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GMM Estimation of Short Dynamic Panel Data Models With Error Cross-Sectional Dependence

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  • Sarafidis, Vasilis

Abstract

This paper considers the issue of GMM estimation of a short dynamic panel data model when the errors are correlated across individuals. We focus particularly on the conditions required in the cross-sectional dimension of the error process for the dynamic panel GMM estimator to remain consistent. To this end, we demonstrate that cross-sectional independence (or uncorrelatedness) is not necessary - rather, it suffices that, if there is such correlation in the errors, this is weak. We define a stochastic scalar sequence to be cross-sectionally weakly correlated at any given point in time if the sequence of the covariances of the observations across individuals i and j at time t, given the conditioning set of all time-invariant characteristics of individuals i and j, converges absolutely as N grows large. Spatial dependence satisfies this condition but factor structure dependence does not. Consequently, the dynamic panel GMM estimator is consistent only in the first case. Under cross-sectionally weakly correlated errors, an additional, non-redundant, set of moment conditions becomes relevant for each i - specifically, instruments with respect to the individual(s) which unit i is correlated with. We demonstrate that these moment conditions remain valid when the errors are subject to both weak and strong correlations, in which situation the standard moment conditions with respect to individual i itself are invalidated - meaning that the dynamic panel GMM estimator is inconsistent. Simulated experiments show that the resulting method of moments estimators largely outperform the conventional ones in terms of both median bias and root median square error.

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  • Sarafidis, Vasilis, 2009. "GMM Estimation of Short Dynamic Panel Data Models With Error Cross-Sectional Dependence," MPRA Paper 25176, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:25176
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    References listed on IDEAS

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    Cited by:

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    2. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    3. Li, Jiaman & Dong, Kangyin & Dong, Xiucheng, 2022. "Green energy as a new determinant of green growth in China: The role of green technological innovation," Energy Economics, Elsevier, vol. 114(C).
    4. 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.
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    6. Shingal, ANIRUDH, 2010. "Services growth and convergence: Getting India’s states together," MPRA Paper 32813, University Library of Munich, Germany.
    7. Bin Peng & Giovanni Forchini, 2012. "Consistent Estimation of Panel Data Models with a Multi-factor Error Structure," School of Economics Discussion Papers 0112, School of Economics, University of Surrey.
    8. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    9. Bin Peng & Giovanni Forchini, 2014. "Consistent Estimation of Panel Data Models with a Multifactor Error Structure when the Cross Section Dimension is Large," Working Paper Series 20, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    10. Yi, Jiahui & Dai, Sheng & Li, Lin & Cheng, Jinhua, 2024. "How does digital economy development affect renewable energy innovation?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Busra Agan & Mehmet Balcilar, 2022. "On the Determinants of Green Technology Diffusion: An Empirical Analysis of Economic, Social, Political, and Environmental Factors," Sustainability, MDPI, vol. 14(4), pages 1-23, February.
    12. Hela Borgi & Fatma Mabrouk & Jihen Bousrih & Mohamed Mehdi Mekni, 2023. "Environmental Change and Inclusive Finance: Does Governance Quality Matter for African Countries?," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    13. Gilhooly, Robert & Weale, Martin & Wieladek, Tomasz, 2015. "Estimation of short dynamic panels in the presence of cross-sectional dependence and dynamic eterogeneity," Discussion Papers 38, Monetary Policy Committee Unit, Bank of England.
    14. Li, Jiaman & Dong, Xiucheng & Dong, Kangyin, 2022. "How much does financial inclusion contribute to renewable energy growth? Ways to realize green finance in China," Renewable Energy, Elsevier, vol. 198(C), pages 760-771.
    15. Ogbeide-Osaretin, Evelyn Nwamaka & Igbafe Aliu, Timothy, 2022. "Can A Deep Preferential Trade Agreement Boost Global Value Chain Participation In Sub-Saharan Africa?," Ilorin Journal of Economic Policy, Department of Economics, University of Ilorin, vol. 9(1), pages 45-56, June.

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    More about this item

    Keywords

    Dynamic panel data; spatial dependence; factor structure dependence; Generalised Method of Moments;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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