New Technical Efficiency Estimates with Improved Bootstrap Confidence Interval Coverage
نویسندگان
چکیده
Bootstrap confidence intervals on fixed-effects efficiency estimates from finite-sample panel data models exhibit low coverage probabilities, because the traditional estimate involves a "max" operator that induces a finite sample bias. Attempts to bootstrap confidence intervals for the traditional estimate have focused on correcting bias. Rather than addressing this bias at the bootstrap stage, we propose alternative efficiency estimates that avoid the "max" operator and yield bootstrap confidence intervals with better coverage probabilities. Additionally, bootstrapped confidence intervals of our estimators are transformation-respecting, while they are not for traditional estimates. A Monte Carlo study and an empirical example are provided that suggest that the new estimators are preferred when the time dimension of the data is small. JEL Classifications: C15, C23, D24
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