| semsfa-package {semsfa} | R Documentation |
Semiparametric Stochastic Frontier Models
Description
Semiparametric Estimation of Stochastic Frontier Models following the two step procedure proposed by Fan et al (1996) and further developed by Vidoli and Ferrara (2015) and Ferrara and Vidoli (2017). In the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions regards the functional form of the frontier and in the second step variance parameters are obtained by pseudolikelihood or method of moments estimators. Monotonicity restrinctions can be imposed by means of P-splines.
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
Ferrara, G., Vidoli, F., 2017. Semiparametric stochastic frontier models: A generalized additive model approach. European Journal of Operational Research, 258:761-777.
Hastie, T., Tibshirani, R., 1990. Generalized additive models. Chapman & Hall
Kumbhakar, S.C., Lovell, C.A.K, 2000. Stochastic Frontier Analysis. Cambridge University Press, U.K
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., 2015. Analyzing Italian citrus sector by semi-nonparametric frontier efficiency models. Empirical Economics, 49:641-658