Quasi-random simulation of discrete choice models

Transportation Research Part B: Methodological

Volumn 38, Issue 4, 2004, Pages 313-327

  Sándor, Zsolt ^a   Train, Kenneth ^b  

^a ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; MAXIMUM LIKELIHOOD ESTIMATION; RANDOM PROCESSES;

DISCRETE CHOICE MODELS; HALTON DRAWS;

TRANSPORTATION;

MAXIMUM LIKELIHOOD ANALYSIS; PLANNING METHOD; TRAFFIC MANAGEMENT; TRANSPORTATION PLANNING;

EID: 1542468059     PISSN: 01912615     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0191-2615(03)00014-6     Document Type: Article

References (28)

1. Bhat C. Accommodating variations in responsiveness to level-of-service variables in travel mode choice models Transportation Research A 32 1998 455-507
2. Bhat C. An analysis of evening commute stop-making behavior using repeated choice observation from a multi-day survey Transportation Research B 33 1999 495-510
3. Bhat C. Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model Transportation Research B 35 2001 677-693
4. Bhat, C., in press. Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transportation Research Part B
5. Bratley P. Fox B.L. Niederreiter H. Implementation and tests of low-discrepancy sequences ACM Transactions on Modeling and Computer Simulation 2 1992 195-213
6. Brownstone D. Train K. Forecasting new product penetration with flexible substitution patterns Journal of Econometrics 89 1999 109-129
7. Faure H. Discrepance de suites associatees a un systeme de numeration (en dimensions) Acta Arithmetica 41 1982 337-351
8. Hajivassiliou V. Ruud P. Classical estimation methods for ldv models using simulation Engle R. McFadden D. Handbook of Econometrics 1994 2383-2441 North-Holland Amsterdam
9. Hensher, D., Greene, W., 2002. The mixed logit model: the state of practice and warnings for the unwary. Working Paper, School of Business, The University of Sydney
10. Lee L. Asymptotic bias in simulated maximum likelihood estimation of discrete choice models Econometric Theory 11 1995 437-483
11. McFadden D. A method of simulated moments for estimation of discrete response models without numerical integration Econometrica 57 1989 995-1026
12. McFadden, D., 2000. Disaggregate behavioral travel demand's rum side: A 30-year restrospective. Paper presented the International Association for Travel Behavior (IATBR) Conference, Gold Coast, Queensland, Australia, 2-7 July
13. Niederreiter H. Point sets and sequences with small discrepancy Monatshefte fur Mathematik 104 1987 273-337
14. Niederreiter H. Low-discrepancy and low dispersion sequences Journal of Number Theory 30 1988 51-70
15. Niederreiter H. Xing C. Low-discrepancy sequences and global function fields with many rational places Finite Fields and their Applications 2 1996 241-273
16. Owen A. Randomly permuted (t,m,s)-nets and (t,s)-sequences Niederreiter H. Shiue P. Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing 1995 299-317 Springer-Verlag New York
17. Pirsic, G., 2002. A software implementation of Niederreiter-Xing sequences, Paper available from http://www.dismat.oeaw.ac.at/pirs/ niedxing.html
18. Revelt D. Train K. Mixed logit with repeated choices Review of Economics and Statistics 80 1998 647-657
19. Sándor, Z., András, P., 2001. Alternative sampling methods for estimating multivariate normal probabilities. Working Paper, Department of Economics, University of Gronigen, The Netherlands
20. Small, K., Winston, C., Yan, J., 2002. Uncovering the distribution of motorists' preferences. Working Paper, Department of Economics, University of California, Irvine
21. Sobol I. The distribution of points in a cube and the approximate evaluation of integrals Zhurnal Vychislitelnoi Matematiki i. Matematicheskoi Fiziki 1967 784-802
22. Tang B. Orthogonal array-based Latin hypercubes Journal of the American Statistical Association 88 1993 1392-1397s
23. Train K. Recreation demand models with taste variation Land Economics 74 1998 230-239
24. Train, K., 2000. Halton sequences for mixed logit. Working Paper No. E00-278, Department of Economics, University of California, Berkeley
25. Train K. Discrete Choice Methods with Simulation 2003 Cambridge University Press New York
26. Train, K., Hudson, K., 2000. The impact of information in vehicle choice and the demand for electric vehicles in California, project report. National Economic Research Associates
27. Tuffin B. On the use of low-discrepancy sequences in Monte Carlo methods Monte Carlo Methods and Applications 2 1996 295-320
28. Wang X. Hickernell F.J. Randomized Halton sequences Mathematical and Computer Modelling 32 2000 887-899