans in the Czech Republic? A Bank-level Analysis," Working Papers 2018/6, Czech National Bank.
    9. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    10. Phillips, Peter C.B. & Leirvik, Thomas & Storelvmo, Trude, 2020. "Econometric estimates of Earth’s transient climate sensitivity," Journal of Econometrics, Elsevier, vol. 214(1), pages 6-32.
    11. Martin Stojanovikj & Goran Petrevski, 2021. "Macroeconomic effects of inflation targeting in emerging market economies," Empirical Economics, Springer, vol. 61(5), pages 2539-2585, November.
    12. Odunayo Magret Olarewaju, 2020. "Investigating the factors affecting nonperforming loans in commercial banks: The case of African lower middle‐income countries," African Development Review, African Development Bank, vol. 32(4), pages 744-757, December.
    13. Mehic, Adrian, 2020. "Half-panel jackknife estimation for dynamic panel models," Economics Letters, Elsevier, vol. 190(C).
    14. Norkutė, Milda & Westerlund, Joakim, 2019. "The factor analytical method for interactive effects dynamic panel models with moving average errors," Econometrics and Statistics, Elsevier, vol. 11(C), pages 83-104.
    15. 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.
    16. Qu, Guangjun & Sylwester, Kevin & Wang, Feng, 2018. "Anticorruption and growth: Evidence from China," European Journal of Political Economy, Elsevier, vol. 55(C), pages 373-390.
    17. Peter C. B. Phillips, 2020. "Dynamic Panel Modeling of Climate Change," Econometrics, MDPI, vol. 8(3), pages 1-28, July.
    18. Norkutė, Milda & Westerlund, Joakim, 2021. "The factor analytical approach in near unit root interactive effects panels," Journal of Econometrics, Elsevier, vol. 221(2), pages 569-590.
    19. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.

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