Monte Carlo Simulation for Modified Parametric Of Sample Selection Models Through Fuzzy Approach

Monte Carlo Simulation for Modified Parametric Of Sample Selection Models Through Fuzzy Approach

Yaya Sudarya Triana

Information Systems Department Information System, Universitas Mercu Buana Jakarta, Indonesia

American Journal of Basic and Applied Sciences

The sample selection model is a combination of the regression and probit models. The models are usually estimated by Heckman’s two-step estimator. However, Heckman’s two-step estimator often performs poorly. In the context of parametric methods, the sample selection model is studied. The best approach is to take advantage of the tools provided by the theory of fuzzy sets. It appears very suitable for modeling vague concepts. It is difficult to determine some of the criteria and arrive at a quantitative value. Fuzzy sets theory and its properties through the concept of fuzzy number. The fuzzy function used for solving uncertain of a parametric sample selection model. Estimates from the fuzzy are used to calculate some of equation of the sample selection model. Finally, estimates of the Mean, Root Mean Square Error (RMSE) and the other estimators can be obtained by Heckman two-step estimator through iteration from some parameters and some of values.

Keywords:  Fuzzy, Heckman’s, Monte Carlo, Sample selection model, Simulation

Free Full-text PDF

How to cite this article:
Melo, M.C.F, Macêdo, T.S, Oliveira, J.C, Araújo, M.G.C, Vidal, A.K.L. Odontologic atention on oncological practice.American Journal of Basic and Applied Sciences, 2018, 1:6. (Accepted for publication, Online first, Under proofreading)


1. Heckman, J.J., “Shadow price, market wages and labor supply”, Econometrics, 42, p. 679-694, 1974.
2. Roy, A. D. (1951). Some Thoughts on the Distribution of Earnings. Oxford Economic Papers, 3. 135-146.
3. Maddala, G.S., “Limited-dependent and qualitative in econometrics”, Cambridge University Press. p. 257-289, 1983.
4. Gronau, R., “Wage comparisons: A selectivity bias”, Journal of Political Economy, 82, p. 1119-1143, 1974.
5. Heckman, J.J., “Sample selection as a specification error”, Econometrica, Vol.47, p.153-161, 1979.
6. Lewis, H.G., “Comments on selectivity biases in wage comparisons”, Journal of Political Economy, 82, p. 1145-1155, 1974.
7. Neumark, D. (1988). Employers’ Discriminatory Behavior and the Estimation of Wage Discrimination. Journal of Human Resource, 23,279-295.
8. Gerfin, M. (1996). Parametric and semi-parametric estimation of the binary response model of labour market participant. Journal of Applied Econometrics, 11, 321-339.
9. Vella, F., “Estimating models with sample selection bias: A survey”, Journal of Human Resource, Vol. 33, p. 127-169, 1998.
10. Christofides, L. N., Li, Q., Liu, Z., & Min, I. (2003). Recent two-stage sample selection procedure with an application to the gander wage gap. Journal of Business & Economic Statistics, 21 (3), 396-405.
11. Heckman, J.J., “The common structure of statistical models of truncation, sample selection, and limited dependent variables, and a simple estimation for such models.”, Annals of Economic and Social Measurement, 5, 475-492.
12. Schafgans, M. (1996). Semi-parametric estimation of a sample selection model: Estimation of the intercept, theory and applications.Unpublished Ph.D. Thesis. Yale University. New Haven. USA.
13. Martins, M. F. O. (2001). Parametric and semiparametric estimation of sample selection models: An empirical application to the female labour force in Portugal. Journal of Applied Econometrics, 16, 23-39.
14. Nawata, K., “Estimation of Sample Selection Bias Models by Maximum Likelihood Estimator and Heckman’s Two-Step estimator”, Econometrics Letters, 45, 33-40, 1994.

Terms of Use/Privacy Policy/ Disclaimer/ Other Policies:
You agree that by using our site, you have read, understood, and agreed to be bound by all of our terms of use/privacy policy/ disclaimer/ other policies (click here for details).

This work and its PDF file(s) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.