Count data models with unobserved heterogeneity: An empirical likelihood approach

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 0704

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities ; Consumer Economics: Empirical Analysis
• Subject: nonparametric likelihood ; poisson model ; nonlinear instrumental variables ; optimal instruments ; approximating functions ; semiparametric efficiency ; Konsumentenverhalten ; Theorie ; Nichtparametrisches Verfahren ; Monte-Carlo-Methode ; Zähldatenmodell ; Schätzung ; Zigarette

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Boes, Stefan
• Event: Veröffentlichung
• (who): University of Zurich, Socioeconomic Institute
• (where): Zurich
• (when): 2007

Further information

• Handle: http://hdl.handle.net/10419/76239
• Last update: 10.03.2025, 11:43 AM CET