semsfa-package: Semiparametric Stochastic Frontier Models

Description Author(s) References


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.


Giancarlo Ferrara, Francesco Vidoli
Maintainer: Giancarlo Ferrara <[email protected]>


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

semsfa documentation built on April 21, 2018, 1:05 a.m.