Next Article in Journal
Previous Article in Journal
Entropy 2010, 12(6), 1569-1580; doi:10.3390/e12061569
Article

A Concentrated, Nonlinear Information-Theoretic Estimator for the Sample Selection Model

1,*  and 2
Received: 16 April 2010; in revised form: 3 June 2001 / Accepted: 11 June 2010 / Published: 14 June 2010
Download PDF [172 KB, uploaded 14 June 2010]
Abstract: This paper develops a semi-parametric, Information-Theoretic method for estimating parameters for nonlinear data generated under a sample selection process. Considering the sample selection as a set of inequalities makes this model inherently nonlinear. This estimator (i) allows for a whole class of different priors, and (ii) is constructed as an unconstrained, concentrated model. This estimator is easy to apply and works well with small or complex data. We provide a number of explicit analytical examples for different priors’ structures and an empirical example.
Keywords: concentrated model; inequalities; information; maximum entropy; priors; sample selection concentrated model; inequalities; information; maximum entropy; priors; sample selection
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Golan, A.; Gzyl, H. A Concentrated, Nonlinear Information-Theoretic Estimator for the Sample Selection Model. Entropy 2010, 12, 1569-1580.

AMA Style

Golan A, Gzyl H. A Concentrated, Nonlinear Information-Theoretic Estimator for the Sample Selection Model. Entropy. 2010; 12(6):1569-1580.

Chicago/Turabian Style

Golan, Amos; Gzyl, Henryk. 2010. "A Concentrated, Nonlinear Information-Theoretic Estimator for the Sample Selection Model." Entropy 12, no. 6: 1569-1580.


Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert