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System GMM Estimation With A Small Sample

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  • Marcelo Soto

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Abstract

Properties of GMM estimators for panel data, which have become very popular in the empirical economic growth literature, are not well known when the number of individuals is small. This paper analyses through Monte Carlo simulations the properties of various GMM and other estimators when the number of individuals is the one typically available in country growth studies. It is found that, provided that some persistency is present in the series, the system GMM estimator has a lower bias and higher efficiency than all the other estimators analysed, including the standard first-differences GMM estimator.

Suggested Citation

  • Marcelo Soto, 2009. "System GMM Estimation With A Small Sample," UFAE and IAE Working Papers 780.09, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  • Handle: RePEc:aub:autbar:780.09
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    References listed on IDEAS

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    Cited by:

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    9. Jones, Sam & Tarp, Finn, 2016. "Does foreign aid harm political institutions?," Journal of Development Economics, Elsevier, vol. 118(C), pages 266-281.
    10. da Silva, Patrícia Pereira & Cerqueira, Pedro A., 2017. "Assessing the determinants of household electricity prices in the EU: a system-GMM panel data approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1131-1137.
    11. Kosta Josifidis & Radmila Dragutinović Mitrović & Olgica Ivančev, 2012. "Heterogeneity of Growth in the West Balkans and Emerging Europe: A Dynamic Panel Data Model Approach," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 59(2), pages 157-183, May.
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    19. Karsten Staehr, 2010. "The global financial crisis and public finances in the New EU Countries from Central and Eastern Europe," Bank of Estonia Working Papers wp2010-02, Bank of Estonia, revised 04 Feb 2010.
    20. Faiza A. Khan, 2014. "Economic Convergence in the African Continent: Closing the Gap," South African Journal of Economics, Economic Society of South Africa, vol. 82(3), pages 354-370, September.
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    22. Monica Raileanu Szeles & Rodrigo Mendieta Muñoz, 2016. "Analyzing the Regional Economic Convergence in Ecuador. Insights from Parametric and Nonparametric Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 43-65, June.
    23. Giorgio d'Agostino & Margherita Scarlato, 2015. "Innovation, Socio-institutional Conditions and Economic Growth in the Italian Regions," Regional Studies, Taylor & Francis Journals, vol. 49(9), pages 1514-1534, September.

    More about this item

    Keywords

    Economic Growth; System GMM estimation; Monte Carlo Simulations;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development

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