Identification of parameters in normal error component logit-mixture (NECLM) models

Journal of Applied Econometrics

Volumn 22, Issue 6, 2007, Pages 1095-1125

  Walker, Joan L ^a   Ben Akiva, Moshe ^b   Bolduc, Denis ^c  

^a BOSTON UNIVERSITY   (United States)

^b USA   (United States)

^c UNIVERSITÉ LAVAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 35648986691     PISSN: 08837252     EISSN: 10991255     Source Type: Journal    
DOI: 10.1002/jae.971     Document Type: Article

References (47)

1. Barros CP, Proenca I. 2005. Mixed logit estimation of radical Islamic terrorism in Europe and North America. Journal of Conflict Resolution 49(2): 298-314.
2. Ben-Akiva M, Bolduc D. 1996. Multinomial probit with a logit kernel and a general parametric specification of the covariance structure. Working paper, Massachusetts Institute of Technology.
3. Ben-Akiva M, Lerman S. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press: Cambridge, MA.
4. Ben-Akiva M, Bolduc D, Bradley M. 1993. Estimation of travel choice models with a distributed value of time. Transportation Research Record 1413: 88-97.
5. Ben-Akiva M, Bolduc D, Walker J. 2002. Specification, identification, and estimation of the logit kernel (or continuous mixed logit) model. Working paper, Massachusetts Institute of Technology.
6. Bhat CR. 1995. A heteroscedastic extreme value model of intercity travel mode choice. Transportation Research B 29(6): 471-483.
7. Bhat CR. 1997. Accommodating flexible substitution patterns in multi-dimensional choice modeling: formulation and application to travel mode and departure time choice. Transportation Research B 32(7): 455-466.
8. Bhat CR, Castelar S. 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 B 36(7): 593-616.
9. Bhat CR, Gossen R. 2004. A mixed multinomial logit model analysis of weekend recreational episode type choice. Transportation Research B 38: 767-787.
10. Bhat CR, Guo J. 2004. A mixed spatially correlated logit model: formulation and application to residential choice modeling. Transportation Research B 38(1): 39-60.
11. Bierlaire M. 2003. BIOGEME: a free package for the estimation of discrete choice models. In Proceedings of the 3rd Swiss Transportation Research Conference, Ascona, Switzerland.
12. Bierlaire M. 2005. An introduction to BIOGEME Version 1.4. biogeme.epfl.ch [29 May 2007].
13. Bolduc D. 1999. A practical technique to estimation of multinomial probit models in transportation. Transportation Research B 33: 63-79.
14. Bolduc D, Ben-Akiva M. 1991. A multinomial probit formulation for large choice sets. In Proceedings of the Sixth International Conference on Travel Behavior, Vol. 2; 243-258.
15. Bonnet C, Simioni M. 2001. Assessing consumer response to protected designation of origin labelling: a mixed multinomial logit approach. European Review of Agricultural Economics 28(4): 433-449.
16. Börsch-Supan A, Heiss F, Seko M. 2001. Housing demand in Germany and Japan. Journal of Housing Economics 10(3): 229-252.
17. Boyd JH, Mellman RE. 1980. The effect of fuel economy standards on the U.S. automotive market: an hedonic demand analysis. Transportation Research A 14: 367-378.
18. Bunch DA. 1991. Estimability in the multinomial probit model. Transportation Research B 25: 1-12.
19. Cardell NS, Dunbar FC. 1980. Measuring the societal impacts of automobile downsizing. Transportation Research A 14: 423-434.
20. Chiou L, Walker JL. 2007. Masking identification of discrete choice models under simulation methods. Journal of Econometrics (forthcoming).
21. Dugundji ER, Walker JL. 2005. Discrete choice models with social and spatial network interdependencies. Transportation Research Record 1921: 70-78.
22. Glasgow G. 2001. Mixed logit models for multiparty elections. Political Analysis 9(1): 116-136.
