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Efficiently Estimating Nested Logit Models with Choice-Based Samples: Example Applications.
| 题名： | Efficiently Estimating Nested Logit Models with Choice-Based Samples: Example Applications. |
| 作者： | Koppelman-Frank-S; Garrow-Laurie-A |
| 关键词： | Calibration-; Discrete-choice-models; Mixed-logit-models; Probits-; Scale-effects; Simulation- |
| 摘要： | Discrete choice models with error structures that are not independent and identically distributed have received enormous attention in the recent literature. A detailed synthetic study tests this type of model in a controlled case. With mixed logit and probit models as the study objects, calibration was implemented with the use of software available on the Internet. The controlled situation was built as a simulation laboratory, which generated databases with known parameters. The effects of various elements were analyzed: number of repetitions of the simulation, number of observations in the database, and how the use of Halton sequences improves the mixed logit calibration. The scale effects on the different models are also discussed. The results obtained in this specific context lead to some recommendations for future users of these powerful modeling tools. In particular, flexible structures require large sample sizes to calibrate the elements of the covariance matrix. |
| 总页数： | Transportation Research Record: Journal of the Transportation Research Board. 2005. (1921) pp53-62 (4 Fig., 4 Tab., 16 Ref.) |
| 报告类型： | 科技报告 |
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