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Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity

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  • Centorrino, Samuele
  • Perez Urdiales, Maria
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.
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Suggested Citation

  • Centorrino, Samuele & Perez Urdiales, Maria, 2021. "Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity," 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 312072, Agricultural Economics Society - AES.
    • Handle: RePEc:ags:aesc21:312072
    DOI: 10.22004/ag.econ.312072
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    Cited by:

    1. Samuele Centorrino & María Pérez‐Urdiales & Boris Bravo‐Ureta & Alan Wall, 2024. "Binary endogenous treatment in stochastic frontier models with an application to soil conservation in El Salvador," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 365-382, April.
    2. Pérez-Urdiales, María & Libra, Jesse M. & Machado, Kleber B. & Serebrisky, Tomás & Sosa, Ben Solís, 2024. "Household water bill perception in Brazil," Utilities Policy, Elsevier, vol. 87(C).
    3. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    4. Centorrino, Samuele & Parmeter, Christopher F., 2024. "Nonparametric estimation of stochastic frontier models with weak separability," Journal of Econometrics, Elsevier, vol. 238(2).

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    More about this item

    Keywords

    Research Methods/ Statistical Methods; Production Economics;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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