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

Yoo, H.I.

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.

Citation

Yoo, H. (2020). lclogit2: An enhanced command to fit latent class conditional logit models. The Stata Journal, 20(2), 405-425. https://doi.org/10.1177/1536867x20931003

• 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