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Count Data Models with Unobserved Heterogeneity: An Empirical Likelihood Approach Author info | Abstract | Publisher info | Download info | Related research | Statistics Stefan Boes () (Socioeconomic Institute, University of Zurich)
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As previously argued, the correlation between included and omitted regressors generally causes inconsistency of standard estimators for count data models. Using a specific residual function and suitable instruments, a consistent generalized method of moments estimator can be obtained under conditional moment restrictions. This approach is extended here by fully exploiting the model assumptions and thereby improving efficiency of the resulting estimator. Empirical likelihood estimation in particular has favorable properties in this setting compared to the two-step GMM procedure, which is demonstrated in a Monte Carlo experiment. The proposed method is applied to the estimation of a cigarette demand function.
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Paper provided by University of Zurich, Socioeconomic Institute in its series Working Papers with number
0704.
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Length: 26 pages
Date of creation: Mar 2007Date of revision:
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Keywords: nonparametric likelihood poisson model nonlinear instrumental variables optimal instruments approximating functions semiparametric efficiency 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 D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
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