lclogit2: An enhanced module to estimate latent class conditional logit models

Hong Il Yoo

MPRA Paper from University Library of Munich, Germany

Abstract: This paper describes Stata command lclogit2, an enhanced version of lclogit (Pacifico and Yoo, 2013). Like its predecessor, lclogit2 uses the Expectation-Maximization (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.

Keywords: lclogit2; lclogitml2; lclogitwtp2; lclogit; mixlogit; fmm; finite mixture; mixed logit (search for similar items in EconPapers)
JEL-codes: C35 C61 C87 (search for similar items in EconPapers)
Date: 2019-11-10
New Economics Papers: this item is included in nep-dcm and nep-ore
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/97014/1/MPRA_paper_97014.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:97014

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().