EcoSta 2018: Registration
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Title: A four-component semiparametric stochastic frontier model with endogenous regressors and determinants of inefficiency Authors:  Subal Kumbhakar - State University of New York at Binghamton (United States)
Kai Sun - Shanghai University (China) [presenting]
Abstract: A semiparametric stochastic production frontier model is proposed where the technology parameters are unknown smooth functions of environmental variables, and inputs are allowed to be endogenous. There are four components in the error term of this stochastic frontier model, where two of them are the noise components including the time-invariant and time-varying noises, and the other two of them are the inefficiency components including the time-invariant (i.e., persistent) and time-varying (i.e., transient) inefficiencies. The transient inefficiency is allowed to be a function of the environmental variables as well. We apply the proposed methodology to the Norwegian salmon production data, and analyze the estimated smooth coefficients (i.e., input elasticities), marginal effects of farm age, and persistent, transient, and overall technical efficiency scores.