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

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  • Shaik, Saleem
  • Tokovenko, Oleksiy

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.

Suggested Citation

  • Shaik, Saleem & Tokovenko, Oleksiy, 2013. "Evaluating the Importance of Multiple Imputations of Missing Data on Stochastic Frontier Analysis Efficiency Measures," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150792, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea13:150792
    DOI: 10.22004/ag.econ.150792
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    1. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    2. George E. Battese & Greg S. Corra, 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 169-179, December.
    3. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Agricultural and Food Policy; Research Methods/ Statistical Methods;

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