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