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Empirical Likelihood in Count Data Models: The Case of Endogenous Regressors


Boes, Stefan (2004). Empirical Likelihood in Count Data Models: The Case of Endogenous Regressors. Working paper series / Socioeconomic Institute No. 404, University of Zurich.

Abstract

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.

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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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Working Paper Series > Socioeconomic Institute (former)
Dewey Decimal Classification:330 Economics
JEL Classification:C14, C25, J13
Language:English
Date:March 2004
Deposited On:29 Nov 2011 22:32
Last Modified:05 Apr 2016 15:11
Series Name:Working paper series / Socioeconomic Institute
Official URL:http://www.econ.uzh.ch/wp.html
Permanent URL: http://doi.org/10.5167/uzh-52190

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