Discrete choice methods with simulation

Discrete Choice Methods with Simulation

Volumn 9780521816960, Issue , 2003, Pages 1-334

  Train, Kenneth E ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CIRCUIT SIMULATION;

DISCRETE CHOICE METHODS; ENVIRONMENTAL STUDIES; GENERALIZED EXTREME VALUE; MIXED LOGIT;

SOFTWARE ENGINEERING;

EID: 84925068967     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511753930     Document Type: Book

References (249)

1. Adamowicz, W. (1994), ‘Habit formation and variety seeking in a discrete choice model of recreation demand’, Journal of Agricultural and Resource Economics 19, 19–31.
2. Ainslie, A., R. Andrews, and I. Currim (2001), ‘An empirical comparison of logit choice models with discrete vs. continuous representation of heterogeneity’, Working Pa-per, Department of Business Administration, University of Delaware.
3. Albert, J. and S. Chib (1993), ‘Bayesian analysis of binary and polychotomous response data’, Journal of the American Statistical Association 88, 669–679.
4. Allenby, G. (1997), ‘An introduction to hierarchical Bayesian modeling’, Tutorial Notes, Advanced Research Techniques Forum, American Marketing Association.
5. Allenby, G. and P. Lenk (1994), ‘Modeling household purchase behavior with logistic normal regression’, Journal of the American Statistical Association 89, 1218–1231.
6. Allenby, G. and P. Rossi (1999), ‘Marketing models of consumer heterogeneity’, Journal of Econometrics 89, 57–78.
7. Amemiya, T. (1978), ‘On two-step estimation of multivariate logit models’, Journal of Econometrics 8, 13–21.
8. Arora, N., G. Allenby, and J. Ginter (1998), ‘A hierachical Bayes model of primary and secondary demand’, Marketing Science 17, 29–44.
9. Beggs, S., S. Cardell, and J. Hausman (1981), ‘Assessing the potential demand for electric cars’, Journal of Econometrics 16, 1–19.
10. Bellman, R. (1957), Dynamic Programming, Princeton University Press, Princeton, NJ.
11. Ben-Akiva, M. (1973), ‘The structure of travel demand models’, PhD Thesis, MIT.
12. Ben-Akiva, M. and M. Bierlaire (1999), ‘Discrete choice methods and their applications in short term travel decisions’, in R. Hall, ed., The Handbook of Transportation Science, Kluwer, Dordrecht, The Netherlands, pp. 5–33.
13. Ben-Akiva, M. and D. Bolduc (1996), ‘Multinomial probit with a logit kernel and a general parametric specification of the covariance structure’, Working Paper, De-partment of Civil Engineering, MIT.
14. Ben-Akiva, M. and B. Francois (1983), ‘Mu-homogenous generalized extreme value model’, Working Paper, Department of Civil Engineering, MIT.
15. Ben-Akiva, M. and S. Lerman (1985), Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, MA.
16. Ben-Akiva, M. and T. Morikawa (1990), ‘Estimation of switching models from revealed preferences and stated intentions’, Transportation Research A 24, 485–495.
17. Ben-Akiva, M., D. Bolduc, and M. Bradley (1993), ‘Estimation of travel model choice models with randomly distributed values of time’, Transportation Research Record 1413, 88–97.
18. Ben-Akiva, M., D. Bolduc, and J. Walker (2001), ‘Specification, estimation and iden-tification of the logit kernel (or continuous mixed logit) model’, Working Paper, Department of Civil Engineering, MIT.
19. Berkovec, J. and S. Stern (1991), ‘Job exit behavior of older men’, Econometrica 59, 189–210.
20. Berndt, E., B. Hall, R. Hall, and J. Hausman (1974),‘Estimation and inference in nonlin-ear structural models’, Annals of Economic and Social Measurement 3/4, 653–665.
21. Bernstein, S. (1917), Calcul des probabilités.
22. Berry, S. (1994), ‘Estimating discrete choice models of product differentiation’, RAND Journal of Economics 25, 242–262.
23. Berry, S., J. Levinsohn, and A. Pakes (1995), ‘Automobile prices in market equilibrium’, Econometrica 63, 841–889.
24. Bhat, C. (1995), ‘A heteroscedastic extreme value model of intercity mode choice’, Transportation Research B 29, 471–483.
25. Bhat, C. (1997), ‘Covariance heterogeneity in nested logit models: Econometric structure and application to intercity travel’, Transportation Research B 31, 11–21.
26. Bhat, C. (1998a), ‘Accommodating variations in responsiveness to level-of-service variables in travel mode choice models’, Transportation Research A 32, 455–507.
27. Bhat, C. (1998b), ‘An analysis of travel mode and departure time choice for urban shopping trips’, Transportation Research B 32, 361–371.
28. Bhat, C. (1999), ‘An analysis of evening commute stop-making behavior using repeated choice observation from a multi-day survey’, Transportation Research B 33, 495–510.
29. Bhat, C. (2000), ‘Incorporating observed and unobserved heterogeneity in urban work mode choice modeling’, Transportation Science 34, 228–238.
30. Bhat, C. (2001), ‘Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model’, Transportation Research B 35, 677–693.
