Semiparametric maximum likelihood estimation of stochastic frontier model with errors-in-variables

被引：0

作者：

Byungtae Seo

Seok-Oh Jeong

机构：

[1] Sungkyunkwan University,

[2] Hankuk University of Foreign Studies,undefined

来源：

Journal of the Korean Statistical Society | 2016年 / 45卷

关键词：

primary 62F99; secondary 62P20; Stochastic frontier model; Semiparametric maximum likelihood estimation; Errors-in-variables; Measurement error; Mixture model; Constrained Newton method; Productivity analysis;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper presents a new technique to analyzea stochastic frontier model when covariates are incorporated with measurement errors. We propose a semiparametric mixture likelihood method to estimate the stochastic frontier model which is free from any erroneous specification of the distribution of latent covariates. Some numerical studies including a real data analysis were done, which highly support the proposed approach.

引用

页码：199 / 209

页数：10

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