Skip to main content Accessibility help
×
Hostname: page-component-84b7d79bbc-7nlkj Total loading time: 0 Render date: 2024-07-31T05:28:53.084Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  05 June 2015

David A. Hensher
Affiliation:
University of Sydney
John M. Rose
Affiliation:
University of Sydney
William H. Greene
Affiliation:
New York University
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Applied Choice Analysis , pp. 1128 - 1162
Publisher: Cambridge University Press
Print publication year: 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aadland, D. and Caplan, A. J. (2003) Willingness to pay for curbside recycling with detection and mitigation of hypothetical bias, American Journal of Agricultural Economics, 85 (3), 492–502.CrossRefGoogle Scholar
Accent Marketing and Research and Centre for Research in Environmental Appraisal & Management (CREAM) (2002) Yorkshire Water Services, Final Report, Prepared for Yorkshire Water, November.
ACT Gover (2003a) Community Summit, Workbook, 27 August, Canberra.
ACT Gover (2003b) Community Water Summit, Workshop Groups’ Summary Reports, 27 August, Canberra. Available at: .
ACTEW Corporation (2004) Community assistance makes water restrictions a success, Media Release, 8 June.
Adamowicz, W., Hanemann, M., Swait, J., Johnson, R., Layton, D., Regenwetter, M., Reimer, T. and Sorkin, R. (2005) Decision strategy and structure in households: a groups perspective, Marketing Letters, 16, 387–399.CrossRefGoogle Scholar
Ailawadi, K. L., Gedenk, K. and Neslin, S. A. (1999) Heterogeneity and purchase event feedback in choice models: an empirical analysis with implications for model buildings, International Journal of Research in Marketing, 16, 177–198.CrossRefGoogle Scholar
Akaike, H. (1974) A new look at the statistical model identification, IEEE Transactions on Automatic Control 19 (6), 716–723.CrossRefGoogle Scholar
Alfnes, F. and Steine, G. (2005) None-of-these bias in hypothetical choice experiments, Discussion Paper DP-06/05, Department of Economics and Resources Management, Norwegian University of Life Sciences, Aas.
Alfnes, F., Guttormsen, A., Steine, G. and Kolstad, K. (2006) Consumers’ willingness to pay for the color of salmon: a choice experiment with real economic incentives, American Journal of Agricultural Economics, 88 (4), 1050–1061.CrossRefGoogle Scholar
Allais, M. (1953) Le comportement de l’homme rationnel devant le risque, Econometrica, 21 (4), 503–546.CrossRefGoogle Scholar
Allenby, G. M., Shively, T. S., Yang, S. and Garratt, M. J. (2004) A choice model for packaged goods: dealing with discrete quantities and quantity discounts, Marketing Science, 23 (1), 95–108.CrossRefGoogle Scholar
Allison, P. (1999) Comparing logit and probit coefficients across groups, Sociological Methods and Research, 28, 186–208.CrossRefGoogle Scholar
Anderson, D. A. and Wiley, J. B. (1992) Efficient choice set designs for estimating cross effect models, Marketing Letters, 3, 357–370.CrossRefGoogle Scholar
Anderson, S., Harrison, G. W., Hole, A. R., Lau, M. and Rutström, E. E. (2012) Non-linear mixed logit, Journal of Theory and Decision, 73, 7–96.Google Scholar
Anderson, S., Harrison, G. W., Lau, M. and Rutström, E. (2007a) Valuation using multiple price list formats, Applied Economics, 39 (4), 675–682.CrossRefGoogle Scholar
Anderson, S., Harrison, G. W., Lau, M. and Rutström, E. (2007b) Dual criteria decisions, Working Paper 06–11, Department of Economics, College of Business Administration, University of Central Florida.
Antonov, I. A. and Saleev, V. M. (1979) An economic method of computing LPtau sequences, Zh, vychisl. Mat. mat. Fiz., 19, 243–245, English translation, USSR Comput. Maths. Math. Phys., 19, 252–256.Google Scholar
Arendt, J. and Holm, A. (2007) Probit models with binary endogenous regressors, 6th International Health Economics Association World Congress, Copenhagen, 8–11 July.
Arentze, T., Borgers, A., Timmermans, H. and DelMistro, R. (2003) Transport stated choice responses: effects of task complexity, presentation format and literacy, Transportation Research Part E, 39, 229–244.CrossRefGoogle Scholar
Aribarg, A., Arora, N. and Bodur, H. O. (2002) Understanding the role of preference revision and concession in group decisions, Journal of Marketing Research, 39, 336–349.CrossRefGoogle Scholar
Armstrong, P. M., Garrido, R. A. and Ortúzar, J. de D. (2001) Confidence intervals to bound the value of time, Transportation Research Part E, 7 (1), 143–161.CrossRefGoogle Scholar
Arora, N. and Allenby, G. M. (1999) Measuring the influence of individual preference structures in group decision making, Journal of Marketing Research, 37, 476–487.CrossRefGoogle Scholar
Asensio, J. and Matas, A. (2008) Commuters’ valuation of travel time variability, Transportation Research Part E, 44 (6), 1074–1085.CrossRefGoogle Scholar
Ashok, K., Dillon, W. R. and Yuan, S. (2002) Extending discrete choice models to incorporate attitudinal and other latent variables, Journal of Marketing Research, 39 (1), 31–46.CrossRefGoogle Scholar
Ashton, W. D. (1972) The logit transformation, Griffin, London. Available at: .Google Scholar
Asmussen, S. and Glynn, P. W. (2007) Stochastic Simulation: Algorithms and Analysis, Springer, New York.Google Scholar
Auger, P., Devinney, T. M. and Louviere, J. J. (2007) Best–worst scaling methodology to investigate consumer ethical beliefs across countries, Journal of Business Ethics, 70, 299–326.CrossRefGoogle Scholar
Backhaus, K., Wilken, R., Voeth, M. and Sichtmann, C. (2005) An empirical comparison of methods to measure willingness to pay by examining the hypothetical bias, International Journal of Market Research 47 (5), 543–562.Google Scholar
Balcombe, K., Fraser, I. and Chalak, A. (2009) Model selection in the Bayesian mixed logit: misreporting or heterogeneous preferences?, Journal of Environmental Economics and Management, 57 (2), 219–225.CrossRefGoogle Scholar
Bateman, I. J., Carson, R. T., Day, B., Dupont, D., Louviere, J. J., Morimoto, S., Scarpa, R. et al. (2008) Choice set awareness and ordering effects in discrete choice experiments, CSERGE Working Paper EDM 08-01.
Bateman, I. J., Carson, R. T., Day, B., Dupont, D., Louviere, J. J., Morimoto, S., Scarpa, R. and Wang, P. (2008) Choice set awareness and ordering effects in discrete choice experiments, CSERGE Working Paper EDM 08–01.
Bateman, I. J. and Munro, A. (2005) An experiment on risky choice amongst households, Economic Journal, 115, 176–189.CrossRefGoogle Scholar
Bates, J., Polak, J., Jones, P. and Cook, A. (2001) The valuation of reliability for personal travel, Transportation Research Part E, 37 (2–3), 191–229.CrossRefGoogle Scholar
Batley, R. and Ibáñez, N. (2009) Randomness in preferences, outcomes and tastes: an application to journey time risk, International Choice Modelling Conference, Harrogate.
Beck, M., Rose, J. M. and Hensher, D. A. (2012) Comparison of group decision making models: a vehicle purchasing case study, Paper presented at the International Association of Traveller Behaviour Research (IATBR) Conference, Toronto, July 13–15.
Beck, M., Rose, J. M. and Hensher, D. A. (2013) Consistently inconsistent: the role of certainty, acceptability and scale in automobile choice, Transportation Research Part E, 56 (3), 81–93.CrossRefGoogle Scholar
Becker, G. (1991) A Treatise on the Family, Harvard University Press, Cambridge, MA.Google Scholar
Becker, G. (1993) A theory of marriage: Part 1, Journal of Political Economy, 81 (4), 813–846.CrossRefGoogle Scholar
Beharry, N., Hensher, D. and Scarpa, R. (2009) An analytical framework for joint vs. separate decisions by couples in choice experiments: the case of coastal water quality in Tobago, Environmental and Resource Economics, 43, 95–117.CrossRefGoogle Scholar
Beharry, N. and Scarpa, R. (2008) Who should select the attributes in choice-experiments for non-market valuation? An application to coastal water quality in Tobago, Sustainability Research Institute, University of Leeds.
Ben-Akiva, M. E. and Bolduc, D. (1996) Multinomial probit with a logit kernel and a general parametric specification of the covariance structure, Unpublished Working Paper, Department of Civil Engineering, MIT, Cambridge, MA.
Ben-Akiva, M. E., Bolduc, D. and Bradley, M. (1993) Estimation of travel choice models with randomly distributed values of time, Transportation Research Record, 1413, 88–97.Google Scholar
Ben-Akiva, M. E., Bradley, M., Morikawa, T., Benjamin, J., Novak, T. P., Oppewal, H. and Rao, V. (1994) Combining revealed and stated preferences data, Marketing Letters, 5 (4), 336–350.CrossRefGoogle Scholar
Ben Akiva, M. E. and Lerman, S. R. (1979) Disaggregate travel and mobility choice models and measures of accessibility, in Hensher, D. A. and Stopher, P. R. (eds.), Behavioural Travel Modelling, Croom Helm, London.Google Scholar
Ben Akiva, M. E. and Lerman, S. R. (1985) Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, Cambridge, MA.Google Scholar
Ben-Akiva, M., McFadden, D., Abe, M., BoÈckenholt, U., Bolduc, D., Gopinath, D., Morikawa, T., Ramaswamy, V., Rao, V., Revelt, D. and Steinberg, D. (1997). Modelling methods for discrete choice analysis, Marketing Letters 8 (3), 273–286.CrossRefGoogle Scholar
Ben-Akiva, M., McFadden, D., Garling, T., Gopinath, D., Walker, J., Bolduc, D., Boersch-Supan, A., Delquié, P., Larichev, O., Morikawa, T., Polydoropoulou, A. and Rao, V. (1999). Extended framework for modelling choice behavior, Marketing Letters, 10 (3), 187–203.CrossRefGoogle Scholar
Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., Bolduc, D., Boersch-Supan, A., Brownstone, D., Bunch, D., Daly, A., de Palma, A., Gopinath, D., Karlstrom, A. and Munizaga, M. A. (2002) Hybrid choice models: progress and challenges, Marketing Letters, 13 (3), 163–175.CrossRefGoogle Scholar
Ben-Akiva, M. E. and Morikawa, T. (1991) Estimation of travel demand models from multiple data sources, in Koshi, M. (ed.), Transportation and Traffic Theory, Proceedings of the 11th ISTTT, Elsevier, Amsterdam, 461–476.Google Scholar
Ben-Akiva, M. E., Morikawa, T. and Shiroishi, F. (1991) Analysis of the reliability of preference ranking data, Journal of Business Research, 23, 253–268.CrossRefGoogle Scholar
Ben-Akiva, M. E., and Swait, J. (1986) The Akaike likelihood ratio index, Transportation Science, 20 (2), 133–136.CrossRefGoogle Scholar
Bentham, J. (1789) An Introduction to the Principles of Morals and Legislation, Clarendon Press, Oxford.CrossRefGoogle Scholar
Berkson, J. (1944) Application of the logistics function to bioassay, Journal of the American Statistical Association, 39, 357–65.Google Scholar
Bernouli, D. (1738) Specimen Theoriae Novae de Mensura Sortis, Commentarii Academiae Scientiarum Imperialis Petropolitanae, Tomus V (Papers of the Imperial Academy of Sciences in Petersburg), V, 175–192.Google Scholar
Berry, S., Levinsohn, J. and Pakes, A. (1995) Automobile prices in market equilibrium, Econometrica, 63 (4), 841–890.CrossRefGoogle Scholar
Bertrand, M. and Mullainathan, S. (2001) Do people mean what they say? Implications for subjective survey data, American Economic Review Papers and Proceedings, 91(2), 67–72.CrossRefGoogle Scholar
Bettman, J. R., Luce, M. F. and Payne, J. W. (1998). Constructive consumer choice processes, Journal of Consumer Research, 25, 187–217.CrossRefGoogle Scholar
Bhat, C. R. (1994) Imputing a continuous income variable from grouped and missing income observations, Economics Letters, 46 (4), 311–320.CrossRefGoogle Scholar
Bhat, C. R. (1995) A heteroscedastic extreme value model of intercity travel mode choice, Transportation Research Part B, 29 (6), 471–483.CrossRefGoogle Scholar
Bhat, C. R. (1997) An endogenous segmentation mode choice model with an application to intercity travel, Transportation Science, 31, 34–48.CrossRefGoogle Scholar
Bhat, C. R. (2001) Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model, Transportation Research Part B, 35 (7), 677–693.CrossRefGoogle Scholar
Bhat, C. R. (2003) Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences, Transportation Research Part B, 37 (9), 837–855.CrossRefGoogle Scholar
Bhat, C. R. (2008) The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations, and model extensions, Transportation Research Part B, 42 (3), 274–303.CrossRefGoogle Scholar
Bhat, C. R. and 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 Part B, 36, 577–669.CrossRefGoogle Scholar
Bhat, C. R. and Pulugurta, V. (1998) A comparison of two alternative behavioral mechanisms for car ownership decisions, Transportation Research Part B, 32 (1), 61–75.CrossRefGoogle Scholar
Bhat, C. R. and Zhao, H. (2002) The spatial analysis of activity stop generation, Transportation Research Part B, 36 (6), 557–575.CrossRefGoogle Scholar
Bickel, P. J. and Doksum, K. A. (1981) An analysis of transformations revisited, Journal of the American Statistical Association, 76, 296–311.CrossRefGoogle Scholar
Blackburn, M., Harrison, G. W. and Rutström, E. E. (1994) Statistical bias functions and informative hypothetical surveys, American Journal of Agricultural Economics, 76 (5), 1084–1088.CrossRefGoogle Scholar
Blanchard, O. and Fischer, S. (1989) Lectures on Macroeconomics, MIT Press, Cambridge, MA.Google Scholar
Bliemer, M. C. J. and Rose, J. M. (2005a) Efficiency and sample size requirements for stated choice studies, Working Paper ITLS-WP-05-08, Institute of Transport and Logistics Studies, The University of Sydney.
Bliemer, M. C. J. and Rose, J. M. (2005b) Efficient designs for alternative specific choice experiments, Working Paper ITLS-WP-05-04, Institute of Transport and Logistics Studies, the University of Sydney.
Bliemer, M. C. J. and Rose, J. M. (2009) Designing stated choice experiments: the state of the art, in Kitamura, R., Yoshi, T. and Yamamoto, T. (eds.), The Expanding Sphere of Travel Behaviour Research, Selected Papers from the 11th International Conference on Travel Behaviour Research, Chapter 25, 495–538.Google Scholar
Bliemer, M. C. J. and Rose, J. M. (2010a) Construction of experimental designs for mixed logit models allowing for correlation across choice observations, Transportation Research Part B: Methodological, 44 (6) 720–34.CrossRefGoogle Scholar
Bliemer, M. C. J. and Rose, J. M. (2010b) Construction of experimental designs for mixed logit models allowing for correlation across choice observations, Transportation Research Part B: Methodological, 44 (6) 720–34.CrossRefGoogle Scholar
Bliemer, M. C. J. and Rose, J. M. (2010c) Serial choice conjoint analysis for estimating discrete choice models, in Hess, S. and Daly, A. (eds.), Choice Modelling: The State of the Art and the State of the Practice, Emerald Group, Bingley, 139–161.Google Scholar
Bliemer, M. C. J. and Rose, J. M. (2011) Experimental design influences on stated choice outputs: an empirical study in air travel choice, Transportation Research Part A, 45 (1), 63–79.Google Scholar
Bliemer, M. C. J. and Rose, J. M. (2013) Confidence intervals of willingness-to-pay for random coefficient logit models, Transportation Research Part B, 58 (2), 199–214.CrossRefGoogle Scholar
Bliemer, M. C. J. and Rose, J. M. (2014) A unified theory of experimental design for stated choice studies, Paper presented at the 10th International Conference on Transport Survey Methods, Leura.