31. Bhat, C. (forthcoming), ‘Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences’, Transportation Research.
32. Bhat, C. and S. Castelar (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 36, 577–669.
33. Bickel, P. and K. Doksum (2000), Mathematical Statistics: Basic Ideas and Selected Topics, Vol. 1, Prentice Hall, Upper Saddle River, NJ.
34. Bierlaire, M. (1998), Discrete choice models, in M. Labbe, G. Laporte, K. Tanczos, and P. Toint, eds., Operations Research and Decision Aid Methodologies in Traffic and Transportation Management, Springer-Verlag, Heidelberg, Germany, pp. 203–227.
35. Boatwright, P., S. Borle, and J. Kadane (2001), ‘A model of the joint distribution of purchase quantity and timing’, Conference Presentation, Bayesian Applications and Methods in Marketing Conference, Ohio State University, and Working Paper, Graduate School of Industrial Administration, Carnegie Mellon University.
36. Bolduc, D. (1992), ‘Generalized autoregressive errors: The multinomial probit model’, Transportation Research B 26, 155–170.
37. Bolduc, D. (1993), ‘Maximum simulated likelihood estimation of MNP models using the GHK probability simulation with analytic derivatives’, Working Paper, Département d’Economique, Université Laval, Quebec.
38. Bolduc, D. (1999), ‘A practical technique to estimate multinomial probit models in transportatio’, Transportation Research B 33, 63–79.
39. Bolduc, D., B. Fortin, and M. Fournier (1996), ‘The impact of incentive policies on the practice location of doctors: A multinomial probit analysis’, Journal of Labor Economics 14, 703–732.
40. Bolduc, D., B. Fortin, and S. Gordon (1997), ‘Multinomial probit estimation of spatially interdependent choices: An empirical comparison of two new techniques’, International Regional Science Review 20, 77–101.
41. Borsch-Supan, A. and V. Hajivassiliou (1993), ‘Smooth unbiased multivariate probability simulation for maximum likelihood estimation of limited dependent variable models’, Journal of Econometrics 58, 347–368.
42. Borsch-Supan, A., V. Hajivassiliou, L. Kotlikoff, and J. Morris (1991), ‘Health, children, and elderly living arrangements: A multiperiod multinomial probit model with unobserved heterogeneity and autocorrelated errors’, in D. Wise, ed., Topics in the Economics of Aging, University of Chicago Press, Chicago.
43. Boyd, J. and J. Mellman (1980), ‘The effect of fuel economy standards on the U.S. automotive market: A hedonic demand analysis’, Transportation Research A 14, 367–378.
44. Braatan, E. and G. Weller (1979), ‘An improved low-discrepancy sequence for multidimensional quasi-Monte Carlo integration’, Journal of Computational Physics 33, 249–258.
45. Bradley, M. and A. Daly (1994), ‘Use of the logit scaling approach to test for rank-order and fatigue effects in stated preference data’, Transportation 21, 167–184.
46. Bradlow, E. and P. Fader (2001), ‘A Bayesian lifetime model for the “hot 100” billboard songs’, Working Paper, The Wharton School, University of Pennsylvania.
47. Brownstone, D. (2001), ‘Discrete choice modeling for transportation’, in D. Hensher, ed., Travel Behavior Research: The Leading Edge, Elsevier, Oxford, UK, pp. 97–124.
48. Brownstone, D. and K. Small (1989), ‘Efficient estimation of nested logit model’, Journal of Business and Economic Statistics 7, 67–74.
49. Brownstone, D. and K. Train (1999), ‘Forecasting new product penetration with flexible substitution patterns’, Journal of Econometrics 89, 109–129.
50. Brownstone, D., D. Bunch, and K. Train (2000), ‘Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles’, Transportation Research B 34, 315–338.
51. Bunch, D. (1991), ‘Estimability in the multinomial probit model’, Transportation Research B 25, 1–12.
52. Bunch, D. and R. Kitamura (1989), ‘Multinomial probit estimation revisited: Testing new algorithms and evaluation of alternative model specification of household car ownership’, Transportation Research Group Report UCD-TRG-RR-4, University of California, Davis.
53. Butler, J. and R. Moffitt (1982), ‘A computationally efficient quadrature procedure for the one factor multinomial probit model’, Econometrica 50, 761–764.
54. Cai, Y., I. Deilami, and K. Train (1998), ‘Customer retention in a competitive power market: Analysis of a “double-bounded plus follow-ups” questionnaire’, The Energy Journal 19, 191–215.
55. Cam, L. Le and G. Yang (1990), Asymptotics in Statistics, Springer-Verlag, New York.
56. Cameron, T. (1988), ‘A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression’, Journal of Environmental Economics and Management 15, 355–379.
57. Cameron, T. and M. James (1987), ‘Efficient estimation methods for closed-ended contingent valuation survey data’, Review of Economics and Statistics 69, 269–276.
58. Cameron, T. and J. Quiggin (1994), ‘Estimation using contingent valuation data from a “dichotomous choice with follow-up” questionnaire’, Journal of Environmental Economics and Management 27, 218–234.
