Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity
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arXiv
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| Format: | Preprint |
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2020
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| author | Centorrino, Samuele Pérez-Urdiales, María |
| author_facet | Centorrino, Samuele Pérez-Urdiales, María |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2004_12369 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity Centorrino, Samuele Pérez-Urdiales, María Econometrics Applications 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. |
| title | Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity |
| topic | Econometrics Applications |
| url | https://arxiv.org/abs/2004.12369 |