Iteration Capping For Discrete Choice Models Using the EM Algorithm
AbstractThe Expectation-Maximization (EM) algorithm is a well-established estimation procedure which is used in many domains of econometric analysis. Recent application in a discrete choice framework (Train, 2008) facilitated estimation of latent class models allowing for very exible treatment of unobserved heterogeneity. The high exibility of these models is however counterweighted by often excessively long computation times, due to the iterative nature of the EM algorithm. This paper proposes a simple adjustment to the estimation procedure which proves to achieve substantial gains in terms of convergence speed without compromising any of the advantages of the original routine. The enhanced algorithm caps the number of iterations computed by the inner EM loop near its minimum, thereby avoiding optimization over suboptimally populated classes. Performance of the algorithm is assessed on a series of simulations, with the adjusted algorithm being 3-5 times faster than the original routine.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2013-019.
Date of creation: 2013
Date of revision:
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EM algorithm; discrete choice models; latent class models;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-03-30 (All new papers)
- NEP-DCM-2013-03-30 (Discrete Choice Models)
- NEP-ECM-2013-03-30 (Econometrics)
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