59. Cardell, S. and F. Dunbar (1980), ‘Measuring the societal impacts of automobile downsizing’, Tranportation Research A 14, 423–434.
60. Carneiro, P., J. Heckman, and E. Vytlacil (2001), ‘Estimating the return to education when it varies among individuals’, Working Paper, Department of Economics, University of Chicago.
61. Casella, G. and E. George (1992), ‘Explaining the Gibbs sampler’, American Statistician 46, 167–174.
62. Chapman, R. and R. Staelin (1982), ‘Exploiting rank ordered choice set data within the stochastic utility model’, Journal of Marketing Research 14, 288–301.
63. Chesher, A. and J. Santos-Silva (2002), ‘Taste variation in discrete choice models’, Review of Economic Studies 69, 62–78.
64. Chiang, J., S. Chib, and C. Narasimhan (1999), ‘Markov chain Monte Carlo and models of onsideration set and parameter heterogeneity’, Journal of Econometrics 89, 223–248.
65. Chib, S. and E. Greenberg (1995), ‘Understanding the Metropolis–Hastings algorithm’, American Statistician 49, 327–335.
66. Chib, S. and E. Greenberg (1996), ‘Markov chain Monte Carlo simulation methods in econometrics’, Econometric Theory 12, 409–431.
67. Chib, S. and E. Greenberg (1998), ‘Analysis of multivariate probit models’, Biometrika 85, 347–361.
68. Chintagunta, P., D. Jain, and N. Vilcassim (1991), ‘Investigating heterogeneity in brand preference in logit models for panel data’, Journal of Marketing Research 28, 417–428.
69. Chipman, J. (1960), ‘The foundations of utility’, Econometrica 28, 193–224.
70. Chu, C. (1981), ‘Structural issues and sources of bias in residential location and travel choice models’, PhD Thesis, Northwestern University.
71. Chu, C. (1989), ‘A paired combinational logit model for travel demand analysis’, Proceedings of Fifth World Conference on Transportation Research 4, 295–309.
72. Clark, C. (1961), ‘The greatest of a finite set of random variables’, Operations Research 9, 145–162.
73. Cosslett, S. (1981), ‘Efficient estimation of discrete choice models’, in C. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge, MA.
74. Cowles, M. and B. Carlin (1996), ‘Markov chain Monte Carlo convergence diagnostics:A comparative review’, Journal of the American Statistical Association 91, 883–904.
75. Daganzo, C. (1979), Multinomial Probit: The Theory and Its Application to Demand Forecasting, Academic Press, New York.
76. Daganzo, C., F. Bouthelier, and Y. Sheffi (1977), ‘Multinomial probit and qualitative choice: A computationally efficient algorithm’, Transportation Science 11, 338–358.
77. Dagsvik, J. (1994), ‘Discrete and continuous choice max-stable processes and independence from irrelevant alternatives’, Econometrica 62, 1179–1205.
78. Daly, A. (1987), ‘Estimating “tree” logit models’, Transportation Research B 21, 251–267.
79. Daly, A. and S. Zachary (1978), Improved multiple choice models, in D. Hensher and M. Dalvi, eds., Determinants of Travel Choice, Saxon House, Sussex.
80. Debreu, G. (1960), ‘Review of R.D. Luce individual choice behavior’, American Economic Review 50, 186–188.
81. DeSarbo, W., V. Ramaswamy, and S. Cohen (1995), ‘Market segmentation with choicebased conjoint analysis’, Marketing Letters 6, 137–147.
82. Desvousges, W., S. Waters, and K. Train (1996), ‘Potential economic losses associated with recreational services in the Upper Clark Fork River basin’, Report, Triangle Economic Research, Durham, NC.
83. Dubin, J. and D. McFadden (1984), ‘An econometric analysis of residential electricappliance holdings and consumption’, Econometrica 52, 345–362.
84. Eckstein, Z. and K. Wolpin (1989), ‘The specification and estimation of dynamic stochastic discrete hoice models: A survey’, Journal of Human Resources 24, 562–598.
85. Elrod, T. and M. Keane (1995), ‘A factor analytic probit model for representing themarket structure in panel data’, Journal of Marketing Research 32, 1–16.
86. Erdem, T. (1996), ‘A dynamic analysis of market structure based on panel data’, Marketing Science 15, 359–378.
87. Forinash, C. and F. Koppelman (1993), ‘Application and interpretation of nested logit models of intercity mode choice’, Transportation Research Record 1413, 98–106.
88. Gelman, A. (1992), ‘Iterative and non-iterative simulation algorithms’, Computing Science and Statistics (Interface Proceedings) 24, 433–438.
89. Gelman, A. and D. Rubin (1992), ‘Inference from iterative simulation using multiple sequences’, Statistical Sciences 7, 457–511.
90. Gelman, A., J. Carlin, H. Stern, and D. Rubin (1995), Bayesian Data Analysis, Chapman and Hall, Suffolk.
91. Geman, S. and D. Geman (1984), ‘Stochastic relaxation Gibbs distributions and the Bayesian restoration of images’, IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741.
92. Geweke, J. (1988), ‘Antithetic acceleration of Monte Carlo integration in Bayesian inference’, Journal of Econometrics 38, 73–89.
