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lclogit2: An enhanced command to fit latent class conditional logit models

Yoo, H.I.

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Authors

H.I. Yoo



Abstract

This paper describes lclogit2, an enhanced version of lclogit (Pacifico and Yoo, 2013). Like its predecessor, lclogit2 uses the ExpectationMaximization (EM) algorithm to estimate latent class conditional logit (LCL) models. But it executes the EM algorithm’s core algebraic operations in Mata, and runs considerably faster as a result. It also allows linear constraints on parameters to be imposed in a more convenient and flexible manner. It comes with parallel command lclogitml2, a new standalone program that uses gradient-based algorithms to estimate LCL models. Both lclogit2 and lclogitml2 are supported by a new postestimation tool, lclogitwtp2, that evaluates willingness-to-pay measures implied by estimated LCL models.

Journal Article Type Article
Acceptance Date Dec 12, 2019
Online Publication Date Jun 19, 2020
Publication Date Jun 19, 2020
Deposit Date Dec 15, 2019
Publicly Available Date Jun 26, 2020
Journal The Stata Journal
Print ISSN 1536-867X
Electronic ISSN 1536-8734
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 20
Issue 2
Pages 405-425
DOI https://doi.org/10.1177/1536867x20931003
Public URL https://durham-repository.worktribe.com/output/1275171

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