Nonparametric stochastic frontiers: a local maximum likelihood approach
(2004) , 25 pages
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- Authors
- Abstract
- This paper proposes a nonparametric approach for stochastic frontier (SF) models based on local maximum likelihood techniques. The SF model is presented as encompassing some anchorage parametric model in a nonparametric way. First, we derive asymptotic properties of the estimator for the general case (local linear approximations). Then the results are tailored to a SF model where the convoluted error term (efficiency plus noise) is the sum of an half normal and a normal random variable. The parametric anchorage model is a linear production function and an homoscedastic error term. The local approximation is thus local linear for the production function and local constant for the parameters of the error terms. The performance of our estimator is first established with a simulated data set and then with real data on milk production in Spanish dairy farms. The methods appear to be robust, numerically stable and particularly useful for investigating a production process and the derived efficiency scores.
- Affiliations
State University of New York at Binghamton Department of Economics Seoul National University Department of Statistics Athens University of Economics and Business Department of Economics
Citations
Kumbhakar, S. C., Park, B. U., Simar, L., & Tsionas, E. G. (2004). Nonparametric stochastic frontiers: a local maximum likelihood approach (STAT Discussion Papers 0417). https://hdl.handle.net/2078.5/34704