Bliemer, M. C. J., Rose, J. M. and Hensher, D. A. (2009) Efficient stated choice experiments for estimating nested logit models, Transportation Research Part B, 43 (1), 19–35.CrossRefGoogle Scholar
Bliemer, M. C. J., Rose, J. M. and Hess, S. (2008) Approximation of Bayesian efficiency in experimental choice designs, Journal of Choice Modelling, 1 (1), 98–127.CrossRefGoogle Scholar
Blumenschein, K., Johanneson, M., Yokoyama, K. K. and Freeman, P. R. (2001) Hypothetical versus real willingness to pay in the health care sector: results from a field experiment, Journal of Health Economics, 20 (3), 441–457.CrossRefGoogle ScholarPubMed
Blumenschein, K., Johannesson, M., Blomquist, G. C., Liljas, B. and O’Coner, R. M. (1998) Experimental results on expressed certainty and hypothetical bias in contingent valuation, Southern Economic Journal, 65 (1), 169–177.CrossRefGoogle Scholar
Bock, R. D. and Jones, L. V. (1968) The Measurement and Prediction of Judgment and Choice, Holden-Day, San Francisco, CA.Google Scholar
Bock, R. D. and Jones, L. V. (2004) Income and happiness: new results from generalized threshold and sequential models, IZA Discussion Paper 1175, SOI Working Paper 0407.
Boes, S. and Winkelman, R. (2007) Ordered response models, Allgemeines Statistiches Archiv Physica Verlag, 90 (1), 165–180.Google Scholar
Bolduc, D. and Alvarez Daziano, R. (2010) On estimation of hybrid choice models, in Hess, S. and Daly, A. (eds.), Choice Modelling: The State of the Art and the State of Practice, Emerald Group, Bingley.Google Scholar
Bolduc, D., Ben-Akiva, M., Walker, J. and Michaud, A. (2005) Hybrid choice models with logit kernel: applicability to large scale models, in Lee-Gosselin, M. and Doherty, S. (eds), Integrated Land-Use and Transportation Models: Behavioural Foundations, Elsevier, Oxford, 275–302.CrossRefGoogle Scholar
Bonini, N., Tentori, K. and Rumiati, R. (2004) Contingent application of the cancellation editing operation: the role of semantic relatedness between risky outcomes, Journal of Behavioral Decision Making, 17, 139–152.CrossRefGoogle Scholar
Bordley, R. (2013) Discrete choice with large choice sets, Economics Letters, 118, 13–15.CrossRefGoogle Scholar
Borsh-Supan, A. and Hajivassiliou, V. (1993) Smooth unbiased multivariate probability simulation for maximum likelihood estimation of limited dependent variable models, Journal of Econometrics, 58 (3), 347–368.CrossRefGoogle Scholar
Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211–252.Google Scholar
Box, G. E. P. and Draper, N. R. (1987) Empirical Model-Building and Response Surfaces, Wiley, New York.Google Scholar
Bradley, M. A. (2006) Process data for understanding and modelling travel behaviour, in Stopher, P. and Stecher, C. (eds.), Travel Survey Methods: Quality and Future Directions, Elsevier, Oxford.Google Scholar
Bradley, M. A., and Daly, A. J. (1997) Estimation of logit choice models using mixed stated preference and revealed preference information, in Stopher, P. R. and Lee-Gosselin, M. (eds.), Understanding Travel Behaviour in an Era of Change, Pergamon, Oxford, 209–232.Google Scholar
Bradley, R. A. and Terry, M. E. (1952) Rank analysis of incomplete block designs. I: the method of paired comparison, Biometrika, 39, 324–345.Google Scholar
Brant, R. (1990) Assessing proportionality in the proportional odds model for ordinal logistic regression, Biometrics, 46, 1171–1178.CrossRefGoogle ScholarPubMed
Bratley, P., Fox, B. L. and Niederreiter, H. (1992) Implementing Sobol’s quasi-random sequence generator, ACM Transactions on Computer Software, 2 (3), 195–213.Google Scholar
Breffle, W. S. and Morey, E. R. (2000) Investigating preference heterogeneity in a repeated discrete-choice recreation demand model of Atlantic salmon fishing, Marine Resource Economics, 15, 1–20.CrossRefGoogle Scholar
Brewer, A. and Hensher, D. A. (2000) Distributed work and travel behaviour: the dynamics of interactive agency choices between employers and employees, Transportation, 27, 117–148.CrossRefGoogle Scholar
Brewer, C., Kovner, C. T., Wu, Y., Greene, W., Liu, Y. and Reimers, C. (2006) Factors influencing female registered nurses’ work behavior, Health Services Research, 43 (1), 860–866.CrossRefGoogle Scholar
Briesch, R. A., Chintagunta, P. K. and Matzkin, R. L. (2010) Nonparametric discrete choice models with unobserved heterogeneity, Journal of Business and Economic Statistics, 28 (2), 291–307.CrossRefGoogle Scholar
Briesch, R. A., Krishnamurthi, L., Mazumdar, T. and Raj, S. P. (1997) A comparative analysis of reference price models, Journal of Consumer Research, 24, 202–214.CrossRefGoogle Scholar
Brown, T. C., Ajzen, I. and Hrubes, D. (2003) Further tests of entreaties to avoid hypothetical bias in referendum contingent valuation, Journal of Environmental Economics and Management, 46 (2), 353–361.CrossRefGoogle Scholar
Browning, M. and Chiappori, P. A. (1998) Efficient intra-household allocations: a general characterization and empirical tests, Econometrica, 66 (6), 1241–78.CrossRefGoogle Scholar
Brownstone, D. and Small, K. A. (2005) Valuing time and reliability: assessing the evidence from road pricing demonstrations, Transportation Research Part A, 39 (4), 279–293.Google Scholar
Brownstone, D. and Train, K. (1999) Forecasting new product penetration with flexible substitution patterns, Journal of Econometrics, 89 (1–2), 109–129.CrossRefGoogle Scholar
Bunch, D. S., Louviere, J. J. and Anderson, D. A. (1996) A comparison of experimental design strategies for multinomial logit models: the case of generic attributes, Working Paper, Graduate School of Management, University of California at Davis.
Burgess, G., Burgess, H., Burton, L., Howe, C. W., Johnson, L., MacDonnell, L. J. and Reitsma, R. F. (1992) Improving the environmental problem-solving process: lessons from the 1980s California drought, Working Paper, University of Colorado.
Burgess, L. and Street, D. (2003) Optimal designs for 2k choice experiments, Communications in Statistics – Theory and Methods, 32 (11), 2185–2206.CrossRefGoogle Scholar
Burgess, L. and Street, D. (2005) Optimal designs for choice experiments with asymmetric attributes, Journal of Statistical Planning and Inference, 134 (1), 288–301.CrossRefGoogle Scholar
Burnett, N. (1997) Gender economics courses in Liberal Arts Colleges, Journal of Economic Education, 28 (4), 369–377.CrossRefGoogle Scholar
Cai, Y., Deilami, I. and Train, K. (1998) Customer retention in a competitive power market: analysis of a ‘double-bounded plus follow-ups’ questionnaire, Energy Journal, 19 (2), 191–215.Google Scholar
Caflisch, R. E. (1998) Monte Carlo and quasi-Monte Carlo methods, Acta Numerica, 7, 1–49.CrossRefGoogle Scholar
Camerer, C. and Ho, T. (1994) Violations of the betweenness axiom and non-linearity in probability, Journal of Risk and Uncertainty, 8 (2), 167–196.CrossRefGoogle Scholar
Cameron, A. and Trivedi, P. (2005) Microeconometrics: Methods and Applications, Cambridge University Press.CrossRefGoogle Scholar
Cameron, S. V. and Heckman, J. J. (1998) Life cycle schooling and dynamic selection bias: models and evidence for five cohorts of American males, Journal of Political Economy, 106 (2), 262–333.CrossRefGoogle Scholar
Cameron, T. A. and DeShazo, J. R. (2010) Differential attention to attributes in utility-theoretic choice models, Journal of Choice Modelling, 3 (3), 73–115.CrossRefGoogle Scholar
Cameron, T. A., Poe, G. L., Ethier, R. G. and Schulze, W. D. (2002) Alternative non-market value-elicitation methods: are the underlying preferences the same?, Journal of Environmental Economics and Management, 44 (3), 391–425.CrossRefGoogle Scholar
Campbell, D., Hensher, D. A. and Scarpa, R. (2011) Non-attendance to attributes in environmental choice analysis: a latent class specification, Journal of Environmental Planning and Management, 54 (8), 1061–1076.CrossRefGoogle Scholar
Campbell, D., Hensher, D. A. and Scarpa, R. (2012) Cost thresholds, cut-offs and sensitivities in stated choice analysis: identification and implications, Resource and Energy Economics, 34, 396–411.CrossRefGoogle Scholar
Campbell, D., Hutchinson, W. and Scarpa, R. (2008) Incorporating discontinuous preferences into the analysis of discrete choice experiments, Environment and Resource Economics, 43 (1), 403–417.Google Scholar
Cantillo, V., Heydecker, B. and Ortúzar, J. de D. (2006) A discrete choice model incorporating thresholds for perception in attribute values, Transportation Research Part B, 40 (9), 807–825.CrossRefGoogle Scholar
Cantillo, V. and Ortúzar, J. de D. (2005) A semi-compensatory discrete choice model with explicit attribute thresholds of perception, Transportation Research Part B, 39 (7), 641–657.CrossRefGoogle Scholar
Carlsson, F and Martinsson, P. (2001) Do hypothetical and actual marginal willingness to pay differ in choice experiments?, Journal of Environmental Economics and Management, 41 (2), 179–192.CrossRefGoogle Scholar
Carlsson, F and Martinsson, P. (2002) Design techniques for stated preference methods in health economics, Health Economics, 12, 281–294.CrossRefGoogle Scholar
Carlsson, F., Frykblom, P. and Lagerkvist, C-J. (2005) Using cheap-talk as a test of validity of choice experiments, Economics Letters, 89 (5), 147–152.CrossRefGoogle Scholar
Carlsson, F., Kataria, M. and Lampi, E. (2008) Ignoring attributes in choice experiments, Proceedings of the EAERE Conference, 25–28 June, Gothenburg.
Carp, F. M. (1974) Position effects on interview responses, Journal of Gerontology, 29 (5), 581–587.CrossRefGoogle ScholarPubMed
Carrasco, J. A. and Ortúzar, J. de D. (2002) A review and assessment of the nested logit model, Transport Reviews, 22, 197–218.CrossRefGoogle Scholar
Carroll, J. S. and Johnson, E. J. (1990). Decision Research: A Field Guide, Sage, Newbury Park, CA.Google Scholar
Carson, R., Flores, E., Martin, K. and Wright, J. (1996) Contingent valuation and revealed preference methodologies: comparing the estimates for quasi-public goods, Land Economics, 72 (1), 80–99.CrossRefGoogle Scholar
Carson, R., Groves, T., List, J. and Machina, M. (2004) Probabilistic influence and supplemental benefits: a field test of the two key assumptions underlying stated preferences, Paper presented at the European Association of Environmental and Resource Economists, Budapest, June.
Carson, R., Groves, T. and Machina, M. (2007) Incentive and informational properties of preference questions, Environment and Resource Economics, 37, 181–210.CrossRefGoogle Scholar
Carson, R., Louviere, J. J., Anderson, D., Arabie, P., Bunch, D., Hensher, D. A., Johnson, R., Kuhfeld, W., Steinberg, D., Swait, J., Timmermans, H. and Wiley, J. (1994) Experimental analysis of choice, Marketing Letters, 5, 351–367.CrossRefGoogle Scholar
Cassel, E. and Mendelsohn, R. (1985) The choice of functional forms for hedonic price equations: comment, Journal of Urban Economics, 18 (2), 135–142.CrossRefGoogle Scholar
Caussade, S., Ortúzar, J. de D., Rizzi, L. and Hensher, D. A. (2005) Assessing the influence of design dimensions on stated choice experiment estimates, Transportation Research Part B, 39 (7), 621–640.CrossRefGoogle Scholar
Chamberlain, G. (1980) Analysis of covariance with qualitative data, Review of Economic Studies, 47, 225–238.CrossRefGoogle Scholar
Cherchi, E., Meloni, I. and Ortúzar, J. de D. (2002) Policy forecasts involving new train services: application of mixed rp/sp models with interaction effects, Paper presented at the XII Panamerican Conference on Transport and Traffic Engineering, Quito.
Chiuri, M. C. (2000) Individual decisions and household demand for consumption and leisure, Research in Economics, 54, 277–324.CrossRefGoogle Scholar
Choice Metrics (2012) NGene 1.1.1 User Manual and Reference Guide, Choice Metrics, Sydney.Google Scholar
Chorus, C. G. (2010) A new model of random regret minimization, European Journal of Transport and Infrastructure Research, 10, 181–196.Google Scholar
Chorus, C. G., Arentze, T. A. and Timmermans, H. J. P. (2008a) A random regret-minimization model of travel choice, Transportation Research Part B, 42 (1), 1–18.CrossRefGoogle Scholar
Chorus, C. G., Arentze, T. A. and Timmermans, H. J. P. (2008b) A comparison of regret minimization and utility-maximization in the context of travel mode-choices, Proceedings of the 87th Annual Meeting of the Transportation Research Board, Washington, DC.
Cirillo, C., Lindveld, K. and Daly, A. (2000) Eliminating bias due to the repeated measurements problem in SP data, in Ortúzar, J. de D. (ed.), Stated Preference Modelling Techniques: PTRC Perspectives 4, PTRC Education and Research Services Ltd, London.Google Scholar
Cohen, E. (2009) Applying best–worst scaling to wine marketing, International Journal of Wine Business Research, 21 (1), 8–23.CrossRefGoogle Scholar
Collins, A. T. and Rose, J. M. (2011) Estimation of stochastic scale with best–worst data, Manuscript, Institute of Transport and Logistics Studies, University of Sydney Business School, 2nd International Choice Modelling Conference ICMC 2011, University of Leeds, 6 July.
Collins, A. T., Rose, J. M. and Hensher, D. A. (2013) Specification issues in a generalised random parameters attribute nonattendance model, Transportation Research Part B: Methodological, 56, 234–53.CrossRefGoogle Scholar
Connolly, T. and Zeelenberg, M. (2002) Regret in decision making, Current Directions in Psychological Science, 11, 212–216.CrossRefGoogle Scholar
Cook, R. D. and Nachtsheim, C. J. (1980) A comparison of algorithms for constructing exact D-optimal designs, Techometrics, 22, 315–324.CrossRefGoogle Scholar
Cooper, B., Rose, J. M. and Crase, L. (2012) Does anybody like water restrictions? Some observations in Australian urban communities, Australian Journal of Agricultural and Resource Economics, 56 (1), 61–51.CrossRefGoogle Scholar
Corfman, K. P. (1991) Perceptions of relative influence: formation and measurement, Journal of Marketing Research, 28, 125–136.CrossRefGoogle Scholar
Corfman, K. P. and Lehmann, D. R. (1987) Models of cooperative group decision-making and relative influence, Journal of Consumer Research, 14, 1–13.CrossRefGoogle Scholar
Coricelli, G., Critchley, H. D., Joffily, M., O’Doherty, J. P., Sirigu, A. and Dolan, R. J. (2005) Regret and its avoidance: a neuroimaging study of choice behaviour, Nature Neuroscience, 8 (9), 1255–1262.CrossRefGoogle Scholar
Creel, M. D. and Loomis, J. B. (1991) Confidence intervals for welfare measures with an application to a problem of truncated counts, Review of Economics and Statistics, 73, 370–373.CrossRefGoogle Scholar
Cummings, R. G., Harrison, G. W. and Osborne, L. L. (1995) Can the bias of contingent valuation be reduced? Evidence from the laboratory, Economics Working Paper B-95-03, Division of Research, College of Business Administration, University of South Carolina. Available at: .
