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Empirical Likelihood in Count Data Models: The Case of Endogenous Regressors Author info | Abstract | Publisher info | Download info | Related research | Statistics Stefan Boes () (Socioeconomic Institute, University of Zurich)
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Recent advances in the econometric modelling of count data have often been based on the generalized method of moments (GMM). However, the two-step GMM procedure may perform poorly in small samples, and several empirical likelihood-based estimators have been suggested alternatively. In this paper I discuss empirical likelihood (EL) estimation for count data models with endogenous regressors. I carefully distinguish between parametric and semi-parametric methods and analyze the properties of the EL estimator by means of a Monte Carlo experiment. I apply the proposed method to estimate the effect of women’s schooling on fertility.
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Paper provided by University of Zurich, Socioeconomic Institute in its series Working Papers with number
0404.
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Length: 22 pages
Date of creation: Mar 2004Date of revision:
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Keywords: Nonparametric likelihood ; Poisson model ; endogeneity ; fertility and education ; Other versions of this item:
Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
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