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Journal of Business & Economic Statistics Volume 7, 1989 - Issue 1
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Original Articles

Efficient Estimation of Nested Logit models

David Brownstone School of Social Sciences and Institute of Transportation Studies, University of California, Irvine, CA, 92717
&
Kenneth A. Small School of Social Sciences and Institute of Transportation Studies, University of California, Irvine, CA, 92717
Pages 67-74 | Published online: 02 Jul 2012
 
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Abstract

This article examines the sequential, full information maximum likelihood (FIML), and linearized maximum likelihood (LML) estimators for a nested logit model of time-of-day choice for work trips. These estimators are compared using a Monte Carlo study based on specification and data from a previously published empirical study. The sequential estimator is found to be much less efficient than LML or FIML, and its uncorrected second-stage standard-error estimates are strongly downward biased. LML is only slightly less efficient than FIML, but it is often easier to compute. There are cases in which the sequential and LML estimators do not exist, but FIML still performs well.

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