93. Geweke, J. (1989), ‘Bayesian inference in econometric models using Monte Carlo integration’, Econometrica 57, 1317–1339.
94. Geweke, J. (1991), ‘Efficient simulation from the multivariate normal and Student-t distributions subject to linear constraints’, in E. M. Keramidas, ed., Computer Science and Statistics: Proceedings of the Twenty-Third Symposium on the Interface, pp. 571–578. Fairfax: Interface Foundation of North America, Inc.
95. Geweke, J. (1992), ‘Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments’, in J. Bernardo, J. Berger, A. Dawid, and F. Smith, eds., Bayesian Statistics, Oxford University Press, New York, pp. 169–193.
96. Geweke, J. (1996), ‘Monte Carlo simulation and numerical integration’, in D. Kendrick and J. Rust, eds., Handbook of Computational Economics, Elsevier Science, Amsterdam, pp. 731–800.
97. Geweke, J. (1997), ‘Posterior simulators in econometrics’, in D. Kreps and K. Wallis, eds., Advance Economics and Econometric Theory and Applications, Cambridge University Press, New York.
98. Geweke, J., M. Keane, and D. Runkle (1994), ‘Alternative computational approaches to inference in the multinomial probit model’, Review of Economics and Statistics 76, 609–632.
99. Goett, A. (1998), ‘Estimating Customer Preferences for New Pricing Products’, Electric Power Research Institute Report TR-111483, Palo Alto.
100. Gourieroux, C. and A. Monfort (1993), ‘Simulation-based inference: A survey with special reference to panel data models’, Journal of Econometrics 59, 5–33.
101. Greene, W. (2000), Econometric Analysis, 4th edn, Prentice Hall, Upper Saddle River, NJ.
102. Greene, W. (2001), ‘Fixed and random effects in nonlinear models’, Working Paper, Stern School of Business, New York University.
103. Griffiths, W. (1972), ‘Estimation of actual response coefficients in the Hildreth–Horck random coefficient model’, Journal of the American Statistical Association 67, 663–635.
104. Guilkey, D. and J. Murphy (1993), ‘Estimation and testing in the random effects probit model’, Journal of Econometrics 59, 301–317.
105. Haaijer, M., M. Wedel, M. Vriens, and T. Wansbeek (1998), ‘Utility covariances and context effects in conjoint MNP models’, Marketing Science 17, 236–252.
106. Hajivassiliou, V. and D. McFadden (1998), ‘The method of simulated scores for the estimation of LDV models’, Econometrica 66, 863–896.
107. Hajivassiliou, V. and P. Ruud (1994), ‘Classical estimation methods for LDV models using simulation’, in R. Engle and D. McFadden, eds., Handbook of Econometrics.
108. North-Holland, Amsterdam, pp. 2383–2441.
109. Hajivassiliou, V., D. McFadden, and P. Ruud (1996), ‘Simulation of multivariate normal rectangle probabilities and their derivatives: Theoretical and computational results’, Journal of Econometrics 72, 85–134.
110. Halton, J. (1960), ‘On the efficiency of evaluating certain quasi-random sequences of points in evaluating multi-dimensional integrals’, Numerische Mathematik 2, 84–90.
111. Hamilton, J. (1996), ‘Specification testing in Markov-switching time-series models’, Journal of Econometrics 70, 127–157.
112. Hamilton, J. and R. Susmel (1994), ‘Autoregressive conditional heteroskedasticity and changes in regime’, Journal of Econometrics 64, 307–333.
113. Hammersley, J. and K. Morton (1956), ‘A new Monte Carlo technique: Antithetic variates’, Proceedings of the Cambridge Philosophical Society 52, 449–474.
114. Hanemann, M., J. Loomis, and B. Kanninen (1991), ‘Statistical efficiency of doublebounded dichotomous choice contengent valuation’, American Journal of Agricultural Economics 73, 1255–1263.
115. Hastings, W. (1970), ‘Monte Carlo sampling methods using Markov chains and their applications’, Biometrika 57, 97–109.
116. Hausman, J., ed. (1993), Contingent Valuation: A Critical Assessment, North-Holland, New York.
117. Hausman, J. and D. McFadden (1984), ‘Specification tests for the multinomial logit model’, Econometrica 52, 1219–1240.
118. Hausman, J. and P. Ruud (1987), ‘Specifying and testing econometric models for rankordered data’, Journal of Econometrics 34, 83–103.
119. Hausman, J. and D. Wise (1978), ‘A conditional probit model for qualitative choice:Discrete decisions recognizing interdependence and heterogeneous preferences’, Econometrica 48, 403–429.
120. Heckman, J. (1978), ‘Dummy endogenous variables in a simultaneous equation system’, Econometrica 46, 931–959.
121. Heckman, J. (1979), ‘Sample selection bias as a specification error’, Econometrica 47, 153–162.
122. Heckman, J. (1981a), ‘The incidental parameters problem and the problem of initial condition in estimating a discrete time–discrete data stochastic process’, in C. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge, MA, pp. 179–185.
123. Heckman, J. (1981b), ‘Statistical models for the analysis of discrete panel data’, in C. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge, MA, pp. 114–178.
