Composite Mardinal Likelihood Estimation of Mixed Discrete Response Choice Models
Event details
| Date | 14.04.2010 |
| Hour | 11:15 |
| Speaker | Prof. Chandra Bhat, Dept of Civil, Architecture and Environmental Engineering, University of Texas at Austin |
| Location |
GC B3 424
|
| Category | Conferences - Seminars |
The likelihood functions of many discrete ordered and unordered-response choice models entail the evalua-tion of analytically-intractable integrals. For instance, the use of a mixing mechanism to relax the independent and identically distributed (IID) error term distribution in the multinomial logit model is well documented in the discrete choice literature on unordered multinomial response models. In such an approach, the error term vector is effectively decomposed into an IID component vector and another vector of jointly distributed ran-dom coefficients that lends the non-IID structure. It is typical (though not always the case) to consider the joint distribution of the random coefficients to be normally distributed. A particular advantage of the mixing approach is that it can be used for both cross-sectional choice data as well as panel data without any substan-tial conceptual and coding difference. However, such mixed models also lead to intractable likelihood func-tion expressions. Except in the case when the integration involves only 1-2 dimensions, maximum simulated likelihood (MSL) techniques are usually employed to estimate these models. Unfortunately, for many practi-cal situations, the computational cost to ensure good asymptotic MSL estimator properties can be prohibitive and literally infeasible as the number of dimensions of integration rises. Besides, the accuracy of simulation techniques is known to degrade rapidly at medium-to-high dimensions, and the simulation noise increases substantially. This leads to convergence problems during estimation. In addition, such simulation-based ap-proaches become impractical in terms of computation time, or even infeasible, as the number of mixing di-mensions grows.
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Contact
- Marianne Ruegg