Cummings, R. G., Harrison, G. W. and Rutström, E. E. (1995) Homegrown values and hypothetical surveys: is the dichotomous choice approach incentive compatible?, American Economic Review, 85 (1), 260–266.Google Scholar
Cummings, R. G. and Taylor, L. O. (1998) Does realism matter in contingent valuation surveys?, Land Economics, 74 (2), 203–215.CrossRefGoogle Scholar
Cummings, R. G. and Taylor, L. O. (1999) Unbiased value estimates for environmental goods: a cheap talk design for the contingent valuation method, American Economic Review, 89 (3), 649–665.CrossRefGoogle Scholar
Cunha, F., Heckman, J. J. and Navarro, S. (2007) The identification and economic content of ordered choice models with stochastic cutoffs, International Economic Review, 48 (4), 1273–1309.CrossRefGoogle Scholar
Daly, A. J. (1987) Estimating ‘tree’ logit models, Transportation Research Part B, 21, 251–67.CrossRefGoogle Scholar
Daly, A. J., Hess, S. and Train, K. (2012) Assuring finite moments for willingness to pay in random coefficient models, Transportation, 39 (1), 19–31.CrossRefGoogle Scholar
Daly, A. J., Hess, S., Patruni, B., Potoglou, D. et al. (2013) Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour, Transportation, 39 (2), 267–297.CrossRefGoogle Scholar
Daly, A. J. and Ortúzar, J. de D. (1990) Forecasting and data aggregation: theory and practice, Traffic Engineering & Control, 31, 632–643.Google Scholar
Daly, A. J. and Zachary, S. (1978) Improved multiple choice models, in Hensher, D. A. and Dalvi, M. Q. (eds.), Determinants of Travel Choice, Saxon House, Farnborough.Google Scholar
Day, B., Bateman, I. J., Carson, R. T., Dupont, D., Louviere, J. J., Morimoto, S., Scarpa, R. et al. (2009) Task independence in stated preference studies: a test of order effect explanations, CSERGE Working Paper EDM 09–14.
Day, B. and Prades, J. P. (2010) Ordering anomalies in choice experiments, Journal of Environmental Economics and Management, 59, 271–285.CrossRefGoogle Scholar
Daykin, A. and Moffitt, P. (2002) Analyzing ordered responses: a review of the ordered probit model, Understanding Statistics, 3, 157–166.CrossRefGoogle Scholar
Daziano, R. and Bolduc, D. (2012) Covariance, identification, and finite sample performance of the MSL and Bayes estimators of a logit model with latent attributes, Transportation, 40 (3), 647–670.CrossRefGoogle Scholar
Debreu, G. (1960) Review of Luce, R. D., Individual Choice Behavior, American Economic Review, 50, 186–188.Google Scholar
Dellaert, B. G. C., Prodigalidad, M. and Louviere, J. J. (1998) Family members’ projections of each other’s preference and influence: a two-stage conjoint approach, Marketing Letters, 9, 135–145.CrossRefGoogle Scholar
Dempster, A. P. (1967) Upper and lower probabilities induced by a multiple-valued mapping, Ann Math. Stat., 38, 325–339.CrossRefGoogle Scholar
DeShazo, J. R. (2002) Designing transactions without framing effects in iterative question formats, Journal of Environmental Economics and Management, 43, 360–385.CrossRefGoogle Scholar
DeShazo, J. R. and Fermo, G. (2002) Designing choice sets for stated preference methods: the effects of complexity on choice consistency, Journal of Environmental Economics and Management, 44, 123–143.CrossRefGoogle Scholar
Diamond, P. and Hausman, J. (1994) Contingent valuation: is some number better than no number?, Journal of Economic Perspectives, 8(4), 45–64.CrossRefGoogle Scholar
Diecidue, E. and Wakker, P. P. (2001) On the intuition of rank-dependent utility, Journal of Risk and Uncertainty, 23 (3), 281–298.CrossRefGoogle Scholar
Diederich, A. (2003) MDFT account of decision making under time pressure, Psychonomic Bulletin & Review, 10 (1), 156–166.CrossRefGoogle ScholarPubMed
Ding, M., Grewal, R. and Liechty, J. (2007) An incentive-aligned mechanism for conjoint analysis, Journal of Marketing Research, 44 (2), 214–223, .CrossRefGoogle Scholar
Domencich, T. and McFadden, D. (1975) Urban Travel Demand, North-Holland, Amsterdam.Google Scholar
Dosman, D. and Adamowicz, W (2006) Combining stated and revealed preference data to construct an empirical examination of intrahousehold bargaining, Review of Economics of the Household, 4, 15–34.CrossRefGoogle Scholar
Drolet, A. and Luce, M. F. (2004) The rationalizing effects of cognitive load on emotion-based trade-off avoidance, Journal of Consumer Research, 31 (1), 63–77.CrossRefGoogle Scholar
Dubois, D. and Prade, H. (1987) Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, 170 (11), 909–924.Google Scholar
Dubois, D. and Prade, H. (1988) Modelling uncertainty and inductive inference: a survey of recent non-additive probability systems, Acta Psychologica, 68, 53–78.CrossRefGoogle Scholar
Dworkin, J. (1973) Global trends in natural disasters 1947–1973, Natural Hazards Research Working Paper 26, Institute of Behavioral Science, University of Colorado.
Einstein, A. (1921) Geometry and experience, Lecture at the Prussian Academy of Science, Berlin, 27 January.
El Helbawy, A. T. and Bradley, R. A. (1978) Treatment contrasts in paired comparisons: large-sample results, applications and some optimal designs, Journal of the American Statistical Association, 73, 831–839.CrossRefGoogle Scholar
Eliasson, J., Hultzkranz, L., Nerhagen, L. and Smidfelt Rosqvist, L. (2009) The Stockholm congestion charging trial 2006: overview of effects, Transportation Research Part A, 43 (3), 240–250.Google Scholar
Eliasson, J. and Mattsson, L. G. (2006) Equity effects of congestion pricing: quantitative methodology and a case study for Stockholm, Transportation Research Part A, 40 (7), 602–620.Google Scholar
Ellsberg, D. (1961) Risk, ambiguity, and the savage axioms, Quarterly Journal of Economics, 75 (4), 643–669.CrossRefGoogle Scholar
Elrod, T. (1988) Choice map: inferring a product-market map from panel data, Marketing Science, 7 (1), 21–40.CrossRefGoogle Scholar
Elrod, T. and Keane, M. P. (1995) A factor-analytic probit model for representing the market structure in panel data, Journal of Marketing Research, 32 (1), 1–16.CrossRefGoogle Scholar
Eluru, N., Bhat, C. R. and Hensher, D. A. (2008) A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes, Accident Analysis and Prevention, 40 (3), 1033–1054.CrossRefGoogle ScholarPubMed
Ericsson, K. A. and Simon, H. A. (1993) Protocol Analysis: Verbal Reports as Data, MIT Press, Cambridge, MA.Google Scholar
Eto, J., Koomey, J., Lehman, B., Martin, N., Mills, E., Webber, C. and Worrell, E. (2001) Scoping study on trends in the economic value of electricity reliability to the US economy, Technical Report, Energy Analysis Department, Lawrence Berkeley Laboratory, Berkeley, CA.
Everitt, B. (1988) A finite mixture model for the clustering of mixed-mode data, Statistics and Probability Letters, 6, 305–309.CrossRefGoogle Scholar
Fader, P. S., Lattin, J. M. and Little, J. D. C. (1992) Estimating nonlinear parameters in the multinomial logit model, Marketing Science, 11 (4), 372–385.CrossRefGoogle Scholar
Fang, K.-T. and Wang, Y. (1994) Number-Theoretic Methods in Statistics, Chapman & Hall, London.CrossRefGoogle Scholar
Ferrini, S. and Scarpa, R. (2007) Designs with a-priori information for nonmarket valuation with choice-experiments: a Monte Carlo study, Journal of Environmental Economics and Management, 53 (3), 342–363.CrossRefGoogle Scholar
Fiebig, D. G., Keane, M., Louviere, J. J. and Wasi, N. (2010) The generalized multinomial logit: accounting for scale and coefficient heterogeneity, Marketing Science, 29 (3), 393–421.CrossRefGoogle Scholar
Fisher, R. A. (1935) The Design of Experiments, Hafner Press, New York.Google Scholar
Flynn, T. N., Louviere, J. J., Peters, T. J. and Coast, J. (2007) Best–worst scaling: what it can do for health care research and how to do it, Journal of Health Economics, 26, 171–189.CrossRefGoogle Scholar
Flynn, T. N., Louviere, J. J., Peters, T. J. and Coast, J. (2008) Estimating preferences for a dermatology consultation using best–worst scaling: comparison of various methods of analysis, BMC Medical Research Methodology, 8 (76), 1–12.CrossRefGoogle ScholarPubMed
Fosgerau, M. (2006) Investigating the distribution of the value of travel time savings, Transportation Research Part B, 40 (8), 688–707.CrossRefGoogle Scholar
Fosgerau, M. (2007) Using nonparametrics to specify a model to measure the value of travel time, Transportation Research Part A, 41 (9), 842–856.Google Scholar
Fosgerau, M. and Bierlaire, M. (2009) Discrete choice models with multiplicative error terms, Transportation Research, Part B: Methodological, 43 (5), 494–505.CrossRefGoogle Scholar
Foster, V., Bateman, I. and Harley, D. (1997) Real and hypothetical willingness to pay for environmental preservation: a non-experimental comparison, Journal of Agricultural Economics, 48 (1), 123–138.CrossRefGoogle Scholar
Fowkes, A. S. and Wardman, M. R. (1988) The design of stated preference travel choice experiments with particular regard to inter-personal taste variations, Journal of Transport Economics and Policy, 22, 27–44.Google Scholar
Fowkes, A. S., Wardman, M. and Holden, D. G. P. (1993) Non-orthogonal stated preference design, Proceedings of the PTRC Summer Annual Meeting, 91–97.
Fox, C. R. and Poldrack, R. A. (2008) Prospect theory and the brain, in Glimcher, P., Fehr, E., Camerer, C. and Poldrack, R. (eds.) Handbook of Neuroeconomics, Academic Press, San Diego, CA, 145–170.Google Scholar
Fox, C. R. and Tversky, A. (1998) A belief-based account of decision under uncertainty, Management Science, 44 (7), 870–895.CrossRefGoogle Scholar
Fox, J. A., Shogren, J. F., Hayes, D. J. and Kliebenstein, J. B. (1998) CVM-X: calibrating contingent values with experimental auction markets, American Journal of Agricultural Economics, 80, 455–465.CrossRefGoogle Scholar
Frykblom, P. (1997) Hypothetical question modes and real willingness to pay, Journal of Environmental Economics and Management, 34 (2), 274–287.CrossRefGoogle Scholar
Fujii, S. and Gärling, T. (2003) Application of attitude theory for improved predictive accuracy of stated preference methods in travel demand analysis, Transportation Research Part A, 37 (4), 389–402.Google Scholar
Galanti, S. and Jung, A. (1997) Low-discrepancy sequences: Monte Carlo simulation of option prices, Journal of Derivatives, 5 (1), 63–83.CrossRefGoogle Scholar
Garrod, G. D., Scarpa, R. and Willis, K. G. (2002) Estimating the benefits of traffic calming on through routes: a choice experiment approach, Journal of Transport Economics and Policy, 36 (2), 211–232.Google Scholar
Georgescu-Roegen, N. (1954) Choice, expectations, and measurability, Quarterly Journal of Economics, 68, 503–534.CrossRefGoogle Scholar
Geweke, J. (1989) Bayesian inference in econometric models using Monte Carlo integration, Econometrica, 57 (6), 1317–1339.CrossRefGoogle Scholar
Geweke, J. (1991) Efficient simulation from multivariate normal and Student-t distributions subject to linear constraints, in Keramidas, M. E. (ed.), Computer Science and Statistics: Proceedings of the Twenty-Third Symposium on the Inference, Interface Foundation of North America, Inc., Fairfax, VA, 571–578.Google Scholar
Ghosh, A. (2001) Valuing time and reliability: commuters’ mode choice from a real time congestion pricing experiment, PhD dissertation, Department of Economics, University of California at Irvine.
Gilboa, I. and Schmeidler, D. (2001) A Theory of Case-Based Decisions, Cambridge University Press.CrossRefGoogle Scholar
Gilbride, T. J. and Allenby, G. M. (2004) A choice model with conjunctive, disjunctive, and compensatory screening rules, Marketing Science, 23 (3), 391–406.CrossRefGoogle Scholar
Gilbride, T. J. and Allenby, G. M. (2006). Estimating heterogeneous EBA and economic screening rule choice models, Marketing Science, 25 (5), 494–509.CrossRefGoogle Scholar
Gilovich, T., Griffin, D. and Kahneman, D. (eds.) (2002) Heuristics and Biases – The Psychology of Intuitive Judgment, Cambridge University Press.CrossRef
Goett, A., Hudson, K. and Train, K. (2000) Customers’ choice among retail energy suppliers: the willingness-to-pay for service attributes, Energy Journal, 21 (4), 1–28.CrossRefGoogle Scholar
Goldstein, W. M. and Einhorn, H. J. (1987) Expression theory and the preference reversal phenomena, Psychological Review, 94 (2), 236–254.CrossRefGoogle Scholar
Golob, T. (2001) Joint models of attitudes and behaviour in evaluation of the San Diego I–15 congestion pricing project, Transportation Research Part A, 35 (6), 495–514.Google Scholar
González, R. and Wu, G. (1999) On the shape of the probability weighting function, Cognitive Psychology, 38 (1), 129–166.CrossRefGoogle ScholarPubMed
González-Vellejo, C. (2002) Making trade-offs: a probabilistic and context-sensitive model of choice behavior, Psychological Review, 109, 137–155.CrossRefGoogle Scholar
Goodwin, P. (1989) The rule of three: a possible solution to the political problem of competing objectives for road pricing, Traffic Engineering and Control, 30, 495–497.Google Scholar
Goodwin, P. (1997) Solving congestion, Inaugural Lecture of the Professorship of Transport Policy, University College London. Available at: , retrieved 19 May 2012.
Gordon, J., Chapman, R. and Blamey, R. (2001) Assessing the options for the Canberra water supply: an application of choice modelling, in Bennett, J. and Blamey, R. (eds.), The Choice Modelling Approach to Environmental Evaluation, Edward Elgar, Cheltenham.Google Scholar
Gotwalt, C. M., Jones, B. A. and Steinberg, D. M. (2009) Fast computation of designs robust to parameter uncertainty for nonlinear settings, Technometrics, 51, 88–95.CrossRefGoogle Scholar
Gourieroux, C., and Monfort, A. (1996) Simulation-Based Methods Econometric Methods, Oxford University Press.Google Scholar
Gourville, J. T. and Soman, D. (2007). Extremeness seeking: when and why consumers prefer the extreme. Harvard Business School Working Paper 07–092.CrossRef
Greene, W. H. (1997) LIMDEP version 7.0 Reference Manual, Econometric Software, New York.
