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Centered-Residuals-Based Moment Estimator and Test for Stochastic Frontier Models

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  • Yi-Ting Chen
  • Hung-Jen Wang

Abstract

The composed error of a stochastic frontier (SF) model consists of two random variables, and the identification of the model relies heavily on the distribution assumptions for each of these variables. While the literature has put much effort into applying various SF models to a wide range of empirical problems, little has been done to test the distribution assumptions of these two variables. In this article, by exploiting the specification structures of the SF model, we propose a centered-residuals-based method of moments which can be easily and flexibly applied to testing the distribution assumptions on both of the random variables and to estimating the model parameters. A Monte Carlo simulation is conducted to assess the performance of the proposed method. We also provide two empirical examples to demonstrate the use of the proposed estimator and test using real data.

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  • Yi-Ting Chen & Hung-Jen Wang, 2012. "Centered-Residuals-Based Moment Estimator and Test for Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 625-653, November.
    • Handle: RePEc:taf:emetrv:v:31:y:2012:i:6:p:625-653
    DOI: 10.1080/07474938.2011.608037
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    2. William C. Horrace & Yulong Wang, 2022. "Nonparametric tests of tail behavior in stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 537-562, April.
    3. Simos G. Meintanis & Christos K. Papadimitriou, 2022. "Goodness--of--fit tests for stochastic frontier models based on the characteristic function," Journal of Productivity Analysis, Springer, vol. 57(3), pages 285-296, June.

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