Generalized maximum entropy estimation of discrete choice models

Paul Corral Rodas and Mungo Terbish ()
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Mungo Terbish: American University

Stata Journal, 2015, vol. 15, issue 2, 512-522

Abstract: In this article, we describe the gmentropylogit command, which implements the generalized maximum entropy estimation methodology for discrete choice models. This information theoretic procedure is preferred over its maximum likelihood counterparts because it is more efficient, avoids strong parametric assumptions, works well when the sample size is small, performs well when the covariates are highly correlated, and functions well when the matrix is ill conditioned. Here we introduce the generalized maximum entropy. Copyright 2015 by StataCorp LP.

Keywords: gmentropylogit; generalized maximum entropy; maximum entropy; logit; discrete choice (search for similar items in EconPapers)
Date: 2015
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