Iclogit: a Stata module for estimating a mixed logit model with discrete mixing distribution via the Expectation-Maximization algorithm
AbstractThis 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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Bibliographic InfoPaper provided by Department of the Treasury, Ministry of the Economy and of Finance in its series Working Papers with number 6.
Date of creation: Jul 2012
Date of revision:
Keywords: st0001; lclogit; latent class model; EM algorithm; mixed logit;
Other versions of this item:
- Daniele Pacifico & Hong il Yoo, 2012. "A Stata module for estimating latent class conditional logit models via the Expectation-Maximization algorithm," Discussion Papers 2012-49, School of Economics, The University of New South Wales.
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-08-23 (All new papers)
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