Journal of Productivity Analysis

, Volume 43, Issue 2, pp 199–214 | Cite as

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

  • Byeong U. Park
  • Léopold Simar
  • Valentin Zelenyuk


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.


Stochastic frontier models Truncated regression Local maximum likelihood Nonparametric smoothing Categorical variables 

JEL Classification

C13 C14 C2 



All authors acknowledge the financial support from ARC Discovery Grant DP130101022 and the CEPA of School of Economics of The University of Queensland (Australia), from the “Interuniversity Attraction Pole”, Phase VII (No. P7/06) of the Belgian Science Policy (Belgium), from the INRA-GREMAQ, Toulouse (France), from the NRF Grant funded by the Korean government (MEST), No. 20100017437, (listed here in alphabetical order). The authors also thank their colleagues and audiences of many conferences and seminars where this work has been presented. Only the authors and not the above mentioned institutions or people remain responsible for the views expressed.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Byeong U. Park
    • 1
  • Léopold Simar
    • 2
  • Valentin Zelenyuk
    • 3
  1. 1.Department of StatisticsSeoul National UniversitySeoulKorea
  2. 2.Institut de Statistique, Biostatistique et Sciences ActuariellesUniversité Catholique de LouvainLouvain-la-NeuveBelgium
  3. 3.School of Economics and Centre for Efficiency and Productivity AnalysisUniversity of QueenslandBrisbaneAustralia

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