Greene, W. H. (1998a) Econometric Analysis, Prentice Hall, Upper Saddle River, NJ, 4th edn.Google Scholar
Greene, W. H. (1998b) Gender economics courses in Liberal Arts Colleges: further results, Journal of Economic Education, 29 (4), 291–300.CrossRefGoogle Scholar
Greene, W. H. (2001) Fixed and random effects in nonlinear models, Working Paper EC-01–01, Stern School of Business, Department of Economics, New York University.
Greene, W. H. (2002) LIMDEP version 8.0 Reference Manual, Econometric Software, New York.
Greene, W. H. (2004a) The behavior of the fixed effects estimator in nonlinear models, Econometrics Journal, 7 (1), 98–119.CrossRefGoogle Scholar
Greene, W. H. (2004b) Fixed effects and bias due to the incidental parameters problem in the Tobit model, Econometric Reviews, 23 (2), 125–147.CrossRefGoogle Scholar
Greene, W. H. (2007) Nlogit 4, Econometric Software, New York and Sydney.Google Scholar
Greene, W. H. (2008) Econometric Analysis, Prentice Hall, Upper Saddle River, NJ, 6th edn.Google Scholar
Greene, W. H. (2012) Econometric Analysis, Prentice Hall, Upper Saddle River, NJ, 7th edn.Google Scholar
Greene, W. H., Harris, M., Hollingsworth, B. and Maitra, P. (2008) A bivariate latent class correlated generalized ordered probit model with an application to modelling observed obesity levels, Working Paper 08–18, Stern School of Business, New York University.
Greene, W. H. and Hensher, D. A. (2007) Heteroscedastic control for random coefficients and error components in mixed logit, Transportation Research Part E, 43 (5), 610–623.CrossRefGoogle Scholar
Greene, W. H. and Hensher, D. A. (2010a) Modelling Ordered Choices, Cambridge University Press.CrossRefGoogle Scholar
Greene, W. H. and Hensher, D. A. (2010b) Ordered choice, heterogeneity, and attribute processing, Journal of Transport Economics and Policy, 44 (3), 331–264.Google Scholar
Greene, W. H. and Hensher, D. A. (2010c) Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models, Transportation, 37 (3), 413–428.CrossRefGoogle Scholar
Greene, W. H. and Hensher, D. A. (2013) Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model, Applied Economics, 45 (14), 1897–1902.CrossRefGoogle Scholar
Greene, W., Hensher, D. A. and Rose, J. (2005) Using classical simulation based estimators to estimate individual willingness to pay values: a mixed logit case study of commuters, in Scarpa, R. and Alberini, A. (eds.), Applications of Simulation Methods in Environmental and Resource Economics, Springer, Dordrecht, 17–34.CrossRefGoogle Scholar
Griffin, R. C. and Mjelde, J. W. (2000) Valuing water supply reliability, American Journal of Agricultural Economics, 82, 414–426.CrossRefGoogle Scholar
Gunn, H. F. (1988) Value of travel time estimation, Working Paper 157, Institute of Transport Studies, University of Leeds.
Haab, T. C. and McConnell, K. E. (2002) Valuing Environmental Natural Resources: The Econometrics of Non-Market Valuation, Edward Elgar, Northampton, MA.CrossRefGoogle Scholar
Haaijer, R., Wagner, K. and Wedel, M. (2000) Response latencies in the analysis of conjoint choice experiments, Journal of Marketing Research, 37, 376–382.CrossRefGoogle Scholar
Hahn, G. J., and Shapiro, S. S. (1966) A Catalog and Computer Program for the Design and Analysis of Orthogonal Symmetric and Asymmetric Fractional Factorial Experiments, General Electric Research and Development Center, Schenectady, NY.Google Scholar
Hajivassiliou, V. and McFadden, D. (1998) The method of simulated scores for the estimation of LDV models, Econometrica, 66 (4), 863–896.CrossRefGoogle Scholar
Hajivassiliou, V. and Ruud, P. (1994) Classical estimation methods for LDV models using simulation, in Engle, R. and McFadden, D. (eds.), Handbook of Econometrics, North-Holland, Amsterdam.Google Scholar
Hall, J., Kenny, P., King., M., Louviere, J. J., Viney, R. and Yeoh, A. (2002) Using stated preference discrete choice modelling to evaluate the introduction of varicella vaccination, Health Economics, 11(10), 457–465.CrossRefGoogle ScholarPubMed
Hallahan, K. (1999) Seven models of framing: implications for public relations, Journal of Public Relations Research, 11 (3), 205–242.CrossRefGoogle Scholar
Halton, J. (1960) On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals, Numerische Mathematik, 2, 84–90.CrossRefGoogle Scholar
Halton, J. (1970) A retrospective and prospective survey of the Monte Carlo method, SIAM Review, 12 (1), 1–63.CrossRefGoogle Scholar
Hammersley, J. M. and Morton, K. W. (1956) A new Monte Carlo technique: antithetic variates, Proceedings of the Cambridge Philosophical Society, 52, Part 3, 449–475.CrossRefGoogle Scholar
Hanemann, W. (1994) Valuing the environment through contingent valuation, Journal of Economic Perspectives, 8 (4), 19–43.CrossRefGoogle Scholar
Harrison, G. W. (2006a) Experimental evidence of alternative environmental valuation methods, Environmental and Resource Economics, 34 (1), 125–162.CrossRefGoogle Scholar
Harrison, G. W. (2006b) Hypothetical bias over uncertain outcomes, in List, J. A. (ed.), Using Experimental Methods in Environmental and Resource Economics, Edward Elgar, Northampton, MA, 41–69.Google Scholar
Harrison, G. W. (2007) Making choice studies incentive compatible, in Kanninen, B. (ed.), Valuing Environmental Amenities Using Stated Choice Studies, Springer, Dordrecht, 67–110.CrossRefGoogle Scholar
Harrison, G. W., Humphrey, S. and Verschoor, A. (2010) Choice under uncertainty: Evidence from Ethiopia, India and Uganda, Economic Journal, 120 (543), 80–104.CrossRefGoogle Scholar
Harrison, G. W. and List, J. A. (2004) Field experiments, Journal of Economic Literature, 42 (4), 1013–1059.CrossRefGoogle Scholar
Harrison, G. W. and Rutström, E. E. (2008) Experimental evidence on the existence of hypothetical bias in value elicitation methods, in Plott, C. R. and Smith, V. L. (eds.), Handbook of Experimental Economics Results, North-Holland, Amsterdam.Google Scholar
Harrison, G. W. and Rutström, E. E. (2009) Expected utility theory and prospect theory: one wedding and a decent funeral, Journal of Experimental Economics, 12 (2), 133–158.CrossRefGoogle Scholar
Hausman, J. (1978) Specification tests in econometrics, Econometrica, 46, 1251–1271.CrossRefGoogle Scholar
Hausman, J. and McFadden, D. (1984) Specification tests for the multinomial logit model, Econometrica, 52, 1219–1240.CrossRefGoogle Scholar
Heckman, J. (1979) Sample selection bias as a specification error, Econometrica, 47, 153–161.CrossRefGoogle Scholar
Heckman, J. and Singer, B. (1984a) A method for minimizing the impact of distributional assumptions in econometric models, Econometrica, 52, 271–320.CrossRefGoogle Scholar
Heckman, J. and Singer, B. (1984b) Econometric duration analysis, Journal of Econometrics, 24, 63–132.CrossRefGoogle Scholar
Henderson, D. and Parmeter, C. (2014) Applied Nonparametric Econometrics, Cambridge University Press.Google Scholar
Hensher, D. A. (1974) A Probabilistic disaggregate model of binary mode choice, in Hensher, D. A. (ed.), Urban travel choice and demand modelling, Special Report 12, Australian Road Research Board, Melbourne, August, 61–99.Google Scholar
Hensher, D. A. (1975) The value of commuter travel time savings: empirical estimation using an alternative valuation model, Journal of Transport Economics and Policy, 10 (2), 167–176.Google Scholar
Hensher, D. A. (1986) Sequential and full information maximum likelihood estimation of a nested-logit model, Review of Economics and Statistics, 58(4), 657–667.CrossRefGoogle Scholar
Hensher, D. A. (1994) Stated preference analysis of travel choices: the state of practice, Special Issue of Transportation on The Practice of Stated Preference Analysis, 21 (2), 106–134.Google Scholar
Hensher, D. A. (1998) Establishing a fare elasticity regime for urban passenger transport, Journal of Transport Economics and Policy, 32 (2), 221–246.Google Scholar
Hensher, D. A. (1999) HEV choice models as a search engine for specification of nested logit tree structures, Marketing Letters, 10 (4), 333–343.CrossRefGoogle Scholar
Hensher, D. A. (2001) Measurement of the valuation of travel time savings, Journal of Transport Economics and Policy, 35 (1), 71–98.Google Scholar
Hensher, D. A. (2001a) The valuation of commuter travel time savings for car drivers in New Zealand: evaluating alternative model specifications, Transportation, 28, 110–118.CrossRefGoogle Scholar
Hensher, D. A. (2002) A systematic assessment of the environmental impacts of transport policy: an end use perspective, Environmental and Resource Economics, 22 (1–2), 185–217.CrossRefGoogle Scholar
Hensher, D. A. (2004) Accounting for stated choice design dimensionality in willingness to pay for travel time savings, Journal of Transport Economics and Policy, 38 (2), 425–446.Google Scholar
Hensher, D. A. (2006a) The signs of the times: imposing a globally signed condition on willingness to pay distributions, Transportation, 33(3), 205–222.CrossRefGoogle Scholar
Hensher, D. A. (2006b) Integrating accident and travel delay externalities in an urban context, Transport Reviews, 26 (4), 521–534.CrossRefGoogle Scholar
Hensher, D. A. (2006c) Revealing differences in behavioral response due to the dimensionality of stated choice designs: an initial assessment, Environmental and Resource Economics, 34 (1), 7–44.CrossRefGoogle Scholar
Hensher, D. A. (2006d) How do respondents handle stated choice experiments? – Attribute processing strategies under varying information load, Journal of Applied Econometrics, 21 (5), 861–878.CrossRefGoogle Scholar
Hensher, D. A. (2008) Joint estimation of process and outcome in choice experiments and implications for willingness to pay, Journal of Transport Economics and Policy, 42 (2), 297–322.Google Scholar
Hensher, D. A. (2010a) Attribute processing, heuristics and preference construction in choice analysis, in Hess, S. and Daly, A. (eds.), Choice Modelling: The State of Art and the State of Practice, Emerald Group, Bingley, 35–70.CrossRefGoogle Scholar
Hensher, D. A. (2010b) Hypothetical bias, choice experiments and willingness to pay, Transportation Research Part B, 44 (6), 735–752.CrossRefGoogle Scholar
Hensher, D. A., Beck, M. J. and Rose, J. M. (2011) Accounting for preference and scale heterogeneity in establishing whether it matters who is interviewed to reveal household automobile purchase preferences, Environment and Resource Economics, 49, 1–22.CrossRefGoogle Scholar
Hensher, D. A. and Bradley, M. (1993) Using stated response data to enrich revealed preference discrete choice models, Marketing Letters, 4 (2), 139–152.CrossRefGoogle Scholar
Hensher, D. A. and Brewer, A. M. (2000) Transport and Economics Management, Oxford University Press.Google Scholar
Hensher, D. A. and Collins, A. (2011) Interrogation of responses to stated choice experiments: is there sense in what respondents tell us? A closer look at what respondents choose in stated choice experiments, Journal of Choice Modelling, 4 (1), 62–89.CrossRefGoogle Scholar
Hensher, D. A. and Goodwin, P. B. (2004) Implementation of values of time savings: the extended set of considerations in a tollroad context, Transport Policy, 11 (2), 171–181.CrossRefGoogle Scholar
Hensher, D. A. and Greene, W. H. (2002) Specification and estimation of the nested logit model: alternative normalisations, Transportation Research Part B, 36 (1), 1–17.CrossRefGoogle Scholar
Hensher, D. A. and Greene, W. H. (2003) Mixed logit models: state of practice, Transportation, 30 (2), 133–176.CrossRefGoogle Scholar
Hensher, D. A. and Greene, W. H. (2010) Non-attendance and dual processing of common-metric attributes in choice analysis: a latent class specification, Empirical Economics, 39 (2), 413–426.CrossRefGoogle Scholar
Hensher, D. A. and Greene, W. H. (2011) Valuation of travel time savings in WTP and preference space in the presence of taste and scale heterogeneity, Journal of Transport Economics and Policy, 45 (3), 505–525.Google Scholar
Hensher, D. A., Greene, W. H. and Chorus, C. (2013) Random regret minimisation or random utility maximisation: an exploratory analysis in the context of automobile fuel choice, Journal of Advanced Transportation, 47, 667–678.CrossRefGoogle Scholar
Hensher, D. A., Greene, W. H. and Li, Z. (2011) Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings, Transportation Research Part B, 45 (7), 954–972.CrossRefGoogle Scholar
Hensher, D. A. and Johnson, L. W. (1981) Applied Discrete-Choice Modelling, Croom Helm, London/John Wiley, New York.Google Scholar
Hensher, D. A. and King, J. (2001) Parking demand and responsiveness to supply, pricing and location in the Sydney Central Business District, Transportation Research Part A, 5 (3), 177–196.Google Scholar
Hensher, D. A. and Knowles, L. (2007) Spatial alliances of public transit operators: establishing operator preferences for area management contracts with government, in Macario, R., Viega, J. and Hensher, D. A. (eds.), Competition and Ownership of Land Passenger Transport, Elsevier, Oxford, 517–546.Google Scholar
Hensher, D. A. and Layton, D. (2010) Common-metric attribute parameter transfer and cognitive rationalisation: implications for willingness to pay, Transportation, 37 (3), 473–490.CrossRefGoogle Scholar
Hensher, D. A. and Li, Z. (2012) Valuing travel time variability within a rank-dependent utility framework and an investigation of unobserved taste heterogeneity, Journal of Transport Economics and Policy, 46 (2), 293–312.Google Scholar
Hensher, D. A. and Li, Z. (2013) Referendum voting in road pricing reform: a review of the evidence, Transport Policy, 25 (1), 186–97.CrossRefGoogle Scholar
Hensher, D. A., Li, Z. and Rose, J. M. (2013) Accommodating risk in the valuation of expected travel time savings, Journal of Advanced Transportation, 47 (2), 206–224.CrossRefGoogle Scholar
Hensher, D. A., Louviere, J. J. and Swait, J. (1999) Combining sources of preference data, Journal of Econometrics, 89, 197–221.CrossRefGoogle Scholar
Hensher, D. A. and Mulley, C. (2014) Complementing distance based charges with discounted registration fees in the reform of road user charges: the impact for motorists and government revenue, Transportation, .
Hensher, D. A., Mulley, C. and Rose, J. M. (2014, in press) Understanding the relationship between voting preferences for public transport and perceptions and preferences for bus rapid transit versus light rail, Journal of Transport Economics and Policy.
