Estimation and inference in two-stage, semi-parametric models of production processes
(2003) , 36 pages
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- Authors
- Abstract
- A plethora of papers have used multi-stage estimation procedures where nonparametric estimates of productive efficiency are obtained in the first stage and then regressed on environmental variables in a subsequent stage in attempts to account for exogenous factors that might affect firms’ performance. None of these papers 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 DGP wherein firms’ efficiencies are influenced by environmental variables. We then propose a single and a double bootstrap procedure; 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.
- Affiliations
University of Texas Department of Economics
Citations
Simar, L., & Wilson, P. (2003). Estimation and inference in two-stage, semi-parametric models of production processes (STAT Discussion Papers 0307). https://hdl.handle.net/2078.5/34412