124. Heckman, J. and B. Singer (1986), ‘Econometric analysis of longitudinal data’, in Z. Griliches and M. Intriligator, eds., Handbook of Econometrics, North-Holland, Amsterdam, pp. 1689–1763.
125. Heiss, F. (2002), ‘Structural choice analysis with nested logit models’, Working Paper, University of Mannheim.
126. Hensher, D. (2001), ‘The valuation of commuter travel time savings for car drivers in New Zealand: Evaluating alternative model specifications’, Transportation 28, 101–118.
127. Hensher, D. and M. Bradley (1993), ‘Using stated response data to enrich revealed preference discrete choice models’, Marketing Letters 4, 39–152.
128. Hensher, D. and W. Greene (2001), ‘The mixed logit model: The state of practice and warnings for the unwary’, Working Paper, School of Business, The University of Sydney.
129. Hensher, D. and W. Greene (2002), ‘Specification and estimation of nested logit model’, Transportation Research B 36, 1–17.
130. Hensher, D., J. Louviere, and J. Swait (1999), ‘Combining sources of preference data’, Journal of Econometrics 89, 197–221.
131. Herriges, J. and C. Kling (1996), ‘Testing the consistency of nested logit models with utility maximization’, Economic Letters 50, 33–39.
132. Horowitz, J. (1991), ‘Reconsidering the multinomial probit model’, Transportation Research B 25, 33–438.
133. Horowitz, J., J. Sparmann, and C. Daganzo (1982), ‘An investigation of the accuracy of the Clark approximation for the multinomial probit model’, Transportation Science 16, 382–401.
134. Hotz, V. and R. Miller (1993), ‘Conditional choice probabilities and the estimation of dynamic models’, Review of Economic Studies 60, 497–529.
135. Hotz, V., R. Miller, S. Sanders, and J. Smith (1993), ‘A simulation estimator for dynamic models of discrete choice’, Review of Economic Studies 61, 265–289.
136. Huber, J. and K. Train (2001), ‘On the similarity of classical and Bayesian estimates of individual mean partworths’, Marketing Letters 12, 259–269.
137. Imai, S., N. Jain, and A. Ching (2001), ‘Bayesian estimation of dynamics discrete choice models’, paper presented at Bayesian Applications and Methods in Marketing Conference, Ohio State University; and Working Paper, Department of Economics, Pennsylvania State University.
138. Joe, S. and I. Sloan (1993), ‘Implementation of a lattice method for numerical multiple integration’, ACM Transactions in Mathematical Software 19, 523–545.
139. Johannesson, M. and D. Lundin (2000), ‘The impact of physical preferences and patient habits on the diffusion of new drugs’, Working Paper, Department of Economics, Stockholm School of Economics.
140. Johnson, N., S. Kotz, and N. Balakrishnan (1994), Continuous Multivariate Distributions, 2nd edn, John Wiley and Sons, New York.
141. Judge, G., R. Hill, W. Griffiths, and T. Lee (1985), The Theory and Practice of Econometrics, 2nd edn, John Wiley and Sons, New York.
142. Judge, G., R. Hill, W. Griffiths, H. Lutkepohl, and T. Lee (1988), Introduction to the Theory and Practice Econometrics, 2nd edn, John Wiley and Sons, New York.
143. Kamakura, W. A. and G. Russell (1989), ‘A probabilistic choice model for market segmentation and elasticity structure’, Journal of Marketing Research 26, 379–390.
144. Karlstrom, A. (2000), ‘Non-linear value functions in random utility econometrics’, Conference Presentation, 9th IATBR Travel Behavior Conference, Australia; and Working Paper, Infrastructure and Planning, Royal Institute of Technology, Stockholm, Sweden.
145. Karlstrom, A. (2001), ‘Developing generalized extreme value models using the Piekands representation theorem’, Working Paper, Infrastructure and Planning, Royal Institute of Technology, Stockholm, Sweden.
146. Kass, R., B. Carlin, A. Gelman, and R. Neal (1998), ‘Markov chain Monte Carlo in practice: A roundtable discussion’, American Statistician 52, 93–100.
147. Keane, M. (1990), ‘Four essays in empirical macro and labor economics’, PhD Thesis, Brown University.
148. Keane, M. (1994), ‘A computationally practical simulation estimator for panel data’, Econometrica 62, 95–116.
149. Keane, M. and K. Wolpin (1994), ‘The solutions and estimation of discrete choice dynamic programming models by simulation and interpretation: Monte Carlo evidence’, Review of Economics and Statistics 76, 648–672.
150. Kling, C. and J. Herriges (1995), ‘An empirical investigation of the consistency of nested logit models with utility maximization’, American Journal of Agricultural Economics 77, 875–884.
151. Koppelman, F. and C. Wen (1998), ‘Alternative nested logit models: Structure, properties and stimation’, Transportation Research B 32, 289–298.
152. Koppelman, F. and C. Wen (2000), ‘The paired combination logit model: Properties, estimation and application’, Transportation Research B 34, 75–89.
