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
收藏
页码：199 / 209
页数：10
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