Categorical data in local maximum likelihood: theory and applications to productivity analysis

Park, Byeong U.;Simar, Léopold;Zelenyuk, Valentin
(2015) Journal of Productivity Analysis — Vol. 43, n° 2, p. 199-214 (2015)

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Authors
  • Park, Byeong U.Seoul National University, Korea
    Author
  • Simar, LéopoldUCLouvain
    Author
  • Zelenyuk, ValentinUCLouvain
    Author
Abstract
In this paper we consider estimation of models popular in efficiency and productivity analysis (such as the stochastic frontier model, truncated regression model, etc.) via the local maximum likelihood method, generalizing this method here to allow for not only continuous but also discrete regressors. We provide asymptotic theory, some evidence from simulations, and illustrate the method with an empirical example. Our methodology and theory can also be adapted for other models where a likelihood of the unknown functions can be used to identify and estimate the underlying model. Simulation results indicate flexibility of the approach and good performances in various complex scenarios, even with moderate sample sizes.
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Park, B. U., Simar, L., & Zelenyuk, V. (2015). Categorical data in local maximum likelihood: theory and applications to productivity analysis. Journal of Productivity Analysis, 43(2), 199-214. https://doi.org/10.1007/s11123-014-0394-y (Original work published 2015)