Abstract
We consider the benchmark stochastic frontier model where inefficiency is directly influenced by observable determinants. In this setting, we estimate the stochastic frontier and the conditional mean of inefficiency without imposing any distributional assumptions. To do so we cast this model in the partly linear regression framework for the conditional mean. We provide a test of correct parametric specification of the scaling function. An empirical example is also provided to illustrate the practical value of the methods described here.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 205-221 |
| Number of pages | 17 |
| Journal | Journal of Productivity Analysis |
| Volume | 47 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jun 1 2017 |
Keywords
- Bandwidth
- Heteroskedasticity
- Kernel
- Partly linear
ASJC Scopus subject areas
- Business and International Management
- Social Sciences (miscellaneous)
- Economics and Econometrics
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Parmeter, C., Wang, H. J., & Kumbhakar, S. C. (2017). Nonparametric estimation of the determinants of inefficiency. Journal of Productivity Analysis, 47(3), 205-221. https://doi.org/10.1007/s11123-016-0479-x