logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations

logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations

logit, utility, preference, willingness to pay, discrete choice models, R, maximum
likelihood estimation.

This paper introduces the logitr R package for fast maximum likelihood estimation of
multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according
to a chosen distribution. The package is faster than other similar packages such as mlogit,
gmnl, mixl, and apollo, and it supports utility models specified with “preference space” or
“willingness-to-pay (WTP) space” parameterizations, allowing for the direct estimation of
marginal WTP. The typical procedure of computing WTP post-estimation using a preference space model can lead to unreasonable distributions of WTP across the population in
mixed logit models. The paper provides a discussion of some of the implications of each
utility parameterization for WTP estimates. It also highlights some of the design features
that enable logitr’s performant estimation speed and includes a benchmarking exercise
with similar packages. Finally, the paper highlights additional features that are designed
specifically for WTP space models, including a consistent user interface for specifying
models in either space and a parallelized multi-start optimization loop, which is particularly useful for searching the solution space for different local minima when estimating
models with non-convex log-likelihood functions.

John Paul Helveston

https://www.jstatsoft.org/article/view/v105i10

George Washington University

January 2023

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