153. Laplace, P. (1820), Th́éorie Analytique des Probabilités, 3rd ed., Paris.
154. Lee, B. (1999), ‘Calling patterns and usage of residential toll service under self-selecting tariffs’, Journal of Regulatory Economics 16, 45–82.
155. Lee, L. (1992), ‘On the efficiency of methods of simulated moments and simulated likelihood estimation of discrete choice models’, Econometric Theory 8, 518–552.
156. Lee, L. (1995), ‘Asymptotic bias in simulated maximum likelihood estimation of discrete choice models’, Econometric Theory 11, 437–483.
157. Lehmann, E. and G. Casella (1998), Theory of Point Estimation, 2nd edn, Springer, New York.
158. Levy, A. (2001), ‘A simple consistent non-parametric estimator of the regression function in truncated samples’, Working Paper, Department of Economics, North Carolina State University.
159. Lewbel, A. and O. Linton (2002), ‘Nonparametric censored and truncated regression’, Econometrica 70, 765–779.
160. Liu, Y. and H. Mahmassani (2000), ‘Global maximum likelihood estimation procedures for multinomial probit (MND) model parameters’, Transportation Research B 34, 419–444.
161. Louviere, J., D. Hensher, and J. Swait (2000), Stated Choice Methods: Analysis and Applications, Cambridge University Press, New York.
162. Luce, D. (1959), Individual Choice Behavior, John Wiley and Sons, New York.
163. Luce, D. and P. Suppes (1965), ‘Preferences, utility and subjective probability’, in R. Luce, R. Bush, and E. Galanter, eds., Handbook of Mathematical Psychology, John Wiley and Sons, New York, pp. 249–410.
164. Manski, C. and S. Lerman (1977), ‘The estimation of choice probabilities from choice based samples’, Econometrica 45, 1977–1988.
165. Manski, C. and S. Lerman (1981), ‘On the use of simulated frequencies to approximate choice probabilities’, in C. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge, MA, pp. 305–319.
166. Manski, C. and D. McFadden (1981), ‘Alternative estimators and sample designs for discrete choice analysis’, in C. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge, MA, pp. 2–50.
167. Marschak, J. (1960), ‘Binary choice constraints on random utility indications’, in K. Arrow, ed., Stanford Symposium on Mathematical Methods in the Social Sciences, Stanford University Press, Stanford, CA, pp. 312–329.
168. McCulloch, R. and P. Rossi (1994), ‘An exact likelihood analysis of the multinomial probit model’, Journal of Econometrics 4, 207–240.
169. McCulloch, R. and P. Rossi (2000), ‘Bayesian analysis of the multinomial probit model’, in R. ariano, T. Schuermann, and M. Weeks, eds., Simulation-Based Inference in Econometrics, Cambridge University Press, New York.
170. McFadden, D. (1974), ‘Conditional logit analysis of qualitative choice behavior’, in P. Zarembka, ed., Frontiers in Econometrics, Academic Press, New York, pp. 105–142.
171. McFadden, D. (1978), ‘Modeling the choice of residential location’, in A. Karlqvist, L. Lundqvist, F. Snickars, and J. Weibull, eds., Spatial Interaction Theory and Planning Models, North-Holland, Amsterdam, pp. 75–96.
172. McFadden, D. (1987), ‘Regression-based specification tests for the multinomial logit model’, Journal of Econometrics 34, 63–82.
173. McFadden, D. (1989), ‘A method of simulated moments for estimation of discrete response models without numerical integration’, Econometrica 57, 995–1026.
174. McFadden, D. (1996), ‘Lectures on simulation-assisted statistical inference’, Conference Presentation, EC-squared Conference, Florence, Italy; and Working Paper, Department of Economics, University of California, Berkeley.
175. McFadden, D. (1999), ‘Computing willingness-to-pay in random utility models’, in J.Moore, R. Riezman, and J. Melvin, eds., Trade, Theory and Econometrics: Essays in Honour of John S. Chipman, Routledge, London, pp. 253–274.
176. McFadden, D. (2001), ‘Economic choices’, American Economic Review 91, 351–378.
177. McFadden, D. and K. Train (1996), ‘Consumers’evaluation of new products: Learning from self and others’, Journal of Political Economy 104, 683–703.
178. McFadden, D. and K. Train (2000), ‘Mixed MNL models of discrete response’, Journal of Applied Econometrics 15, 447–470.
179. McFadden, D., A. Talvitie, S. Cosslett, I. Hasan, M. Johnson, F. Reid, and K. Train (1977), ‘Demand model estimation and validation’, Final Report, Volume V, Urban Travel Demand Forecasting Project, Institute of Transportation Studies, University of California, Berkeley.
180. McFadden, D., K. Train, and W. Tye (1978), ‘An application of diagnostic tests for the independence from irrelevant alternatives property of the multinomial logit model’, Transportation Research Record 637, 39–46.
181. McGrath, E. (1970), Fundamentals of Operations Research, West Coast University Press, San Francisco.
182. Mehndiratta, S. (1996), ‘Time-of-day effects in inter-city business travel’, PhD Thesis, University of California, Berkeley.
183. Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller (1953), ‘Equations of state calculations by fast computing machines’, Journal of Chemical Physics 21, 1087–1092.
