Testing whether two-stage estimation is meaningful in non-parametric models of production
(2010) , 40 pages
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- Authors
- Abstract
- Simar and Wilson (J. Econometrics, 2007) provided a statistical model that can rationalize two-stage estimation of technical efficiency in non-parametric settings. Two-stage estimation has been widely used, but requires a strong assumption: the second-stage environmental variables cannot affect the support of the input and output variables in the first stage. In this paper, we provide a fully non-parametric test of this assumption; in addition, we provide a theoretical link to results obtained by Politis et al.(Statistica Sinica, 2001), allowing us to estimate critical values for our test statistics using bootstrap sub-sampling while optimizing the choice of sub-sample size by minimizing a measure of volatility. Our simulation results indicate that our tests perform well both in terms of size and power. We present a real-world empirical example by updating the analysis performed by Aly et al. (R. E. Stat., 1990) on U.S. commercial banks; our tests easily reject the assumption required for two-stage estimation, calling into question results that appear in hundreds of papers that have been published in recent years.
- Affiliations
University of Bologna Dipartimento di Scienze Aziendali Clemson University The John E.Walker Department of Economics
Citations
Daraio, C., Simar, L., & Wilson, P. (2010). Testing whether two-stage estimation is meaningful in non-parametric models of production (ISBA Discussion Papers 1031). https://hdl.handle.net/2078.5/208718