文章基本信息

标题：Evaluating the Importance of Multiple Imputations of Missing Data on Stochastic Frontier Analysis Efficiency Measures
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作者：Shaik, Saleem ; Tokovenko, Oleksiy
期刊名称：Journal of Agribusiness
印刷版ISSN：0738-8950
出版年度：2013
期号：Suppl
出版社：Journal of Agribusiness
摘要：The robustness of the multiple imputation of missing data on parame- ter coefficients and efficiency measures is evaluated using stochastic frontier analysis in the panel Bayesian context. Second, the implications of multi- ple imputations on stochastic frontier analysis technical efficiency measures under alternative distributional assumptions−half-normal, truncation and exponential is evaluated. Empirical estimates indicate difference in the between-variance and within-variance of parameter coefficients estimated from stochastic frontier analysis and generalized linear models. Within stochastic frontier analysis, the between-variance and within-variance of technical efficiency are different across the three alternative distributional assumptions. Finally, results from this study indicate that even though the between- and within variance of multiple imputed data is close to zero, between- and within-variance of production function parameters, as well as, the technical efficiency measures are different.