Hensher, D. A. and Prioni, P. (2002) A service quality index for area-wide contract performance assessment, Journal of Transport Economics and Policy, 36, 93–113.Google Scholar
Hensher, D. A. and Puckett, S. M. (2007) Congestion charging as an effective travel demand management instrument, Transportation Research Part A, 41 (5), 615–626.Google Scholar
Hensher, D. A., Puckett, S. M. and Rose, J. M. (2007a) Extending stated choice analysis to recognise agent-specific attribute endogeneity in bilateral group negotiation and choice: a think piece, Transportation, 34 (6), 667–679.CrossRefGoogle Scholar
Hensher, D. A., Puckett, S. and Rose, J. (2007b) Agency decision making in freight distribution chains: revealing a parsimonious empirical strategy from alternative behavioural structures, Transportation Research Part B, 41 (9), 924–949.CrossRefGoogle Scholar
Hensher, D. A. and Rose, J. 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, 41 (5), 428–433.Google Scholar
Hensher, D. A. and Rose, J. M (2009) Simplifying choice through attribute preservation or non-attendance: implications for willingness to pay, Transportation Research Part E, 45 (4), 583–590.CrossRefGoogle Scholar
Hensher, D. A. and Rose, J. M (2012) The influence of alternative acceptability, attribute thresholds and choice response certainty on automobile purchase preferences, Journal of Transport Economics and Policy, 46 (3), 451–468.Google Scholar
Hensher, D. A., Rose, J. M. and Beck, M. J. (2012) Are there specific design elements of choice experiments and types of people that influence choice response certainty?, Journal of Choice Modelling, 5 (1), 77–97.CrossRefGoogle Scholar
Hensher, D. A., Rose, J. M. and Collins, A. T. (2011) Identifying commuter preferences for existing modes and a proposed Metro, Public Transport – Planning and Operation, online, , 3:109–147.CrossRefGoogle Scholar
Hensher, D. A., Rose, J. M. and Black, I. (2008) Interactive agency choice in automobile purchase decisions: the role of negotiation in determining equilibrium choice outcomes, Journal of Transport Economics and Policy, 42 (2), 269–296.Google Scholar
Hensher, D. A., Rose, J. M. and Collins, A. (2013) Understanding buy in for risky prospects: incorporating degree of belief into the ex ante assessment of support for alternative road pricing schemes, Journal of Transport Economics and Policy, 47 (3), 453–73.Google Scholar
Hensher, D. A., Rose, J. and Greene, W. (2005a) The implications on willingness to pay of respondents ignoring specific attributes, Transportation, 32 (3), 203–220.CrossRefGoogle Scholar
Hensher, D. A., Rose, J. and Greene, W. H. (2005b) Applied Choice Analysis: A Primer, Cambridge University Press.CrossRefGoogle Scholar
Hensher, D. A., Rose, J. and Greene, W. H. (2012) Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design, Transportation, 39 (2), 235–245.CrossRefGoogle Scholar
Hensher, D. A., Shore, N. and Train, K. N. (2005) Households’ willingness to pay for water service attributes, Environmental and Resource Economics, 32, 509–531.CrossRefGoogle Scholar
Hess, S. and Hensher, D. A. (2010) Using conditioning on observed choices to retrieve individual-specific attribute processing strategies, Transportation Research Part B, 44 (6), 781–790.CrossRefGoogle Scholar
Hess, S., Hensher, D. A. and Daly, A. J. (2012) Not bored yet – revisiting respondent fatigue in stated choice experiments, Transportation Research Part A, 46 (3), 626–644.Google Scholar
Hess, S. and Rose, J. M. (2007) A latent class approach to modelling heterogeneous information processing strategies in SP studies, Paper presented at the Oslo Workshop on Valuation Methods in Transport Planning, Oslo.
Hess, S. and Rose, J. M. (2012) Can scale and coefficient heterogeneity be separated in random coefficients models?, Transportation, 39 (6), 1225–1239.CrossRefGoogle Scholar
Hess, S., Rose, J. M. and Bain, S. (2010) Random scale heterogeneity in discrete choice models, Paper presented at the 89th Annual Meeting of the Transportation Research Board, Washington, DC.
Hess, S., Rose, J. M. and Hensher, D. A. (2008) Asymmetrical preference formation in willingness to pay estimates in discrete choice models, Transportation Research Part E, 44 (5), 847–863.CrossRefGoogle Scholar
Hess, S., Stathopoulos, A. and Daly, A. (2012) Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies, Transportation, 39 (3), 565–591.CrossRefGoogle Scholar
Hess, S., Train, K. E. and Polak, J. W. (2004) On the use of randomly shifted and shuffled uniform vectors in the estimation of the mixed logit model for vehicle choice, Paper presented at the 83rd Annual Meeting of the Transportation Research Board, Washington, DC.
Hess, S., Train, K. E. and Polak, J. W. (2006) On the use of a Modified Latin Hypercube Sampling (MLHS) approach in the estimation of a mixed logit model for vehicle choice, Transportation Research Part B, 40 (2), 147–163.CrossRefGoogle Scholar
Hole, A. R. (2011) A discrete choice model with endogenous attribute attendance, Economics Letters, 110 (3), 203–205.CrossRefGoogle Scholar
Hollander, Y. (2006) Direct versus indirect models for the effects of unreliability, Transportation Research Part A, 40 (9), 699–711.Google Scholar
Holmes, C. (1974) A statistical evaluation of rating scales, Journal of the Market Research Society, 16, 87–107.Google Scholar
Holt, C. A. and Laury, S. K. (2002) Risk aversion and incentive effects, American Economic Review, 92 (5), 1644–1655.CrossRefGoogle Scholar
Houston, D. A. and Sherman, S. J. (1995) Cancellation and focus: the role of shared and unique features in the choice process, Journal of Experimental Social Psychology, 31, 357–378.CrossRefGoogle Scholar
Houston, D. A., Sherman, S. J. and Baker, S. M. (1989) The influence of unique features and direction of comparison on preferences, Journal of Experimental Social Psychology, 25, 121–141.CrossRefGoogle Scholar
Howe, C. W. and Smith, M. G. (1994) The value of water supply reliability in urban water systems, Journal of Environmental Economics and Management, 26, 19–30. Available at: .CrossRefGoogle Scholar
Huber, J. and Zwerina, K. (1996) The importance of utility balance in efficient choice designs, Journal of Marketing Research, 33, 307–317.CrossRefGoogle Scholar
Hudson, D., Gallardo, K. and Hanson, T. (2006) Hypothetical (non)bias in choice experiments: evidence from freshwater prawns, Working Paper. Department of Agricultural Economics, Mississippi State University.
Hull, C. L. (1943) Principles of Behavior, Appleton-Century, New York.Google Scholar
Idson, L. C., Krantz, D. H., Osherson, D. and Bonini, N. (2001) The relation between probability and evidence judgment: an extension of support theory, Journal of Risk and Uncertainty, 22 (3), 227–249.CrossRefGoogle Scholar
Isacsson, G. (2007) The trade off between time and money: is there a difference between real and hypothetical choices?, Swedish National Road and Transport Research Institute, Borlange.Google Scholar
Ison, S. (1998) The saleability of urban road pricing, Economic Affairs, 18 (4), 21–25.CrossRefGoogle Scholar
Johannesson, M., Blomquist, G., Blumenschien, K., Johansson, P., Liljas, B. and O’Connor, R. (1999) Calibrating hypothetical willingness to pay responses, Journal of Risk and Uncertainty, 8 (1), 21–32.CrossRefGoogle Scholar
Johansson-Stenman, O. and Svedsäter, H. (2003) Self image and choice experiments: hypothetical and actual willingness to pay, Working Papers in Economics 94, Department of Economics, Gothenburg University.
John, J. A. and Draper, N. R. (1980) An alternative family of transformations, Applied Statistics, 29, 190–197.CrossRefGoogle Scholar
Johnson, F. R., Kanninen, B. J. and Bingham, M. (2006) Experimental design for stated choice studies, in Kanninen, B. J. (ed.) Valuing Environmental Amenities Using Stated Choice Studies: A Common Sense Approach to Theory and Practice, Springer, Dordrecht, 159–202.Google Scholar
Johnson, R. and Orme, B. (2003) Getting the most from CBC, Sawtooth Conference Paper, Sawtooth ART Conference, Beaver Creek.
Jones, B. D. (1999) Bounded rationality, Annual Review of Political Science, 2, 297–321.CrossRefGoogle Scholar
Jones, P. M. (1998) Urban road pricing: public acceptability and barriers to implementation, inButton, K. J. and Verhoef, E. T. (eds.), Road Pricing, Traffic Congestion and the Environment: Issues of Efficiency and Social Feasibility, Edward Elgar, Cheltenham, 263–284.Google Scholar
Jones, S. and Hensher, D. A. (2004) Predicting financial distress: a mixed logit model, Accounting Review, 79 (4), 1011–1038.CrossRefGoogle Scholar
Joreskog, K. G. and Goldberger, A. S. (1975) Estimation of a model with multiple indicators and multiple causes of a single latent variable, Journal of the American Statistical Association, 70 (351), 631–639.Google Scholar
Jou, R. (2001) Modelling the impact of pre-trip information on commuter departure time and route choice, Transportation Research Part B, 35 (10), 887–902.CrossRefGoogle Scholar
Jovicic, G. and Hansen, C. O. (2003) A passenger travel demand model for Copenhagen, Transportation Research Part A, 37 (4), 333–349.Google Scholar
Kahneman, D. and Tversky, A. (1979) Prospect theory: an analysis of decision under risk, Econometrica, 47 (2), 263–292.CrossRefGoogle Scholar
Kanninen, B. J. (2002) Optimal design for multinomial choice experiments, Journal of Marketing Research, 39, 214–217.CrossRefGoogle Scholar
Kanninen, B. J. (2005) Optimal design for binary choice experiments with quadratic or interactive terms, Paper presented at the 2005 International Health Economics Association conference, Barcelona.
Karmarkar, U. S. (1978) Weighted subjective utility: a descriptive extension of the expected utility model, Organizational Behavior and Human Performance, 21 (1), 61–72.CrossRefGoogle Scholar
Kates, R. W. (1979) The Australian experience: summary and prospect, in Heathcote, R. L. and Thom, B. G. (eds.), Natural Hazards in Australia, Australian Academy of Science, Canberra, 511–520.Google Scholar
Kaye-Blake, W. H., Abell, W. L. and Zellman, E. (2009) Respondents’ ignoring of attribute information in a choice modelling survey, Australian Journal of Agricultural and Resource Economics, 53, 547–564.CrossRefGoogle Scholar
Keane, M. (1990) Four essays in empirical macro and labor economics, PhD thesis, Brown University.
Keane, M. (1994) A computationally practical simulation estimator for panel data, Econometrica, 62 (1), 95–116.CrossRefGoogle Scholar
Keane, M. (2006) The generalized logit model: preliminary ideas on a research program, Presentation at Motorola–CenSoC Hong Kong Meeting, 22 October.
Keppel, G. and Wickens, D. W. (2004) Design and Analysis: A Researcher’s Handbook, Pearson Prentice Hall, Upper Saddle River, NJ, 4th edn.Google Scholar
Kessels, R., Bradley, B., Goos, P. and Vandebroek, M. (2009) An efficient algorithm for constructing Bayesian optimal choice designs, Journal of Business and Economic Statistics, 27 (2), 279–291.CrossRefGoogle Scholar
Kessels, R., Goos, P. and Vandebroek, M. (2006) A comparison of criteria to design efficient choice experiments, Journal of Marketing Research, 43, 409–419.CrossRefGoogle Scholar
King, D., Manville, M. and Shoup, D. (2007) The political calculus of congestion pricing, Transport Policy, 14 (2), 111–123.CrossRefGoogle Scholar
King, G., Murray, C., Salomon, J. and Tandon, A. (2004) Enhancing the validity and cross-cultural comparability of measurement in survey research, American Political Science Review, 98, 191–207.CrossRefGoogle Scholar
King, G. and Wand, J. (2007) Comparing incomparable survey responses: new tools for anchoring vignettes, Political Analysis, 15, 46–66.CrossRefGoogle Scholar
Kivetz, R., Netzer, O. and Srinivasan, V. (2004) Alternative models for capturing the compromise effect, Journal of Marketing Research, 41 (3), 237–257.CrossRefGoogle Scholar
Klein, R. and Spady, R. (1993) An efficient semiparametric estimator for discrete choice models, Econometrica, 61, 387–421.CrossRefGoogle Scholar
Knight, F. H. (1921) Risk, Uncertainty and Profit, University of Chicago Press.Google Scholar
Koss, P. and Sami Khawaja, M. (2001) The value of water supply reliability in California: a contingent valuation study, Water Policy, 3, 165–174.CrossRefGoogle Scholar
Krantz, D. H. (1991) From indices to mappings: the representational approach to measurement, in Brown, D. and Smith, E. (eds.), Frontiers of Mathematical Psychology: Essays in Honour of Clyde Coombs, Springer, New York, 1–52.Google Scholar
Krinsky, I. and Robb, A. L. (1986) On approximating the statistical properties of elasticities, Review of Economics and Statistics, 68, 715–719.CrossRefGoogle Scholar
Krosnick, J. A. and Schuman, H. (1988) Attitude intensity importance, and certainty and susceptibility to response effects, Journal of Personality and Social Psychology, 54 (6), 940–952.CrossRefGoogle Scholar
Kuehl, R. O. (1994) Statistical Principles of Research Design and Analysis, Duxbury Press, Belmont, CA.Google Scholar
Kuehl, R. O. (2000) Statistical Principles of Research Design and Analysis, Duxbury Press, Pacific Grove, CA, 2nd edn.Google Scholar
Kuhfeld, W. F., Tobias, R. D. and Garratt, M. (1994) Efficient experimental design with marketing research applications, Journal of Marketing Research, 21, 545–557.CrossRefGoogle Scholar
Ladenburg, J., Olsen, S. and Nielsen, R. (2007) Reducing hypothetical bias in choice experiments, Powerpoint presentation, Institute of Food and Resource Economics, University of Copenhagen.
Lam, S. H. and Xie, F. (2002) Transit path models that use revealed preference and stated preference data, Transportation Research Record, 1779, 58–65.CrossRefGoogle Scholar
Lampietti, J. (1999) Do husbands and wives make the same choices? Evidence from Northern Ethiopia, Economics Letters, 62, 253–260.CrossRefGoogle Scholar
Lancaster, K. J. (1966) A new approach to consumer theory, Journal of Political Economy, 74 (2), 132–157.CrossRefGoogle Scholar
Lancsar, E. and Louviere, J. J. (2006) Deleting ‘irrational’ responses from discrete choice experiments: a case of investigating or imposing preferences?, Health Economics, 15, 797–811.CrossRefGoogle ScholarPubMed
Landry, C. E. and List, J. A. (2007) Using ex ante approaches to obtain credible signals for value in contingent markets: evidence from the field, American Journal of Agricultural Economics, 89 (2), 420–429.CrossRefGoogle Scholar
Lattimore, P., Baker, J. and Witte, A. (1992) The influence of probability on risky choice – a parametric examination, Journal of Economic Behavior & Organization, 17 (3), 377–400.CrossRefGoogle Scholar
Laury, S. K. and Holt, C. A. (2000) Further reflections on prospect theory, Working Paper, Department of Economics, University of Virginia, Charlottesville, VA.
