Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences
AbstractThe use of simulation techniques has been increasing in recent years in the transportation and related fields to accommodate flexible and behaviorally realistic structures for analysis of decision processes. This paper proposes a randomized and scrambled version of the Halton sequence for use in simulation estimation of discrete choice models. The scrambling of the Halton sequence is motivated by the rapid deterioration of the standard Halton sequence's coverage of the integration domain in high dimensions of integration. The randomization of the sequence is motivated from a need to statistically compute the simulation variance of model parameters. The resulting hybrid sequence combines the good coverage property of quasi-Monte Carlo sequences with the ease of estimating simulation error using traditional Monte Carlo methods. The paper develops an evaluation framework for assessing the performance of the traditional pseudo-random sequence, the standard Halton sequence, and the scrambled Halton sequence. The results of computational experiments indicate that the scrambled Halton sequence performs better than the standard Halton sequence and the traditional pseudo-random sequence for simulation estimation of models with high dimensionality of integration.
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Bibliographic InfoArticle provided by Elsevier in its journal Transportation Research Part B: Methodological.
Volume (Year): 37 (2003)
Issue (Month): 9 (November)
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- Vassilis A. Hajivassiliou & Daniel L. McFadden & Paul Ruud, 1993.
"Simulation of Multivariate Normal Rectangle Probabilities and their Derivatives: Theoretical and Computational Results,"
_024, Yale University.
- Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, Elsevier, vol. 72(1-2), pages 85-134.
- Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 29(6), pages 471-483, December.
- Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 35(7), pages 677-693, August.
- Kenneth E. Train., 1995.
"Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning,"
Economics Working Papers, University of California at Berkeley
95-237, University of California at Berkeley.
- Kenneth E. Train, 1996. "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Econometrics, EconWPA 9605001, EconWPA.
- Kenneth Train, . "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Working Papers _009, University of California at Berkeley, Econometrics Laboratory Software Archive.
- Train, Kenneth E., 1995. "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Department of Economics, Working Paper Series, Department of Economics, Institute for Business and Economic Research, UC Berkeley qt94h8x4gd, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
- J. A. Hausman & D. A. Wise, 1976.
"A Conditional Profit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences,"
173, Massachusetts Institute of Technology (MIT), Department of Economics.
- Hausman, Jerry A & Wise, David A, 1978. "A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences," Econometrica, Econometric Society, Econometric Society, vol. 46(2), pages 403-26, March.
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