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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
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)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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".
- Daniele Pacifico, 2010. "Estimating nonparametric mixed logit models via EM algorithm," Center for the Analysis of Public Policies (CAPP) 0072, Universita di Modena e Reggio Emilia, Dipartimento di Economia Politica.
- Joel Huber and Kenneth Train., 2000.
"On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths,"
Economics Working Papers
E00-289, University of California at Berkeley.
- Joel Huber & Kenneth Train, 2001. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Econometrics 0012003, EconWPA.
- 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.
- Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
- Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
- Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor and Francis Journals, vol. 41(22), pages 2915-2924.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marina Sabatini).
If references are entirely missing, you can add them using this form.