Lave, C. (1970) The demand for urban mass transit, Review of Economics and Statistics, 52, 320–323.CrossRefGoogle Scholar
Layton, D. and Hensher, D. A. (2010) Aggregation of common-metric attributes in preference revelation in choice experiments and implications for willingness to pay, Transportation Research Part D, 15 (7), 394–404.CrossRefGoogle Scholar
Lazari, A. G. and Anderson, D. A. (1994) Designs of discrete choice experiments for estimating both attribute and availability cross effects, Journal of Marketing Research, 31(3), 375–383.CrossRefGoogle Scholar
Lee, L. (1995) Asymptotic bias in simulated maximum likelihood estimation of discrete choice models, Econometric Theory, 11, 437–483.CrossRefGoogle Scholar
Leong, W. and Hensher, D. A. (2012) Embedding decision heuristics in discrete choice models: a review, Transport Reviews, 32 (3), 313–331.CrossRefGoogle Scholar
Levinson, D. (2010) Equity effects of road pricing: a review, Transport Reviews, 30 (1), 33–57.CrossRefGoogle Scholar
Li, Q. and Racine, J. (2007) Nonparametric Econometrics, Princeton University Press.Google Scholar
Li, Q. and Racine, J. (2010) Smooth varying-coefficient estimation and inference for qualitative and quantitative data, Econometric Theory, 26, 1607–1637.CrossRefGoogle Scholar
Li, Z. and Hensher, D. A. (2010) Toll roads in Australia: an overview of characteristics and accuracy of demand forecasts, Transport Reviews, 30 (5), 541–569.CrossRefGoogle Scholar
Li, Z. and Hensher, D. A. (2011) Prospect theoretic contributions in understanding traveller behaviour: a review and some comments, Transport Reviews, 31 (1), 97–117.CrossRefGoogle Scholar
Li, Z. and Hensher, D. A. (2012) Congestion charging and car use: a review of stated preference and opinion studies and market monitoring evidence, Transport Policy, 20, 47–61.CrossRefGoogle Scholar
Li, B. and Hensher, D. A. (2015) Choice analysis for risky prospects, submitted toJournal of Econometrics, 3 February.Google Scholar
Li, Z., Hensher, D. A. and Rose, J. M. (2010) Willingness to pay for travel time reliability in passenger transport: a review and some new empirical evidence, Transportation Research Part E, 46 (3), 384–403.CrossRefGoogle Scholar
Lindzey, G. (1954) The Handbook of Social Psychology, Vol. II, Addison-Wesley, Reading, MA.Google Scholar
Lisco, T. (1967) The value of commuters’ travel time: a study in urban transportation, PhD dissertation, University of Chicago.
List, J. A. (2001) Do explicit warnings eliminate the hypothetical bias in elicitation procedures?: evidence from field auctions for sportscards, American Economic Review, 91(5), 1498–1507.CrossRefGoogle Scholar
List, J. A. and Gallet, G. (2001) What experimental protocol influence disparities between actual and hypothetical stated values?, Environmental and Resource Economics, 20 (2), 241–254.CrossRefGoogle Scholar
List, J., Sinha, P. and Taylor, M. (2006) Using choice experiments to value non-market goods and services: evidence from field experiments, Advances in Economic Analysis and Policy, 6 (2), 1132–1132.Google Scholar
Liu, Y.-H. and Mahmassani, H. S. (2000) Global maximum likelihood estimation procedure for multinomial probit model parameters, Transportation Research Part B, Special Issue: Methodological Development in Travel Behaviour Research, 34B (5), 419–449.CrossRefGoogle Scholar
Long, J. S. (1997), Regression Models for Categorical and Limited Dependent Variables, Sage, New York.Google Scholar
Long, J. S. and Frees, J. (2006) Regression Models for Categorical and Limited Dependent Variables Using Stata, Stata Press, College Station, TX.Google Scholar
Louviere, J. J. (2003) Random utility theory-based stated preference elicitation methods: applications in health economics with special reference to combining sources of preference data, keynote address, Australian Health Economics Society Conference, Canberra.
Louviere, J. J., Carson, R. T., Ainslie, A., Cameron, T. A., DeShazo, J. R., Hensher, D. A., Kohn, R., Marley, T. and Street, D. J. (2002) Dissecting the random component of utility, Marketing Letters, 13, 177–193.CrossRefGoogle Scholar
Louviere, J. J. and Eagle, T. (2006) Confound it! That pesky little scale constant messes up our convenient assumptions, Proceedings of the 2006 Sawtooth Software Conference, Sawtooth Software, Sequem, Washington, DC, 211–218.
Louviere, J. J. and Hensher, D. A. (1982) On the design and analysis of simulated choice or allocation experiments in travel choice modelling, Transportation Research Record, 890, 11–17.Google Scholar
Louviere, J. J. and Hensher, D. A. (1983) Using discrete choice models with experimental design data to forecast consumer demand for a unique cultural event, Journal of Consumer Research, 10 (3), 348–361.CrossRefGoogle Scholar
Louviere, J. J. and Hensher, D. A. (2001) Combining preference data, in Hensher, D. A. (ed.), The Leading Edge of Travel Behaviour Research, Pergamon Press, Oxford, 125–144.Google Scholar
Louviere, J., Hensher, D. A. and Swait, J. (2000) Stated Choice Methods: Analysis and Applications, Cambridge University Press.CrossRefGoogle Scholar
Louviere, J. J. and Islam, T. (2008) A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best–worst scaling, Journal of Business Research, 61, 903–911.CrossRefGoogle Scholar
Louviere, J. J., Islam, T., Wasi, N., Street, D. and Burgess, L. (2008) Designing discrete choice experiments: do optimal designs come at a price?, Journal of Consumer Research, 35 (2), 360–375.CrossRefGoogle Scholar
Louviere, J. J. and Lanscar, E. (2009) Choice experiments in health: the good, the bad, and the ugly and toward a brighter future, Health Economics, Policy and Law, 4 (4), 527–546.CrossRefGoogle Scholar
Louviere, J. J., Lings, I., Islam, T., Gudergan, S. and Flynn, T. (2013) An introduction to the application of (case 1) best–worst scaling in marketing research, International Journal of Marketing Research, 30, 292–303.CrossRefGoogle Scholar
Louviere, J. J., Meyer, R. J., Bunch, D. S., Carson, R., Dellaert, B., Hanemann, W. A., Hensher, D. A. and Irwin, J. (1999) Combining sources of preference data for modelling complex decision processes, Marketing Letters, 10 (3), 205–217.CrossRefGoogle Scholar
Louviere, J., Oppewal, H., Timmermans, H. and Thomas, T. (2003) Handling large numbers of attributes in conjoint applications, Working Paper 3.
Louviere, J. J., Street, D., Burgess, L., Wasi, N., Islam, T. and Marley, A. A. J. (2008) Modelling the choices of individual decision makers by combining efficient choice experiment designs with extra preference information, Journal of Choice Modelling, 1(1), 128–163.CrossRefGoogle Scholar
Louviere, J. J. and Woodworth, G. (1983) Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data, Journal of Marketing Research, 20, 350–367.CrossRefGoogle Scholar
Luce, R. D. (1959) Individual Choice Behavior, Wiley, New York.Google Scholar
Luce, R. D. and Suppes, P. (1965) Preference, utility and subjective probability, in Luce, R. D., Bush, R. R. and Galanter, E. (eds.), Handbook of Mathematical Psychology, Vol. III, Wiley, New York.Google Scholar
Lui, Y. and Mahmassani, H. (2000) Global maximum likelihood estimation procedures for multinomial probit (MNP) model parameters, Transportation Research Part B, 34 (5), 419–444.Google Scholar
Lundhede, T. H., Olsen, S. B., Jacobsen, J. B. and Thorsen, B. J. (2009) Handling respondent uncertainty in choice experiments: evaluating recoding approaches against explicit modelling of uncertainty, Faculty of Life Sciences, University of Copenhagen.
Lusk, J. L. (2003) Willingness to pay for golden rice, American Journal of Agricultural Economics, 85 (4), 840–856.CrossRefGoogle Scholar
Lusk, J. L. and Norwood, F. B. (2005) Effect of experimental design on choice-based conjoint valuation estimates, American Journal of Agricultural Economics, 87 (3), 771–785.CrossRefGoogle Scholar
Lusk, J. and Schroeder, T. (2004) Are choice experiments incentive compatible? A test with quality differentiated beef steaks, American Journal of Agricultural Economics, 86 (2), 467–482.CrossRefGoogle Scholar
Maddala, G. S. (1983) Limited-Dependent and Qualitative Variables in Econometrics, Cambridge University Press.CrossRefGoogle Scholar
Maddala, T., Phillip, K. A. and Johnson, F. R. (2003) An experiment on simplifying conjoint analysis designs for measuring preferences, Health Economics, 12, 1035–1047.CrossRefGoogle Scholar
Malhotra, N. K. (1982) Information load and consumer decision making, Journal of Consumer Research, 8(4), 419–430.CrossRefGoogle Scholar
Manheim, C. F. (1973) Practical implications of some fundamental properties of travel demand models, Highway Research Record, 244, 21–38.Google Scholar
Manly, B. F. (1976) Exponential data transformation, The Statistician, 25, 37–42.CrossRefGoogle Scholar
Manski, C. F. (1977) The structure of random utility models, Theory and Decisions, 8, 229–254.CrossRefGoogle Scholar
Manski, C. F. and Lerman, S. R. (1977) The estimation of choice probabilities from choice-based samples, Econometrica, 45 (8), 1977–1988.CrossRefGoogle Scholar
Manski, C. F. and McFadden, D. (1981) Alternative estimators and sample designs for discrete choice analysis, in Manski, C. F. and McFadden, D. (eds.), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge, MA, 2–50.Google Scholar
Manville, M. and King, D. (2013) Credible commitment and congestion pricing, Transportation, 40, 229–250.CrossRefGoogle Scholar
Marcucci, E., Marini, M. and Ticchi, D. (2005) Road pricing as a citizen-candidate game, European Transport, 31, 28–45.Google Scholar
Marcucci, E., Stathopoulos, A., Rotaris, L. and Danielis, R. (2011) Comparing single and joint preferences: a choice experiment on residential location in three-member households, Environment and Planning Part A, 43 (5), 1209–1225.CrossRefGoogle Scholar
Marley, A. A. J. and Louviere, J. J. (2005) Some probabilistic models of best, worst, and best–worst choices, Journal of Mathematical Psychology, 49, 464–480.CrossRefGoogle Scholar
Marley, A. A. J. and Pihlens, D. (2012) Models of best–worst choice and ranking among multi-attribute options (profiles), Journal of Mathematical Psychology, 56, 24–34.CrossRefGoogle Scholar
Marschak, J. (1959) Binary choice constraints and random utility indicators, in Arrow, K. J. (ed.), Mathematical Methods in the Social Sciences, Stanford University Press.Google Scholar
McClelland, G. H. and Judd, C. M. (1993) The statistical difficulties of detecting interactions and moderator effects, Psychological Bulletin, 144 (2), 376–390.CrossRefGoogle Scholar
McFadden, D. (1968) The revealed preferences of a public bureaucracy, Department of Economics, University of California.
McFadden, D. (1974) Conditional logit analysis of qualitative choice behavior, in Zarembka, P. (ed.), Frontiers in Econometrics, Academic Press: New York, 105–142.Google Scholar
McFadden, D. (1981) Econometric models of probabilistic choice, in Manski, C. and McFadden, D. (eds.), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, Cambridge, MA, 198–272.Google Scholar
McFadden, D. (1984) Econometric analysis of qualitative response models, in Griliches, Z. and Intrilligator, M. (eds.), Handbook of Econometrics, Vol. II, Elsevier, Amsterdam.Google Scholar
McFadden, D. (1989) A method of simulated moments for estimation of discrete response models without numerical integration, Econometrica, 57 (5), 995–1026.CrossRefGoogle Scholar
McFadden, D. (1998) Measuring willingness-to-pay for transportation improvements, in Garling, T., Laitila, T. and Westin, K. (eds.), Theoretical Foundations of Travel Choice Modelling, Elsevier, Oxford, 339–364.Google Scholar
McFadden, D. (2001a) Disaggregate behavioural travel demand RUM side – a 30 years retrospective, in Hensher, D. A. (ed.), Travel Behaviour Research: The Leading Edge, Pergamon, Oxford, 17–64.Google Scholar
McFadden, D. (2001b) Economic choices, Nobel Lecture, December 2000, American Economic Review, 91 (3), 351–378.CrossRefGoogle Scholar
McFadden, D. and Train, K. (2000) Mixed MNL models for discrete response, Journal of Applied Econometrics, 15 (5), 447–470.3.0.CO;2-1>CrossRefGoogle Scholar
McNair, B. J., Bennett, J. and Hensher, D. A. (2010) Strategic response to a sequence of discrete choice questions, 54th Annual Conference of the Australian Agricultural and Resource Economics Society. Adelaide.
McNair, B. J., Bennett, J. and Hensher, D. A. (2011) A comparison of responses to single and repeated discrete choice questions, Resource and Energy Economics, 33, 544–571.CrossRefGoogle Scholar
McNair, B., Hensher, D. A. and Bennett, J. (2012) Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a probabilistic decision process model, Environment and Resource Economics, 51, 599–616.CrossRefGoogle Scholar
Meral, G. H. (1979) Local drought-induced conservation: California experiences, Proceedings of the Conference on Water Conservation: Needs and Implementing Strategies, American Society of Civil Engineers, New York.
Meyer, R. K. and Nachtsheim, C. J. (1995) The coordinate-exchange algorithm for constructing exact optimal experimental designs, Technometrics, 37 (1), 60–69.CrossRefGoogle Scholar
Mongin, P. (1997) Expected utility theory, in Davis, J., Hands, W. and Mäki, U. (eds.) Handbook of Economic Methodology, Edward Elgar, London, 342–350.Google Scholar
Morikawa, T. (1989) Incorporating stated preference data in travel demand analysis, PhD dissertation, Department of Civil Engineering, MIT.
Morikawa, T., Ben-Akiva, M. and McFadden, D. (2002) Discrete choice models incorporating revealed preferences and psychometric data, in Franses, P. H. and Montgomery, A. L. (eds.), Econometric Models in Marketing, Vol. 16, Elsevier, Amsterdam, 29–55.Google Scholar
Morokoff, W. J. and Caflisch, R. E. (1995) Quasi-Monte Carlo integration, Journal of Computational Physics, 122 (2), 218–230.CrossRefGoogle Scholar
Mundlak, Y. (1978) On the pooling of time series and cross sectional data, Econometrica, 56, 69–86.CrossRefGoogle Scholar
Murphy, J., Allen, P., Stevens, T. and Weatherhead, D. (2004) A meta-analysis of hypothetical bias in stated preference valuation, Department of Resource Economics, University of Massachusetts, Amherst, MA, January.
Murphy, J., Allen, P., Stevens, T. and Weatherhead, D. (2005) Is cheap talk effective at eliminating hypothetical bias in a provision point mechanism?, Environmental and Resource Economics, 30 (3), 313–325.CrossRefGoogle Scholar
Nelson, J. O. (1979) Northern California rationing lessons, Proceedings of the Conference on Water Conservation: Needs and Implementing Strategies, American Society of Civil Engineers, New York.
Niederreiter, H. (1992) Random number generation and quasi-Monte Carlo methods, CBMS-NFS Regional Conference Series in Applied Mathematics, 63, SIAM, Philadelphia, PA.
Noland, R. B. and Polak, J. W. (2002) Travel time variability: a review of theoretical and empirical issues, Transport Reviews, 22 (1), 39–93.CrossRefGoogle Scholar
Ohler, T., Li, A., Louviere, J. J. and Swait, J. (2000) Attribute range effects in binary response tasks, Marketing Letters, 11 (3), 249–260.CrossRefGoogle Scholar
Olshavsky, R. W. (1979) Task complexity and contingent processing in decision making: a replication and extension, Organizational Behavior and Human Performance, 24, 300–316.CrossRefGoogle Scholar
Orme, B. (1998) Sample size issues for conjoint analysis studies, Sawtooth Software Technical Paper.
