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

Hong Il Yoo ()
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Hong Il Yoo: Durham University Business School

Stata Journal, 2020, vol. 20, issue 2, 405-425

Abstract: In this article, I describe the lclogit2 command, an enhanced version of lclogit (Pacifico and Yoo, 2013, Stata Journal 13: 625–639). Like its predeces- sor, lclogit2 uses the expectation-maximization algorithm to fit latent class conditional logit (LCL) models. But it executes the expectation-maximization algorithm’s core algebraic operations in Mata, so it runs considerably faster as a result. It also allows linear constraints on parameters to be imposed more conveniently and flexibly. It comes with the parallel command lclogitml2, a new stand-alone command that uses gradient-based algorithms to fit LCL models. Both lclogit2 and lclogitml2 are supported by a new postestimation command, lclogitwtp2, that evaluates willingness-to-pay measures implied by fitted LCL models.

Keywords: lclogit2; lclogitml2; lclogitpr2; lclogitcov2; lclogitwtp2; latent class model; conditional logit; expectation-maximization algorithm; lclogit; fmm; finite mixture; mixlogit; mixed logit; willingness to pay (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (22)

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DOI: 10.1177/1536867X20931003

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