Evaluating the Importance of Multiple Imputations of Missing Data on Stochastic Frontier Analysis Efficiency Measures

Saleem Shaik and Oleksiy Tokovenko

No 150792, 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. from Agricultural and Applied Economics Association

Abstract: 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.

Keywords: Agricultural and Food Policy; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 31
Date: 2013
New Economics Papers: this item is included in nep-agr, nep-ecm and nep-eff
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea13:150792

DOI: 10.22004/ag.econ.150792

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