Ortúzar, J. de D., Iacobelli, A. and Valeze, C. (2000) Estimating demand for a cycle-way network, Transportation Research Part A, 34 (5), 353–373.Google Scholar
Ortúzar, J. de D. and Willumsen, P. (2011) Transport Modelling, Wiley, New York, 4th edn.CrossRefGoogle Scholar
Pareto, V. (1906) Manuale di economia politica, con una introduzione alla scienza sociale, Societa Editrice Libraria, Milan.Google Scholar
Park, Y.-H., Ding, M. and Rao, V. (2008) Eliciting preference for complex products: a web-based upgrading method, Journal of Marketing Research, 45, 562–574.CrossRefGoogle Scholar
Paterson, R. W., Boyle, K. J., Parmeter, C. F., Beumann, J. E. and De Civita, P. (2008) Heterogeneity in preferences for smoking cessation, Health Economics, 17 (12), 1363–1377.CrossRefGoogle ScholarPubMed
Paulhus, D. L. (1991) Measurement and control of response bias, in Robinson, P. J., Shaver, P. R. and Wrightsman, L. S. (eds.), Measures of Personality and Social Psychological Attitudes, Academic Press, San Diego, CA, 17–59.CrossRefGoogle Scholar
Payne, J. D. (1972) The effects of reversing the order of verbal rating scales in a postal survey, Journal of the Market Research Society, 14, 30–44.Google Scholar
Payne, J. W. (1976) Task complexity and contingent processing in decision making: an information search and protocol analysis, Organizational Behavior and Human Performance, 16, 366–387.CrossRefGoogle Scholar
Payne, J. W. and Bettman, J. R. (1992) Behavioural decision research: a constructive processing perspective, Annual Review of Psychology, 43, 87–131.CrossRefGoogle Scholar
Payne, J. W., Bettman, J. R. and Johnson, E. J. (1993) The Adaptive Decision Maker, Cambridge University Press.CrossRefGoogle Scholar
Payne, J. W., Bettman, J. R. and Schkade, D. A. (1999) Measuring constructed preferences: towards a building code, Journal of Risk and Uncertainty, 19, 243–270.CrossRefGoogle Scholar
Peeta, S., Ramos, J. L. and Pasupathy, R. (2000) Content of variable message signs and on-line driver behavior, Transportation Research Record, 1725, 102–103.CrossRefGoogle Scholar
Peirce, C. S. (1876) Note on the Theory of the Economy of Research, Coast Survey Report, 197–201.Google Scholar
Pendyala, R. and Bricka, S. (2006). Collection and analysis of behavioural process data: challenges and opportunities, in Stopher, P. and Stecher, C. (eds.), Travel Survey Methods: Quality and Future Directions, Elsevier, Oxford.Google Scholar
Peters, R. P. and Kramer, J. (2012) Just who should pay for what? Vertical equity, transit subsidy and broad pricing: the case of New York City, Journal of Public Transportation, 15 (2), 117–136.CrossRefGoogle Scholar
Poe, G., Giraud, K. and Loomis, J. (2005) Simple computational methods for measuring the difference of empirical distributions: application to internal and external scope tests in contingent valuation, American Journal of Agricultural Economics, 87 (2), 353–365.CrossRefGoogle Scholar
Polak, J. (1987) A more general model of individual departure time choice transportation planning methods, Proceedings of Seminar C held at the PTRC Summer Annual Meeting, P290, 247–258.
Polak, J., Hess, S. and Liu, X. (2008) Characterising heterogeneity in attitudes to risk in expected utility models of mode and departure time choice, Paper presented at the Transportation Research Board (TRB) 87th Annual Meeting, Washington, DC.
Portney, P. R. (1994) The contingent valuation debate: why economists should care, Journal of Economic Perspectives, 8 (4), 3–17.CrossRefGoogle Scholar
Powers, D. A. and Xie, Y. (2000) Statistical Methods for Categorical Data Analysis, Academic Press, New York.Google Scholar
Powers, E. A., Morrow, P., Goudy, W. J. and Keith, P. (1977) Serial order preference in survey research, Public Opinion Quarterly, 41 (1), 80–85.CrossRefGoogle Scholar
Prato, C. G., Bekhor, S. and Pronello, C. (2012) Latent variables and route choice behaviour, Transportation, 39 (3), 299–319.CrossRefGoogle Scholar
Pratt, J. (1981) Concavity of the log likelihood, Journal of the American Statistical Association, 76, 103–106.CrossRefGoogle Scholar
Prelec, D. (1998) The probability weighting function, Econometrica, 66 (3), 497–527.CrossRefGoogle Scholar
Puckett, S. M. and Hensher, D. A. (2009) Revealing the extent of process heterogeneity in choice analysis: an empirical assessment, Transportation Research Part A, 43 (2), 117–126.Google Scholar
Puckett, S. M. and Hensher, D. A. (2006) Modelling interdependent behaviour utilising a sequentially-administered stated choice experiment: analysis of urban road freight stakeholders, Conference for the International Association of Transport Behaviour Research, Kyoto.
Puckett, S. M. and Hensher, D. A. (2008) The role of attribute processing strategies in estimating the preferences of road freight stakeholders under variable road user charges, Transportation Research Part E, 44, 379–395.CrossRefGoogle Scholar
Puckett, S. M., Hensher, D. A., Rose, J. M. and Collins, A., (2007) Design and development of a stated choice experiment for interdependent agents: accounting for interactions between buyers and sellers of urban freight services, Transportation, 34 (4), 429–451.CrossRefGoogle Scholar
Pudney, S. and Shields, M. (2000) Gender, race, pay and promotion in the British nursing profession: estimation of a generalized ordered probit model, Journal of Applied Econometrics, 15 (4), 367–399.3.0.CO;2-Z>CrossRefGoogle Scholar
Pullman, M. E., Dodson, K. J. and Moore, W. L. (1999) A comparison of conjoint methods when there are many attributes, Marketing Letters, 10, 1–14.CrossRefGoogle Scholar
Quan, W., Rose, J. M., Collins, A. T. and Bliemer, M. C. J. (2011) A comparison of algorithms for generating efficient choice experiments, Working Paper ITLS-WP-11-19, Institute of Transport and Logistics Studies, University of Sydney.
Quandt, R. E. (1970) The Demand for Travel: Theory and Measurement, D.C. Heath, Lexington, MA.Google Scholar
Quiggin, J. (1982) A theory of anticipated utility, Journal of Economic Behavior and Organization, 3 (4), 323–343.CrossRefGoogle Scholar
Quiggin, J. (1994) Regret theory with general choice sets, Journal of Risk and Uncertainty, 8 (2), 153–165.CrossRefGoogle Scholar
Quiggin, J. (1995) Regret theory with general choice sets, Risk and Uncertainty, 8 (2), 153–165.CrossRefGoogle Scholar
Quiggin, J. (1998) Individual and household willingness to pay for public goods, American Journal of Agricultural Economics, 80, 58–63.CrossRefGoogle Scholar
Racevskis, L. and Lupi, F. (2008) Incentive compatibility in an attribute-based referendum model, Paper presented at the American Agricultural Economics Association Annual Meeting, Orlando, FL, 27–29 July.
Rasch, G. (1960) Probabilistic Models for Some Intelligence and Attainment Tests, Denmark Paedogiska, Copenhagen.Google Scholar
Rasouli, S. and Timmermans, H. (2014) Specification of regret-based models of choice behavior: formal analyses and experimental design based evidence, Eindhoven University of Technology.
Restle, F. (1961) Psychology of Judgment and Choice, Wiley, New York.Google Scholar
Revelt, D. and Train, K. (1998) Mixed logit with repeated choices: households’ choices of appliance efficiency level, Review of Economics and Statistics, 80 (4), 647–657.CrossRefGoogle Scholar
Riedl, R., Brandstatter, E. and Roithmayr, F. (2008). Identifying decision strategies: a process- and outcome-based classification method, Behavior Research Methods, 40 (3): 795–807.CrossRefGoogle ScholarPubMed
Riphahn, R., Wambach, A. and Million, A. (2003) Incentive effects in the demand for health care: a bivariate panel count estimation, Journal of Applied Econometrics, 18 (4), 387–405.CrossRefGoogle Scholar
Roberts, J. A., Hann, L.-H. and Slaughter, S. A. (2006) Understanding the motivations, participation, and performance of open source software development: a longitudinal study of the Apache Projects, Management Science, 52 (7), 984–999.CrossRefGoogle Scholar
Roeder, K., Lynch, K. and Nagin, D. (1999) Modeling uncertainty in latent class membership: a case study in criminology, Journal of the American Statistical Association, 94, 766–776.CrossRefGoogle Scholar
Rose, J. M. (2014) Interpreting discrete choice models based on best–worst data: a matter of framing, 93rd Annual Meeting of the Transportation Research Board TRB 2014, Washington DC, 16 January.
Rose, J. M., Bain, S. and Bliemer, M. C. J. (2011) Experimental design strategies for stated preference studies dealing with non market goods, in Bennett, J. (ed.), International Handbook on Non-Marketed Environmental Valuation, Edward Elgar, Cheltenham, 273–299.Google Scholar
Rose, J. M., Bekker de-Grob, E. and Bliemer, E. (2012) If theoretical framework matters, then why are we ignoring their tenants? An (re)examination of random utility theory and beyond, Working Paper, Institute of Transport and Logistics Studies, The University of Sydney, November.
Rose, J. M. and Black, I. (2006) Means matter, but variances matter too: decomposing response latency influences on variance heterogeneity in stated preference experiments, Marketing Letters, 17 (4), 295–310.CrossRefGoogle Scholar
Rose, J. M. and Bliemer, M. C. J. (2004) The design of stated choice experiments: the state of practice and future challenges, Working Paper ITS-WP-04-09, Institute of Transport and Logistics Studies, University of Sydney.
Rose, J. M. and Bliemer, M. C. J. (2005) Sample optimality in the design of stated choice experiments, Report ITLS-WP-05-13, Institute of Transport and Logistics Studies, University of Sydney.
Rose, J. M. and Bliemer, M. C. J. (2006) Designing efficient data for stated choice experiments, Proceedings of the 11th International Conference on Travel Behaviour Research, Kyoto.
Rose, J. M. and Bliemer, M. C. J. (2008) Stated preference experimental design strategies, in Hensher, D. A. and Button, K. J. (eds), Handbook of Transport Modelling, Elsevier, Oxford, 151–179.Google Scholar
Rose, J. M. and Bliemer, M. C. J. (2009) Constructing efficient stated choice experimental designs, Transport Reviews, 29 (5), 587–617.CrossRefGoogle Scholar
Rose, J. M. and Bliemer, M. C. J. (2011) Stated preference experimental design strategies, in Hensher, D. A. (ed.), Transport Economics: Critical Concepts in Economics, Vol. 1, Routledge, Oxford, 304–332.Google Scholar
Rose, J. M. and Bliemer, M. C. J. (2012) Sample optimality in the design of stated choice experiments, in Pendyala, R. and Bhat, C. (eds), Travel Behaviour Research in the Evolving World, IATBR, Jaipur, 119–145.Google Scholar
Rose, J. M. and Bliemer, M. C. J. (2013) Sample size requirements for stated choice experiments, Transportation, 40 (5), 1021–1041.CrossRefGoogle Scholar
Rose, J. M. and Bliemer, M. C. J. (2014) Stated choice experimental design theory: the who, the what and the why, in Hess, S. and Daly, A. (eds.), Handbook of Choice Modelling, Edward Elgar, Cheltenham.Google Scholar
Rose, J., Bliemer, M., Hensher, D. A. and Collins, A. (2008) Designing efficient stated choice experiments in the presence of reference alternatives, Transportation Research Part B, 42 (4), 395–406.CrossRefGoogle Scholar
Rose, J. and Hensher, D. A. (2004) Modelling agent interdependency in group decision making: methodological approaches to interactive agent choice experiments, Transportation Research Part E, 40 (1), 63–79.CrossRefGoogle Scholar
Rose, J. and Hensher, D. A. (2014) Tollroads are only part of the overall trip: the error of our ways in past willingness to pay studies, Transportation, 41 (4), 819–837.CrossRefGoogle Scholar
Rose, J., Hensher, D. A. and Greene, W. (2005) Recovering costs through price and service differentiation: accounting for exogenous information on attribute processing strategies in airline choice, Journal of Air Transport Management, 11, 400–407.CrossRefGoogle Scholar
Rose, J. M., Scarpa, R. and Bliemer, M. C. J. (2009) Incorporating model uncertainty into the generation of efficient stated choice experiments: a model averaging approach, International Choice Modelling Conference, March 30-April 1, Harrogate.
Rumelhart, D. L. and Greeno, J. G. (1968) Choice between similar and dissimilar objects: an experimental test of the Luce and Restle choice models, presented at the Midwestern Psychological Association meeting, Chicago, May.
Russell, C. S., Arey, D. G. and Kates, R. W. (1970) Drought and Water Supply: Implications of the Massachusetts Experience for Municipal Planning, Johns Hopkins University Press for Resources for the Future, Inc., Baltimore, MD.Google Scholar
Russo, J. E. and Dosher, B. A. (1983) Strategies for multiattribute binary choice, Journal of Experimental Psychology: Learning, Memory, & Cognition, 9 (4), 676–696.Google ScholarPubMed
Sakia, R. M. (1992) The Box–Cox transformation technique: a review, The Statistician, 41 (2), 169–178.CrossRefGoogle Scholar
Sándor, Z. and Train, K. (2004), Quasi-random simulation of discrete choice models, Transportation Research Part B, 38 (4), 313–327.CrossRefGoogle Scholar
Sándor, Z. and Wedel, M. (2002) Profile construction in experimental choice designs for mixed logit models, Marketing Science, 21 (4), 455–475.CrossRefGoogle Scholar
Sándor, Z. and Wedel, M. (2005) Heterogeneous conjoint choice designs, Journal of Marketing Research, 42, 210–218.CrossRefGoogle Scholar
Sándor, Z. and Wedel, M. (2001) Designing conjoint choice experiments using managers’ prior beliefs, Journal of Marketing Research, 36, 430–444.CrossRefGoogle Scholar
Savage, L. J. (1954) The Foundations of Statistics, Wiley, London.Google Scholar
Scarpa, R., Campbell, D. and Hutchinson, G. (2005) Individual benefit estimates for rural landscape improvements: the role of sequential Bayesian design and response rationality in a choice study, Paper presented at the 14th Annual Conference of the European Association of Environmental and Resource Economics, Bremen.