184. Morokoff, W. and R. Caflisch (1995), ‘Quasi-Monte Carlo integration’, Journal of Computational Physics 122, 218–230.
185. Munizaga, M. and R. Alvarez-Daziano (2001), ‘Mixed logit versus nested logit and probit’, Working Paper, Departmento de Ingeniera Civil, Universidad de Chile.
186. Nevo, A. (2001), ‘Measuring market power in the ready-to-eat cereal industry’, Econometrica 69, 307–342.
187. Niederreiter, H. (1978), ‘Quasi-Monte Carlo methods and pseudo-random numbers’, Bulletin of the American Mathematical Society 84, 957–1041.
188. Niederreiter, H. (1988), ‘Low-discrepancy and low dispersion sequences’, Journal of Number Theory 30, 51–70.
189. O’Donoghue, T. and M. Rabin (1999), ‘Doing it now or later’, American Economic Review 89, 103–124.
190. Ortuzar, J. (1983), ‘Nested logit models for mixed-mode travel in urban corridors’, Transportation Research A 17, 283–299.
191. Pakes, A. (1986), ‘Patents as options: Some estimates of the value of holding European patent stocks’, Econometrica 54, 755–785.
192. Pakes, A. and D. Pollard (1989), ‘Simulation and asymptotics of optimization estimators’, Econometrica 57, 1027–1057.
193. Papatla, P. and L. Krishnamurthi (1992), ‘A probit model of choice dynamics’, Marketing Science 11, 89–206.
194. Petrin, A. and K. Train (2002), ‘Omitted product attributes in discrete choice models’, Working Paper, Department of Economics, University of California, Berkeley.
195. Rao, B. (1987), Asymptotic Theory of Statistical Inference, John Wiley and Sons, New York.
196. Recker, W. (1995), ‘Discrete choice with an oddball atlernative’, Transportation Research B 29, 207–211.
197. Research Triangle Institute (1997), ‘Predicting retail customer choices among electricity pricing alternatives’, Electric Power Research Institute Report, Palo Alto.
198. Revelt, D. (1999), ‘Three discrete choice random coefficients papers and one police crime study’, PhD Thesis, University of California, Berkeley.
199. Revelt, D. and K. Train (1998), ‘Mixed logit with repeated choices’, Review of Economics and Statistics 80, 647–657.
200. Revelt, D. and K. Train (2000), ‘Specific taste parameters and mixed logit’, Working Paper No. E00-274, Department of Economics, University of California, Berkeley.
201. Rossi, P., R. McCulloch, and G. Allenby (1996), ‘The value of household information in target marketing’, Marketing Science 15, 321–340.
202. Rust, J. (1987), ‘Optimal replacement of GMC bus engines: An empirical model of Harold Zurchner’, Econometrica 55, 993–1033.
203. Rust, J. (1994), ‘Estimation of dynamic structural models, problems and prospects:Discrete decision processes’, in C. Sims, ed., Advances in Econometrics: Sixth World Congress, Vol. II, Cambridge University Press, New York, pp. 5–33.
204. Rust, J. (1997), ‘Using randomization to break the curse of dimensionality’, Econometrica 65, 487–516.
205. Ruud, P. (1996), ‘Simulation of the multinomial probit model: An analysis of covariance matrix estimation’, Working Paper, Department of Economics, University of California, Berkeley.
206. Ruud, P. (2000), An Introduction to Classical Econometric Theory, Oxford University Press, NewYork.
207. Sándor, Z. and P. András (2001), ‘Alternative sampling methods for estimating multivariate normal probabilities’, Working Paper, Department of Economics, University of Groningen, The Netherlands.
208. Sándor, Z. and K. Train (2002), ‘Quasi-random simulation of discrete choice models’, Working Paper, Department of Economics, University of California, Berkeley.
209. SawtoothSoftware (1999), ‘The CBC/HB module for hierarchical Bayes’, at www.sawtoothsoftware.com.
210. Schechter, L. (2001), ‘The apple and your eye: Visual and taste rankordered probit analysis’, Working Paper, Department of Agricultural and Resource Economics, University of California, Berkeley.
211. Siikamaki, J. (2001), ‘Discrete choice experiments valuing biodiversity conservation in Finland’, PhD Thesis, University of California, Davis.
212. Siikamaki, J. and D. Layton (2001), ‘Pooled models for contingent valuation and contingent ranking data: Valuing benefits from biodiversity conservation’, Working Paper, Department of Agricultural and Resource Economics, University of California, Davis.
213. Sloan, I. and H. Wozniakowski (1998),‘When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?’, Journal of Complexity 14, 1–33.
214. Small, K. (1987), ‘A discrete choice model for ordered alternatives’, Econometrica 55, 409–424.
215. Small, K. (1994), ‘Approximate generalized extreme value models of discrete choice’, Journal of Econometrics 62, 351–382.
216. Small, K. and H. Rosen (1981), ‘Applied welfare economics of discrete choice models’, Econometrica 49, 105–130.
217. Spanier, J. and E. Maize (1991), ‘Quasi-random methods for estimating integrals using relatively small samples’, SIAM Review 36,18–44.
