Maximum likelihood estimation of stochastic frontier models with endogeneity

Samuele Centorrino and María Pérez-Urdiales

Journal of Econometrics, 2023, vol. 234, issue 1, 82-105

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 present some of its asymptotic properties, and we showcase its finite sample behavior in Monte-Carlo simulations and an empirical application to farmers in Nepal.

Keywords: Stochastic frontier; Endogeneity; Control functions; Maximum likelihood; Technical efficiency (search for similar items in EconPapers)
JEL-codes: C10 C13 C26 C36 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity (2021)
Working Paper: Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:234:y:2023:i:1:p:82-105

DOI: 10.1016/j.jeconom.2021.09.019

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