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Entropy 2010, 12(6), 1569-1580; doi:10.3390/e12061569

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

1
Department of Economics and the Info-Metrics Institute, American University, Kreeger Hall 104, 4400 Massachusetts Ave., NW, Washington, DC 20016-8029, USA
2
Centro de Finanzas, IESA, Caracas, Venezuela
*
Author to whom correspondence should be addressed.
Received: 16 April 2010 / Revised: 3 June 2001 / Accepted: 11 June 2010 / Published: 14 June 2010
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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. View Full-Text
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 (CC BY 3.0).

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MDPI and ACS Style

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

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