218. Srinivasan, K. and H. Mahmassani (2000), ‘Dynamic kernel logit model for the analysis of longtitude discrete choice data: Properties and computational assessment’, Working Paper, Department of Civil Engineering, University of Texas, Austin.
219. Steckel, J. and W. Vanhonacker (1988), ‘A heterogeneous conditional logit model of choice’, Journal of Business and Economic Statistics 6, 391–398.
220. Swait, J. and J. Louviere (1993), ‘The role of the scale parameter in the estimation and use of multinomial logit models’, Journal of Marketing Research 30, 305–314.
221. Talvitie, A. (1976), ‘Disaggregate travel demand models with disaggregate data, not aggregate data, and why’, Working Paper No. 7615, Urban Travel Demand Forecasting Project, Institute of Transportation Studies, University of California, Berkeley.
222. Theil, H. (1971), Principles of Econometrics, John Wiley and Sons, New York.
223. Thurstone, L. (1927), ‘A law of comparative judgement’, Psychological Review 34, 273–286.
224. Train, K. (1978), ‘A validation test of a diaggregate mode choice model’, Transportation Research 12, 167–174.
225. Train, K. (1986), Qualitative Choice Analysis, MIT Press, Cambridge, MA.
226. Train, K. (1995), ‘Simulation methods for probit and related models based on convenient error partitioning’, Working Paper No. 95-237, Department of Economics, University of California, Berkeley.
227. Train, K. (1998), ‘Recreation demand models with taste variation’, Land Economics 74, 230–239.
228. Train, K. (1999), ‘Mixed logit models for recreation demand’, in J. Herriges and C. Kling, eds., Valuing Recreation and the Environment, Edward Elgar, Northampton, MA.
229. Train, K. (2000), ‘Halton sequences for mixed logit, Working Paper No. E00-278, Department of Economics, University of California, Berkeley.
230. Train, K. (2001), ‘A comparison of hierarchical Bayes and maximum simulated likelihood for mixed logit’, Working Paper, Department of Economics, University of California, Berkeley.
231. Train, K. and D. McFadden (1978), ‘The goods–leisure tradeoff and disaggregate work trip mode choice models’, Transportation Research 12, 349–353.
232. Train, K., D. McFadden, and M. Ben-Akiva (1987a), ‘The demand for local telephone service: A fully discrete model of residential calling patterns and service choice’, Rand Journal of Economics 18, 109–123.
233. Train, K., D. McFadden, and A. Goett (1987b), ‘Consumer attitudes and voluntary rate schedules for public utilities’, Review of Economics and Statistics LXIX, 383–391.
234. Train, K., M. Ben-Akiva, and T. Atherton (1989), ‘Consumption patterns and selfselecting tariffs’, Review of Economics and Statistics 71, 62–73.
235. Tuffin, B. (1996), ‘On the use of low-discrepancy sequences in Monte Carlo methods’, Monte Carlo Methods and Applications 2, 295–320.
236. Tversky, A. (1972), ‘Elimination by aspects: A theory of choice’, Psychological Review 79, 281–299.
237. Vijverberg, W. (1997), ‘Monte Carlo evaluation of multivariate normal probabilities’, Journal of Econometrics 76, 281–307.
238. von Mises, R. (1931), Wahrscheinlichkeitsrechnung, Springer-Verlag, Berlin.
239. Vovsha, P. (1997), ‘The cross-nested logit model: Application to mode choice in the Tel Aviv metropolitan area’, Conference Presentation, 76th Transportation Research Board Meetings, Washington, DC.
240. Wang, X., E. Bradlow, and H. Wainer (2001), ‘A general Bayesian model for testlets:Theory and application’, Working Paper, Department of Statistics, University of North Carolina, Chapel Hill.
241. Wedel, M. and W. Kamakura (2000), Market Segmentation: Conceptual and Methodological Foundations, 2nd edn, Kluwer Academic Publishers, Boston.
242. Wen, C.-H. and F. Koppelman (2001), ‘The generalized nested logit model’, Transportation Research B 35, 627–641.
243. Wen, D. and M. Levy (2001), ‘An application of Bayes estimation under bounded asymmetric blinex loss: Direct mail decision problem’, Conference Presentation, Bayesian Application and Methods in Marketing Conference, Ohio State University.
244. Williams, H. (1977), ‘On the formation of travel demand models and economic evaluation measures of user benefits’, Environment and Planning A 9, 285–344.
245. Wolpin, K. (1984), ‘An estimable dynamic stochastic model of fertility and child mortality’, Journal of Political Economy 92, 852–874.
246. Wolpin, K. (1987), ‘Estimating a structural search model: The transition from school to work’, Econometrica 55, 801–818.
247. Yai, T., S. Iwakura, and S. Morichi (1997), ‘Multinomial probit with structured covariance for route choice behavior’, Transporation Research B 31, 195–207.
248. Zavoina, R. and W. McElvey (1975), ‘A statistical model for the analysis of ordinal level dependent variables’, Journal of Mathematical Sociology Summer, 103–120.
249. Zellner, A. (1971), An Introduction to Bayesian Inference in Econometrics, John Wiley and Sons, New York.