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Local generalized method of moments estimation based on kernel weights: An application to panel data

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  • Rosa Bernardini Papalia

Abstract

This paper presents and applies a local generalized method of moments (LGMM) estimator for regression functions. The method is an extension of previous results obtained by Gozalo and Linton. The LGMM estimation procedure can be applied to estimate a mean regression function and its derivatives at an interior point x , without making explicit assumptions about its functional form. The method has been applied to estimate dynamic models based on panel data.

Suggested Citation

  • Rosa Bernardini Papalia, 1999. "Local generalized method of moments estimation based on kernel weights: An application to panel data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 1005-1015.
  • Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:1005-1015
    DOI: 10.1080/02664769921990
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Pedro Gozalo & Oliver Linton, 1994. "Local Nonlinear Least Squares Estimation: Using Parametric Information Nonparametrically," Cowles Foundation Discussion Papers 1075, Cowles Foundation for Research in Economics, Yale University.
    3. 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.
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    Cited by:

    1. Rosa Bernardini Papalia & Silvia Bertarelli, 2010. "Evaluating Total Factor Productivity Differences by a Mapping Structure in Growth Models," International Regional Science Review, , vol. 33(1), pages 31-59, January.

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