Yoo, H.I. (2019) 'lclogit2: An enhanced module to estimate latent class conditional logit models.', Stata journal. .
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.
|Full text:||(AM) Accepted Manuscript|
First Live Deposit - 16 December 2019
Download PDF (408Kb)
|Publisher Web site:||https://uk.sagepub.com/en-gb/eur/the-stata-journal/journal203560|
|Publisher statement:||Author(s), Article Title, Journal Title (Journal Volume Number and Issue Number) pp. xx-xx. Copyright © [year] (Copyright Holder). DOI: [DOI number]|
|Record Created:||16 Dec 2019 13:43|
|Last Modified:||17 Dec 2019 01:12|
|Social bookmarking:||Export: EndNote, Zotero | BibTex|
|Look up in GoogleScholar | Find in a UK Library|