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

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  • Hong Il Yoo

    (Durham University Business School)

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.

Suggested Citation

  • Hong Il Yoo, 2020. "lclogit2: An enhanced command to fit latent class conditional logit models," Stata Journal, StataCorp LP, vol. 20(2), pages 405-425, June.
    • Handle: RePEc:tsj:stataj:v:20:y:2019:i:2:p:405-425
    DOI: 10.1177/1536867X20931003
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    Cited by:

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    8. Burns, J.G. & Glenk, K. & Eory, V. & Simm, G. & Wall, E., 2021. "Preferences of European dairy stakeholders in breeding for resilient and efficient cattle: a Best-Worst Scaling approach," 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 311092, Agricultural Economics Society - AES.
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