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Inference and estimation in small sample dynamic panel data models

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  • Sebastian Galiani
  • Martin Gonzalez-Rozada

Abstract

We study the finite sample properties of the most important methods of estimation of dynamic panel data models in a special class of small samples: a two-sided small sample (i.e., a sample in which the time dimension is not that short but the cross-section dimension is not that large). This case is encountered increasingly in applied work. Our main results are the following: the estimator proposed by Kiviet (1995) outperforms all other estimators considered in the literature. However, standard statistical inference is not valid for any of them. Thus, to assess the true sample variability of the parameter estimates, bootstrap standard errors have to be computed. We find that standard bootstrapping techniques work well except when the autoregressive parameter is close to one. In this last case, the best available solution is to estimate standard errors by means of the Grid-t bootstrap estimator due to Hansen (1999).

Suggested Citation

  • Sebastian Galiani & Martin Gonzalez-Rozada, 2002. "Inference and estimation in small sample dynamic panel data models," Business School Working Papers treinta, Universidad Torcuato Di Tella.
  • Handle: RePEc:udt:wpbsdt:treinta
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    File URL: http://www.utdt.edu/departamentos/empresarial/cif/pdfs-wp/wpcif-022002.pdf
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    References listed on IDEAS

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    1. David Card & Dean Hyslop, 1997. "Does Inflation "Grease the Wheels of the Labor Market"?," NBER Chapters,in: Reducing Inflation: Motivation and Strategy, pages 71-122 National Bureau of Economic Research, Inc.
    2. Bruce E. Hansen, 1999. "The Grid Bootstrap And The Autoregressive Model," The Review of Economics and Statistics, MIT Press, pages 594-607.
    3. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, pages 218-230.
    4. Bruce E. Hansen, 1999. "The Grid Bootstrap And The Autoregressive Model," The Review of Economics and Statistics, MIT Press, pages 594-607.
    5. Olivier Blanchard & Lawrence F. Katz, 1997. "What We Know and Do Not Know about the Natural Rate of Unemployment," Journal of Economic Perspectives, American Economic Association, pages 51-72.
    6. Olivier Blanchard & Lawrence F. Katz, 1997. "What We Know and Do Not Know about the Natural Rate of Unemployment," Journal of Economic Perspectives, American Economic Association, pages 51-72.
    7. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    8. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, pages 53-78.
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    Cited by:

    1. Raul Alberto Jimenez Mori & Jorge Enrique Mercado Díaz, 2013. "Energy Intensity: A Decomposition and Counterfactual Exercise for Latin American Countries," IDB Publications (Working Papers) 4594, Inter-American Development Bank.
    2. Alessia LO TURCO, 2005. "The Growth Impact of Structural Reforms in Latin America. Another Look," Working Papers 235, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. José Miguel Benavente & Alexander Galetovic & Ricardo Sanhueza & Pablo Serra, 2004. "Estimando la demanda residencial por electricidad en Chile: a doña Juanita le importa el precio (Calculating the residential demand for electricity in Chile: Mrs. Jones does care about the price)," Documentos de Trabajo 192, Centro de Economía Aplicada, Universidad de Chile.
    4. Jimenez, Raul & Mercado, Jorge, 2014. "Energy intensity: A decomposition and counterfactual exercise for Latin American countries," Energy Economics, Elsevier, pages 161-171.

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