Generalized maximum entropy estimation of discrete choice models
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
procedure and provide an example using the gmentropylogit command.
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Paul Corral, Mungo Terbish
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gmentropylogit, generalized maximum entropy, maximum entropy, logit, discrete choice
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