Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity

Samuele Centorrino and Mar\'ia P\'erez-Urdiales

Papers from arXiv.org

Abstract: We propose and study a maximum likelihood estimator of stochastic frontier models with endogeneity in cross-section data when the composite error term may be correlated with inputs and environmental variables. Our framework is a generalization of the normal half-normal stochastic frontier model with endogeneity. We derive the likelihood function in closed form using three fundamental assumptions: the existence of control functions that fully capture the dependence between regressors and unobservables; the conditional independence of the two error components given the control functions; and the conditional distribution of the stochastic inefficiency term given the control functions being a folded normal distribution. We also provide a Battese-Coelli estimator of technical efficiency. Our estimator is computationally fast and easy to implement. We study some of its asymptotic properties, and we showcase its finite sample behavior in Monte-Carlo simulations and an empirical application to farmers in Nepal.

Date: 2020-04, Revised 2021-03
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
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Citations: View citations in EconPapers (1)

Published in Journal of Econometrics, 2023

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http://arxiv.org/pdf/2004.12369 Latest version (application/pdf)

Related works:
Journal Article: Maximum likelihood estimation of stochastic frontier models with endogeneity (2023)
Working Paper: Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2004.12369

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