Alternative Panel Data Estimators for Stochastic Frontier Models

نویسنده

  • William Greene
چکیده

Received analyses based on stochastic frontier modeling with panel data have relied primarily on results from traditional linear fixed and random effects models. This paper examines several extensions of these models that employ nonlinear techniques. The fixed effects model is extended to the stochastic frontier model using results that specifically employ the nonlinear specification. Based on Monte Carlo results, we find that in spite of the well documented incidental parameters problem, the fixed effects estimator appears to be no less effective than traditional approaches in a correctly specified model. We then consider two additional approaches, the random parameters (or ‘multilevel’ or ‘hierarchical’) model and the latent class model. Both of these forms allow generalizations of the model beyond the familiar normal distribution framework.

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تاریخ انتشار 2002