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Finite-Sample Properties of the Maximum Likelihood Estimator for the Binary Logit Model With Random Covariates

Qian Chen and David Giles ()

No 906, Econometrics Working Papers from Department of Economics, University of Victoria

Abstract: We examine the finite sample properties of the maximum likelihood estimator for the binary logit model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error function for the maximum likelihood estimator in this model are derived, and we undertake some numerical evaluations to analyze and illustrate these analytic results for the single covariate case. For various data distributions, the bias of the estimator is signed the same as the covariate’s coefficient, and both the absolute bias and the mean squared errors increase symmetrically with the absolute value of that parameter. The behaviour of a bias-adjusted maximum likelihood estimator, constructed by subtracting the (maximum likelihood) estimator of the first-order bias from the original estimator, is examined in a Monte Carlo experiment. This bias-correction is effective in all of the cases considered, and is recommended when the logit model is estimated by maximum likelihood with small samples.

Keywords: Logit model; bias; mean squared error; bias correction; random covariates (search for similar items in EconPapers)
JEL-codes: C01 C13 C25 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2009-08-05
New Economics Papers: this item is included in nep-dcm and nep-ecm
Note: ISSN 1485-6441
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

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Related works:
Journal Article: Finite-sample properties of the maximum likelihood estimator for the binary logit model with random covariates (2012) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0906

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