lclogit: A Stata Module for Estimating Latent Class Conditional Logit Models via the Expectation-Maximization Algorithm

UNSW Australian School of Business Research Paper No. 2012 ECON 49

16 Pages Posted: 12 Nov 2012 Last revised: 6 Oct 2013

See all articles by Daniele Pacifico

Daniele Pacifico

Italian Department of the Treasury

Hong Il Yoo

Durham Business School

Date Written: November 11, 2012

Abstract

This paper describes lclogit, a Stata module for estimating discrete mixture or latent class logit models via the EM algorithm. The software components can be downloaded by typing -net install http://www.stata-journal.com/software/sj13-3/st0312- at the Stata command prompt. The EM algorithm allows overcoming some of the computational difficulties that normally arise when fitting such models with many latent classes. The final version of this article is available in the Stata Journal, Volume 13 Number 3: pp. 625-639.

Keywords: lclogit, latent class model, EM algorithm, mixed logit

JEL Classification: C35, C61, C87

Suggested Citation

Pacifico, Daniele and Yoo, Hong Il, lclogit: A Stata Module for Estimating Latent Class Conditional Logit Models via the Expectation-Maximization Algorithm (November 11, 2012). UNSW Australian School of Business Research Paper No. 2012 ECON 49, Available at SSRN: https://ssrn.com/abstract=2174146

Daniele Pacifico

Italian Department of the Treasury ( email )

Rome, 00187
Italy

Hong Il Yoo (Contact Author)

Durham Business School ( email )

Mill Hill Lane
Durham, Durham DH1 3LB
United Kingdom

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