Semiparametric Estimation of Stochastic Frontier Models following the two step procedure originally proposed by Fan et al (1996) and further developed also by Vidoli and Ferrara (2014). In the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions regards the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood or method of moment estimators.
Author(s)
Giancarlo Ferrara, Francesco Vidoli
Maintainer: Giancarlo Ferrara <giancarlo.ferrara@gmail.com>
References
Aigner., D., Lovell, C.A.K., Schmidt, P., 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6:21-37
Fan, Y., Li, Q., Weersink, A., 1996. Semiparametric estimation of stochastic production frontier models. Journal of Business & Economic Statistics 14:460-468
Hastie, T., Tibshirani, R., 1990. Generalized additive models. Chapman & Hall
Meeusen, W., van den Broeck, J., 1977. Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review 18:435-444
Vidoli, F., Ferrara, G., 2014. Analyzing Italian citrus sector by semi-nonparametric frontier efficiency models. Empirical Economics doi 10.1007/s00181-014-0879-6