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Indirect Inference of Stochastic Frontier Models

In: Essays in Honor of Subal Kumbhakar

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  • Hung-pin Lai

Abstract

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic errorvand a one-sided inefficiency random componentu. Whenvoruhas a nonstandard distribution, such asvfollows a generalizedtdistribution oruhas aχ2distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

Suggested Citation

  • Hung-pin Lai, 2024. "Indirect Inference of Stochastic Frontier Models," Advances in Econometrics, in: Essays in Honor of Subal Kumbhakar, volume 46, pages 415-438, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320240000046014
    DOI: 10.1108/S0731-905320240000046014
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    More about this item

    Keywords

    Stochastic frontier; maximum likelihood estimation; indirect inference; maximum simulated likelihood estimation; ordinary least squares; characteristic function; C15; D24;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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