Estimation and inference in two-stage, semi-parametric models of production processes

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 approac...

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Bibliographic Details
Published in:Journal of econometrics 2007, Vol.136 (1), p.31-64
Main Authors: Simar, Léopold, Wilson, Paul W.
Format: Article
Language:English
Subjects:
Applications
Applied sciences
Bootstrap
Bootstrap mechanism
Bootstrap method
Data analysis
Data envelopment analysis
DEA
Decision theory
Econometrics
Environment
Estimation
Exact sciences and technology
Insurance, economics, finance
Mathematical programming
Mathematics
Monte Carlo simulation
Nonparametric
Nonparametric inference
Operational research and scientific management
Operational research. Management science
Parameter estimation
Probability and statistics
Production functions
Sciences and techniques of general use
Statistics
Studies
Technical efficiency
Two-stage estimation
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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.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2005.07.009