Yoo, H.I. (2020) 'lclogit2 : an enhanced command to fit latent class conditional logit models.', Stata journal., 20 (2). pp. 405-425.
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.
| Item Type: | Article |
|---|---|
| Full text: | Publisher-imposed embargo (AM) Accepted Manuscript File format - PDF (408Kb) |
| Full text: | (AM) Accepted Manuscript Download PDF (Revised version) (384Kb) |
| Full text: | (VoR) Version of Record Available under License - Creative Commons Attribution. Download PDF (554Kb) |
| Status: | Peer-reviewed |
| Publisher Web site: | https://doi.org/10.1177/1536867X20931003 |
| Publisher statement: | This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
| Date accepted: | 12 December 2019 |
| Date deposited: | 16 December 2019 |
| Date of first online publication: | 2019 |
| Date first made open access: | 16 December 2019 |
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