23. Goett A, Hudson K, Train K. 2000. Customer choice among retail energy suppliers: the willingness-to-pay for service attributes. Energy Journal 21(4): 1-28.
24. Gönül F, Srinivasan K. 1993. Modeling multiple sources of heterogeneity in multinomial logit models: methodological and managerial issues. Marketing Science 12(3): 213-229.
25. Gopinath D, Schofield M, Walker JL, Ben-Akiva M. 2005. Comparative analysis of discrete choice models with flexible substitution patterns. In Proceedings of the 84th Annual Meeting of the Transportation Research Board, Washington, DC.
26. Greene WH. 2003. Econometric Analysis (5th edn). Prentice-Hall: Upper Saddle River, NJ.
27. Hensher DA. 2001. The sensitivity of the valuation of travel time savings to the specification of unobserved effects. Transportation Research E 37: 129-142.
28. Hess S, Polak JW. 2005. Accounting for random taste heterogeneity in airport-choice modelling. Transportation Research Record 1915: 36-43.
29. Hess S, Bierlaire M, Polak J. 2005. Capturing correlation and taste heterogeneity with mixed GEV models. In The Economics of Non-Market Goods and Resources, Vol. 6: Application of Simulation Methods in Environmental and Resource Economics, Scarpa R, Alberini A (eds). Springer: Berlin; 55-76.
30. Herriges JA, Phaneuf DJ. 2002. Inducing patterns of correlation and substitution in repeated logit models of recreation demand. American Journal of Agricultural Economics 84(4): 1076-1090.
31. Louiviere JJ, Hensher DA, Swait JD. 2000. Stated Choice Methods: Analysis and Application. Cambridge University Press: Cambridge, UK.
32. Manski CF. 1995. Identification Problems in the Social Sciences. Harvard University Press.
33. McFadden D. 1984. Econometric analysis of qualitative response models. In Handbook of Econometrics II, Griliches Z, Intriligator MD (eds). Elsevier: Amsterdam; 1395-1457.
34. McFadden D. 1989. A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica 57(5): 995-1026.
35. McFadden D, Train K. 2000. Mixed MNL models for discrete response. Journal of Applied Econometrics 15(5): 447-470.
36. Persson TH. 2002. Welfare calculations in models of the demand for sanitation. Applied Economics 34(12): 1509-1518.
37. Revelt D, Train K. 1998. Mixed logit with repeated choices: households' choice of appliance efficiency level. Review of Economics and Statistics 80(4): 647-657.
38. Spellucci P. 1993. DONLP2 Users Guide. Department of Mathematics, Technical University at Darmstadt: Germany.
39. Steckel JH, Vanhonacker WR. 1988. A heterogeneous conditional logit model of choice. Journal of Business and Economic Statistics 6(3): 391-398.
40. Stern S. 1992. A method for smoothing simulated moments of discrete probabilities in multinomial probit models. Econometrica 60(4): 943-952.
41. Taylor CL, Adamowicz WL, Luckert MK. 2003. Preferences over the timing of forest resource use. Journal of Forest Economics 9(3): 223-240.
42. Toledo T. 2003. Integrating driver behavior modeling, PhD dissertation, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology.
43. Train KE. 2003. Discrete Choice Methods with Simulation. Cambridge University Press: Cambridge, UK.
44. Train KE, McFadden D, Ben-Akiva M. 1987. The demand for local telephone service: a fully discrete model of residential calling patterns and service choices. Rand Journal of Economics 18(1): 109-123.
45. Train KE, Revelt D, Ruud P. 1999. Mixed logit estimation routine for cross-sectional data. http://elsa. berkeley.edu/Software/abstracts/train0196. html [29 May 2007].
46. Walker JL. 2001. Extended discrete choice models: integrated framework, flexible error structures, and latent variables. PhD dissertation, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology.
47. Walker J. 2002. The mixed logit (or logit kernel) model: dispelling misconceptions of identification. Transportation Research Record 1805: 86-98.