Count Data Models with Unobserved Heterogeneity: An Empirical Likelihood Approach

Stefan Boes

No 704, SOI - Working Papers from Socioeconomic Institute - University of Zurich

Abstract: 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.

Keywords: nonparametric likelihood; poisson model; nonlinear instrumental variables; optimal instruments; approximating functions; semiparametric efficiency (search for similar items in EconPapers)
JEL-codes: C14 C25 D12 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2007-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (16)

Published in Scandinavian Journal of Statistics 37(3), pp. 382-402, 2010

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Persistent link: https://EconPapers.repec.org/RePEc:soz:wpaper:0704

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