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Estimation and inference in two-stage, semi-parametric models of production processes. (English) Zbl 1418.62535

Summary: Many papers have regressed non-parametric estimates of productive efficiency on environmental variables in two-stage procedures to account for exogenous factors that might affect firms’ performance. None of these have described a coherent data-generating process (DGP). Moreover, conventional approaches to inference employed in these papers are invalid due to complicated, unknown serial correlation among the estimated efficiencies. We first describe a sensible DGP for such models. We propose single and double bootstrap procedures; both permit valid inference, and the double bootstrap procedure improves statistical efficiency in the second-stage regression. We examine the statistical performance of our estimators using Monte Carlo experiments.

MSC:

62P20 Applications of statistics to economics
91B38 Production theory, theory of the firm
90B50 Management decision making, including multiple objectives

Software:

bootstrap; FRONTIER
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