Iclogit: a Stata module for estimating a mixed logit model with discrete mixing distribution via the Expectation-Maximization algorithm
This paper describe Iclogit, a Stata module to fit latent class logit models through the Expectation-Maximization algorithm. The stability of this estimation method allows overcoming some of the computational difficulties that normally arise when fitting such models with many latent classes. This, in turn, permits users to estimate nonparameterically the mixing distribution of the random coefficients because the more the mass points of the latent class model, the better the approximation of the unknown joint density of the random coefficients.
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- Huber, Joel & Train, Kenneth, 2000.
"On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths,"
Department of Economics, Working Paper Series
qt7zm4f51b, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
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- Joel Huber & Kenneth Train, 2001. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Econometrics 0012003, EconWPA.
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- Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2002. "Reliable estimation of generalized linear mixed models using adaptive quadrature," Stata Journal, StataCorp LP, vol. 2(1), pages 1-21, February.
- Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
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