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Open Access Publications from the University of California

Efficient Estimation of Nested Logit Models


This paper 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 either LML or FIML; and its uncorrected second-stage standard-error estimates are strongly downward biased. LML is only slightly less efficient than FIML, but is often easier to compute. However there are cases where the sequential and LML estimators do not exist.

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