A tractable estimator for general mixed multinomial logit models
AbstractThe mixed logit is a framework for incorporating unobserved heterogeneity in discrete choice models in a general way. These models are difficult to estimate because they result in a complicated incomplete data likelihood. This paper proposes a new approach for estimating mixed logit models. The estimator is easily implemented as iteratively re-weighted least squares: the well known solution for complete data likelihood logits. The main benefit of this approach is that it requires drastically fewer evaluations of the simulated likelihood function, making it significantly faster than conventional methods that rely on numerically approximating the gradient. The method is rooted in a generalized expectation and maximization (GEM) algorithm, so it is asymptotically consistent, efficient, and globally convergent.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1219.
Date of creation: 2012
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-12-10 (All new papers)
- NEP-DCM-2012-12-10 (Discrete Choice Models)
- NEP-ECM-2012-12-10 (Econometrics)
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- Arcidiacono, Peter & Jones, John B., 2000.
"Finite Mixture Distribution, Sequential Likelihood, and the EM Algorithm,"
Working Papers, Duke University, Department of Economics
00-16, Duke University, Department of Economics.
- Peter Arcidiacono & John Bailey Jones, 2003. "Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm," Econometrica, Econometric Society, Econometric Society, vol. 71(3), pages 933-946, 05.
- Dankmar Böhning, 1992. "Multinomial logistic regression algorithm," Annals of the Institute of Statistical Mathematics, Springer, Springer, vol. 44(1), pages 197-200, March.
- Dankmar Böhning & Bruce Lindsay, 1988. "Monotonicity of quadratic-approximation algorithms," Annals of the Institute of Statistical Mathematics, Springer, Springer, vol. 40(4), pages 641-663, December.
- Bernal, Raquel & Keane, Michael P., 2010. "Quasi-structural estimation of a model of childcare choices and child cognitive ability production," Journal of Econometrics, Elsevier, Elsevier, vol. 156(1), pages 164-189, May.
- Train,Kenneth E., 2009.
"Discrete Choice Methods with Simulation,"
Cambridge Books, Cambridge University Press,
Cambridge University Press, number 9780521747387.
- Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, SUNY-Oswego, Department of Economics, number emetr2, Spring.
- Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, Cambridge University Press, number 9780521766555.
- Nielsen, Soren Feodor, 2000. "On simulated EM algorithms," Journal of Econometrics, Elsevier, Elsevier, vol. 96(2), pages 267-292, June.
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