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.
|Date of creation:||Jul 2012|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.dt.tesoro.it|
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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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- Daniele Pacifico, 2011. "Estimating nonparametric mixed Logit Models via EM algorithm," Department of Economics 0663, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
- 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.
- repec:mod:cappmo:0072 is not listed on IDEAS
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