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Title: Copula-Based Stochastic Frontier Quantile Model with Unknown Quantile
Authors: Paravee Maneejuk
Woraphon Yamaka
Authors: Paravee Maneejuk
Woraphon Yamaka
Keywords: Computer Science
Issue Date: 1-Jan-2021
Abstract: © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. This study aims to improve the copula-based stochastic frontier quantile model by treating the quantile as the unknown parameter. This method can solve the problem of quantile selection bias as the quantile will be estimated simultaneously with other parameters in the model. We then evaluate the performance and accuracy of the proposed model by conducting two simulation studies and a real data analysis with two different data sets. The overall results reveal that our proposed model can beat the conventional stochastic frontier model and also the copula-based stochastic frontier model with a given quantile.
ISSN: 18609503
Appears in Collections:CMUL: Journal Articles

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