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On the effect of mean-nonstationarity in dynamic panel data models

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  • Hayakawa, Kazuhiko

Abstract

In this paper, we investigate the effect of mean-nonstationarity on the first-difference generalized method of moments (FD-GMM) estimator in dynamic panel data models. We find that when data is mean-nonstationary and the variance of individual effects is significantly larger than that of disturbances, the FD-GMM estimator performs quite well. We demonstrate that this is because the correlation between the lagged dependent variable and instruments gets larger owing to the unremoved individual effects, i.e., instruments become strong. This implies that, under mean-nonstationarity, the FD-GMM estimator does not always suffer from the weak instruments problem even when data is persistent.

Suggested Citation

  • Hayakawa, Kazuhiko, 2009. "On the effect of mean-nonstationarity in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 153(2), pages 133-135, December.
    • Handle: RePEc:eee:econom:v:153:y:2009:i:2:p:133-135
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    3. M. E. Bontempi & I. Mammi, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," Working Papers wp843, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Rosen Azad Chowdhury & Bill Russell, 2018. "The difference, system and ‘Double‐D’ GMM panel estimators in the presence of structural breaks," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(3), pages 271-292, July.
    5. Artūras Juodis, 2018. "Rank based cointegration testing for dynamic panels with fixed T," Empirical Economics, Springer, vol. 55(2), pages 349-389, September.
    6. Aquaro, M., 2013. "Pairwise difference estimation of linear panel data," Other publications TiSEM 2786f9bb-fbe1-4bac-8efc-b, Tilburg University, School of Economics and Management.
    7. 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.
    8. Jan Kiviet & Milan Pleus & Rutger Poldermans, 2017. "Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models," Econometrics, MDPI, vol. 5(1), pages 1-54, March.
    9. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    10. Juodis, Arturas & Sarafidis, Vasilis, 2020. "Online Supplement to An Incidental Parameters Free Inference Approach for Panels with Common Shocks," MPRA Paper 104908, University Library of Munich, Germany.
    11. Sarafidis, Vasilis, 2016. "Neighbourhood GMM estimation of dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 526-544.
    12. Christoph Doerffel & Peter Draper & Andreas Freytag & Sebastian Schuhmann, 2021. "Drivers of Inclusive Development: An Empirical Investigation," Jena Economics Research Papers 2021-015, Friedrich-Schiller-University Jena.
    13. Arturas Juodis, 2013. "First Difference Transformation in Panel VAR models: Robustness, Estimation and Inference," UvA-Econometrics Working Papers 13-06, Universiteit van Amsterdam, Dept. of Econometrics.
    14. Lorde, Troy & Li, Gang & Airey, David, 2014. "Modeling Caribbean Tourism Demand: An Augmented Gravity Approach," MPRA Paper 95476, University Library of Munich, Germany.
    15. Hayakawa, Kazuhiko, 2016. "Improved GMM estimation of panel VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 240-264.
    16. Arnaud Deseau & Adam Levai & Michèle Schmiegelow, 2019. "Access to Justice and Economic Development: Evidence from an International Panel Dataset," LIDAM Discussion Papers IRES 2019009, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    17. Lin, Boqiang & Wang, Yao, 2020. "Analyzing the elasticity and subsidy to reform the residential electricity tariffs in China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 189-206.
    18. 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.
    19. Jan F. Kiviet & Milan Pleus & Rutger Poldermans, 2014. "Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models," UvA-Econometrics Working Papers 14-09, Universiteit van Amsterdam, Dept. of Econometrics.
    20. Khalaf, Lynda & Saunders, Charles J., 2020. "Monte Carlo two-stage indirect inference (2SIF) for autoregressive panels," Journal of Econometrics, Elsevier, vol. 218(2), pages 419-434.
    21. Takuya Hasebe, 2012. "The tests for the level moment conditions: GMM estimation in a linear dynamic panel data model," Economics Bulletin, AccessEcon, vol. 32(1), pages 412-420.
    22. 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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