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Estimation on stated-preference experiments constructed from revealed-preference choices

  • Train, Kenneth
  • Wilson, Wesley W.

Constructing stated-preference (sp) experiments from a choice that the respondent made in a revealed-preference setting can enhance the realism of the sp task and the efficacy of preference revelation. However, the practice creates dependence between the sp attributes and unobserved factors, contrary to the independence assumption that is maintained for standard estimation procedures. We describe a general estimation method that accounts for this non-independence and give specific examples based on standard and mixed logit specifications of utility. We show conditions under which standard estimation methods are consistent despite the non-independence. We illustrate the general methodology through an application to shippers' choice of route and mode along the Columbia/Snake River system.

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Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

Volume (Year): 42 (2008)
Issue (Month): 3 (March)
Pages: 191-203

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Handle: RePEc:eee:transb:v:42:y:2008:i:3:p:191-203
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  1. Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
  3. David Hensher & Nina Shore & Kenneth Train, 2006. "Water Supply Security and Willingness to Pay to Avoid Drought Restrictions," The Economic Record, The Economic Society of Australia, vol. 82(256), pages 56-66, 03.
  4. Steven Berry & James Levinsohn & Ariel Pakes, 2004. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 68-105, February.
  5. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
  6. Richard Blundell & James Powell, 2001. "Endogeneity in semiparametric binary response models," CeMMAP working papers CWP05/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
  8. Brownstone, David & Bunch, David S & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," University of California Transportation Center, Working Papers qt45f996hh, University of California Transportation Center.
  9. Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2, March.
  10. Dubin, Jeffrey A & McFadden, Daniel L, 1984. "An Econometric Analysis of Residential Electric Appliance Holdings and Consumption," Econometrica, Econometric Society, vol. 52(2), pages 345-62, March.
  11. David A. Hensher, 2006. "How do respondents process stated choice experiments? Attribute consideration under varying information load," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 861-878.
  12. Hensher, David A. & Rose, John M., 2007. "Development of commuter and non-commuter mode choice models for the assessment of new public transport infrastructure projects: A case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 428-443, June.
  13. David Hensher & Nina Shore & Kenneth Train, 2005. "Households’ Willingness to Pay for Water Service Attributes," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 32(4), pages 509-531, December.
  14. Sándor, Zsolt & Train, Kenneth, 2004. "Quasi-random simulation of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 313-327, May.
  15. Shinghal, Nalin & Fowkes, Tony, 2002. "Freight mode choice and adaptive stated preferences," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(5), pages 367-378, September.
  16. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
  17. Broniarczyk, Susan M & Alba, Joseph W, 1994. " The Role of Consumers' Intuitions in Inference Making," Journal of Consumer Research, University of Chicago Press, vol. 21(3), pages 393-407, December.
  18. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
  19. Greene, William H. & Hensher, David A. & Rose, John, 2006. "Accounting for heterogeneity in the variance of unobserved effects in mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 75-92, January.
  20. Caussade, Sebastián & Ortúzar, Juan de Dios & Rizzi, Luis I. & Hensher, David A., 2005. "Assessing the influence of design dimensions on stated choice experiment estimates," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 621-640, August.
  21. Anas, Alex & Feng, Cheng Min, 1988. "Invariance of expected utilities in logit models," Economics Letters, Elsevier, vol. 27(1), pages 41-45.
  22. Bhat, Chandra R. & Castelar, Saul, 2002. "A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 593-616, August.
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