Scarpa, R., Campbell, D. and Hutchinson, G. (2007) Benefit estimates for landscape improvements: sequential Bayesian design and respondents’ rationality in a choice experiment study, Land Economics, 83 (4), 617–634.CrossRefGoogle Scholar
Scarpa, R., Ferrini, S. and Willis, K. G. (2005) Performance of error component models for status-quo effects in choice experiments, in Scarpa, R., Ferrini, S. and Willis, K. G. (eds.), Applications of Simulation Methods in Environmental and Resource Economics, Springer, Dordrecht, 247–274.CrossRefGoogle Scholar
Scarpa, R., Gilbride, T. J., Campbell, D. and Hensher, D. A. (2009) Modelling attribute non-attendance in choice experiments for rural landscape valuation, European Review of Agricultural Economics, 36 (2), 151–174.CrossRefGoogle Scholar
Scarpa, R. and Rose, J. M. (2008) Designs efficiency for non-market valuation with choice modelling: how to measure it, what to report and why, Australian Journal of Agricultural and Resource Economics, 52 (3), 253–282.CrossRefGoogle Scholar
Scarpa, R., Thiene, M. and Hensher, D. A. (2010) Monitoring choice task attribute attendance in non-market valuation of multiple park management services: does it matter?, Land Economics, 86 (4), 817–839, Waikato Management School, The University of Waikato.CrossRefGoogle Scholar
Scarpa, R., Thiene, M. and Hensher, D. A. (2012) Preferences for tap water attributes within couples: an exploration of alternative mixed logit parameterizations, Water Resources Research Journal, 48 (1), 1–11, .CrossRefGoogle Scholar
Scarpa, R., Thiene, M. and Marangon, F. (2008) Using flexible taste distributions to value collective reputation for environmentally-friendly production methods, Canadian Journal of Agricultural Economics, 56, 145–162.CrossRefGoogle Scholar
Scarpa, R., Thiene, M. and Train, K. (2008) Utility in willingness to pay space: a tool to address confounding random scale effects in destination choice to the Alps, American Journal of Agricultural Economics, 90 (4), 994–1010. (See also Appendix: Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps. Available at: .).CrossRefGoogle Scholar
Scarpa, R., Willis, K. G. and Acutt, M. (2004) Individual-specific welfare measures for public goods: a latent class approach to residential customers of Yorkshire Water, in Koundouri, P. (ed.), Econometrics Informing Natural Resource Management, Edward Elgar, Cheltenham.Google Scholar
Schade, J. and Baum, M. (2007) Reactance or acceptance? Reactions towards the introduction of road pricing, Transportation Research Part A, 41 (1), 41–48.Google Scholar
Schade, J. and Schlag, B. (eds.) (2003) Acceptability of Transport Pricing Strategies, Elsevier, Oxford.CrossRef
Schwanen, T. and Ettema, D. (2009) Coping with unreliable transportation when collecting children: examining parents’ behavior with cumulative prospect theory, Transportation Research Part A, 43 (5), 511–525.Google Scholar
Senna, L. A. D. S. (1994) The influence of travel time variability on the value of time, Transportation, 21 (2), 203–228.CrossRefGoogle Scholar
Seror, V. (2007) Fitting observed and theoretical choices – women’s choices about prenatal diagnosis of Down syndrome, Health Economics, 14 (2), 161–167.Google Scholar
Shafer, G. (1976) A Mathematical Theory of Evidence, Princeton University Press.Google Scholar
Sillano, M. and Ortúzar, J. de D. (2005) Willingness-to-pay estimation with mixed logit models: some new evidence, Environment and Planning A, 37 (5), 525–550.CrossRefGoogle Scholar
Simon, H. (1978) Rational decision making in organisations, American Economic Review, 69 (4), 493–513.Google Scholar
Simonson, I. and Tversky, A. (1992) Choice in context: tradeoff contrast and extremeness aversion, Journal of Marketing Research, 29 (3): 281–295.CrossRefGoogle Scholar
Slovic, P. (1987) Perception of risk, Science, New Series, 236 (4799), 280–285.Google ScholarPubMed
Slovic, P. (1995) The construction of preference, American Psychologist, 50, 364–371.CrossRefGoogle Scholar
Small, K. A. (1992) Using the revenues from congestion pricing, Transportation, 19 (3), 359–381.CrossRefGoogle Scholar
Small, K. A., Noland, R. B., Chu, X. and Lewis, D. (1999) Valuation of travel-time savings and predictability in congested conditions for highway user-cost estimation, NCHRP Report 431, Transportation Research Board, National Research Council.
Smith, V. K. and Van Houtven, G. (1998) Non-market valuation and the household, Resources for the Future, Discussion Paper 98–31, Washington, DC.
Sobol, I. M. (1967) Distribution of points in a cube and approximate evaluation of integrals, USSR Computational Mathematics and Mathematical Physics, 7 (4), 784–802.CrossRefGoogle Scholar
Sonnier, G., Ainslie, A. and Otter, T. (2003) The influence of brand image and product style on consumer brand valuations, Working Paper, Anderson Graduate School of Management, University of California, Los Angeles, CA.
Sonnier, G., Ainslie, A. and Otter, T. (2007) Heterogeneity distributions of willingness-to-pay in choice models, Quantitative Marketing Economics, 5 (3), 313–331.CrossRefGoogle Scholar
Starmer, C. (2000) Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk, Journal of Economic Literature, 38, 332–382.CrossRefGoogle Scholar
Starmer, C. and Sugden, R. (1993) Testing for juxtaposition and event splitting effects, Journal of Risk and Uncertainty, 6, 235–254.CrossRefGoogle Scholar
Steimetz, (2008) Defensive driving and the external costs of accidents and travel delays, Transportation Research Part B, 42 (9), 703–724.CrossRefGoogle Scholar
Steimetz, S. and Brownstone, D. (2005) Estimating commuters’ ‘value of time’ with noisy data: a multiple imputation approach, Transportation Research Part B, 39 (7), 565–591.CrossRefGoogle Scholar
Stewart, M. B. (2004) A comparison of semiparametric estimators for the ordered response model, Computational Statistics and Data Analysis, 49, 555–573.CrossRefGoogle Scholar
Stewart, N., Chater, N., Stott, H. P. and Reimers, S. (2003) Prospect relativity: how choice options influence decision under risk, Journal of Experimental Psychology: General, 132, 23–46.CrossRefGoogle ScholarPubMed
Stopher, P. R. and Lisco, T. (1970) Modelling travel demand: a disaggregate behavioral approach, issues and applications, Transportation Research Forum Proceedings, 195–214.Google Scholar
Stott, H. P. (2006) Cumulative prospect theory’s functional menagerie, Journal of Risk and Uncertainty, 32 (2), 101–130.CrossRefGoogle Scholar
Street, D., Bunch, D. and Moore, B. (2001) Optimal designs for 2k paired comparison experiments, Communications in Statistics – Theory and Method, 30, 2149–2171.CrossRefGoogle Scholar
Street, D. J. and Burgess, L. (2004) Optimal and near-optimal pairs for the estimation of effects in 2-level choice experiments, Journal of Statistical Planning and Inference, 118, 185–199.CrossRefGoogle Scholar
Street, D. J., Burgess, L. and Louviere, J. J. (2005) Quick and easy choice sets: contructing optimal and nearly optimal stated choice experiments, International Journal of Research in Marketing, 22, 459–470.CrossRefGoogle Scholar
Sugden, R. (2005) Anomalies and stated preference techniques: a framework for a discussion of coping strategies, Environmental and Resource Economics, 32, 1–12.CrossRefGoogle Scholar
Sundstrom, G. A. (1987) Information search and decision making: the effects of information displays, Acta Psychologica, 65, 165–179.CrossRefGoogle Scholar
Svenson, O. (1998) The perspective from behavioral decision theory on modelling travel choice, in Garling, T., Laitila, T. and Westin, K. (eds.), Theoretical Foundations of Travel Choice Modelling, Elsevier, Oxford, 141–172.Google Scholar
Svenson, O. and Malmsten, N. (1996) Post-decision consolidation over time as a function of gain or loss of an alternative, Scandinavian Journal of Psychology, 37 (3), 302–311.CrossRefGoogle Scholar
Swait, J. (1994) A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data, Journal of Retail and Consumer Services, 1 (2), 77–89.CrossRefGoogle Scholar
Swait, J. (2001) A non-compensatory choice model incorporating attribute cut-offs, Transportation Research Part B, 35 (10), 903–928.CrossRefGoogle Scholar
Swait, J. (2009) Choice models based on mixed discrete/continuous PDFs, Transportation Research Part B, 43 (7), 766–783.CrossRefGoogle Scholar
Swait, J. and Adamowicz, W. (1996) The effect of choice environment and task demands on consumer behaviour: discriminating between contribution and confusion, Working Paper, Department of Rural Economy, University of Alberta.
Swait, J. and Adamowicz, W. (2001a) The influence of task complexity on consumer choice: a latent class model of decision strategy switching, Journal of Consumer Research, 28, 135–148.CrossRefGoogle Scholar
Swait, J. and Adamowicz, W. (2001b) Choice environment, market complexity, and consumer behavior: a theoretical and empirical approach for incorporating decision complexity into models of consumer choice, Organizational Behavior and Human Decision Processes, 49, 1–27.Google Scholar
Swait, J., Adamowicz, W. and van Buren, M. (2004) Choice and temporal welfare impacts: incorporating history into discrete choice models, Journal of Environmental Economics and Management, 47, 94–116.CrossRefGoogle Scholar
Swait, J. and Louviere, J. J. (1993) The role of the scale parameter in the estimation and use of multinomial logit models, Journal of Marketing Research, 30, 305–314.CrossRefGoogle Scholar
Swait, J., Louviere, J. J. and Williams, M. (1994) A sequential approach to exploiting the combined strengths of SP and RP data: application to freight shipper choice, Transportation, 21, 135–152.CrossRefGoogle Scholar
Swait, J. and Stacey, E. C. (1996) Consumer brand assessment and assessment confidence in models of longitudinal choice behavior, Paper presented at the 1996 INFORMS Marketing Science Conference, March 7–10, Gainesville, FL.
Temme, D., Paulssen, M. and Dannewald, T. (2008) Incorporating latent variables into discrete choice models: a simultaneous estimation approach using SEM software, Business Research, 1, 220–237.CrossRefGoogle Scholar
Terza, J. (1985) Ordinal probit: generalisation, Communications in Statistics – Theory and Methods, 14 (1), 1–11.Google Scholar
Thaler, R. (1999) Mental accounting matters, Journal of Behavioral Decision Making, 12, 183–206.3.0.CO;2-F>CrossRefGoogle Scholar
Thiene, M. and Scarpa, R. (2009) Deriving and testing efficient estimates of WTP distributions in destination choice models, Environmental and Resource Economics, 44, 379–395.CrossRefGoogle Scholar
Thurstone, L. L. (1927) A law of comparative judgment, Psychological Review, 34, 278–286.CrossRefGoogle Scholar
Thurstone, L. L. (1945) The prediction of choice, Psychometrika, 10, 237–253.CrossRefGoogle Scholar
Toner, J. P., Clark, S. D., Grant-Muller, S. M. and Fowkes, A. S. (1998) Anything you can do, we can do better: a provocative introduction to a new approach to stated preference design, WCTR Proceedings, 107–120, Antwerp.
Toner, J. P., Wardman, M. and Whelan, G. (1999) Testing recent advances in stated preference design, Proceedings of the European Transport Conference, Cambridge.
Train, K. (1978) A validation test for a disaggregate mode choice model, Transportation Research, 12, 167–173.CrossRefGoogle Scholar
Train, K. (1997) Mixed logit models for recreation demand, in Kling, C. and Herriges, J. (eds.), Valuing the Environment Using Recreation Demand Models, Edward Elgar, New York.Google Scholar
Train, K. (2000) Halton sequences for mixed logit, Working Paper, Department of Economics, University of California, Berkeley, CA.
Train, K. (2003, 2009) Discrete Choice Methods with Simulation, Cambridge University Press.CrossRefGoogle Scholar
Train, K. and Revelt, D. (2000) Customer-specific taste parameters and mixed logit, Working Paper, Department of Economics, University of California, Berkeley. Available at: .
Train, K. and Weeks, M. (2005) Discrete choice models in preference space and willing to-pay space, in Scarpa, R. and Alberini, A. (eds.), Applications of Simulation Methods in Environmental and Resource Economics, Springer, Dordrecht, 1–16.Google Scholar
Train, K. and Wilson, W. (2008) Estimation on stated-preference experiments constructed from revealed-preference choice, Transportation Research Part B, 40 (2), 191–203.CrossRefGoogle Scholar
Truong, T. P. and Hensher, D. A. (2012) Linking discrete choice to continuous demand models within a computable general equilibrium framework, Transportation Research Part B, 46 (9), 1177–1201.CrossRefGoogle Scholar
Tuffin, B. (1996) On the use of low-discrepancy sequences in Monte Carlo methods, Monte Carlo Methods and Applications, 2 (4), 295–320.CrossRefGoogle Scholar
Tukey, J. W. (1957) The comparative anatomy of transformations, Annals of Mathematical Statistics, 28, 602–632.CrossRefGoogle Scholar
Tukey, J. W. (1962) The future of data analysis, Annals of Mathematical Statistics, 33 (1), 13.Google Scholar
Tversky, A. and Fox, C. (1995) Weighing risk and uncertainty, Psychological Reviews, 102 (2), 269–283.CrossRefGoogle Scholar
Tversky, A. and Kahneman, D. (1981) The framing of decisions and the psychology of choice, Science, 211 (4), 453–458.CrossRefGoogle Scholar
Tversky, A. and Kahneman, D. (1992) Advances in prospect theory: cumulative representations of uncertainty, Journal of Risk and Uncertainty, 5 (4), 297–323.CrossRefGoogle Scholar
Tversky, A. and Koehler, D. (1994) Support theory: a nonextensional representation of subjective probability, Psychological Review, 1010, 547–567.CrossRefGoogle Scholar
Tversky, A. and Simonson, I. (1993) Context-dependent preferences, Management Science, 39 (10), 1179–89.CrossRefGoogle Scholar
Tversky, A., Slovic, P. and Kahneman, D. (1990) The causes of preference reversal, American Economic Review, 80 (1), 204–217.Google Scholar
Ubbels, B. and Verhoef, E. (2006) Acceptability of road pricing and revenue use in the Netherlands, European Transport/Trasporti Europei, 32, 69–94.Google Scholar
Uebersax, J. (1999) Probit latent class analysis with dichotomous or ordered category measures: conditional independence/dependence models, Applied Psychological Measurement, 23, 283–297.CrossRefGoogle Scholar
Van Amelsfort, D. H. and Bliemer, M. C. J. (2005) Uncertainty in travel conditions related travel time and arrival time: some findings from a choice experiment, Proceedings of the European Transport Conference (ETC), Strassbourg.
van de Kaa, E. J. (2008) Extended prospect theory, TRAIL Research School, Delft.Google Scholar
Verlegh, P. W., Schifferstein, H. N. and Wittink, D. R. (2002) Range and number-of-levels effects in derived and stated measures of attribute importance, Marketing Letters, 13, 41–52.CrossRefGoogle Scholar
Vermeulen, B., Goos, P. and Vandeboek, M. (2008) Models and optimal designs for conjoint choice experiments including a no-choice option, International Journal of Research in Marketing, 25 (2), 94–103.CrossRefGoogle Scholar
Vermuelen, F. (2002) Collective household models: principles and main results, Journal of Economic Surveys, 16, 533–564.CrossRefGoogle Scholar
Viney, R., Savage, E. and Louviere, J. J. (2005) Empirical investigation of experimental design properties of discrete choice experiments, Health Economics, 14 (4), 349–362.CrossRefGoogle ScholarPubMed
von Neumann, J. and Morgenstern, O. (1947) Theory of Games and Economic Behavior, Princeton University Press, 2nd edn.Google Scholar
Vuong, Q. H. (1989) Likelihood ratio tests for model selection and non-nested hypotheses, Econometrica, 57 (1989), 307–333.CrossRefGoogle Scholar
Wakker, P. P. (2008) Explaining the characteristics of the power (CRRA) utility family, Health Economics, 17 (12), 1329–1344.CrossRefGoogle ScholarPubMed
Walker, J. L. and Ben-Akiva, M. E. (2002) Generalized random utility model, Math Soc. Sc., 43 (3), 303–343.CrossRefGoogle Scholar
Walker, J. L., Ben-Akiva, M. and Bolduc, D. (2007) Identification of parameters in normal error component logit-mixture (NECLM) models, Journal of Applied Econometrics, 22 (6), 1095–1125.CrossRefGoogle Scholar
Wang, X. and Hickernell, F. J. (2000) Randomized Halton sequences, Mathematical and Computer Modelling, 32 (7–8), 887–899.CrossRefGoogle Scholar
Wardman, M. (2001) A review of British evidence on time and service quality valuations, Transportation Research Part E, 37 (2–3), 107–128.CrossRefGoogle Scholar
Watson, S. M., Toner, J. P., Fowkes, A. S. and Wardman, M. R. (2000) Efficiency properties of orthogonal stated preference designs, in Ortúzar, J. de D. (ed.), Stated Preference Modelling Techniques, PTRC Education and Research Services Ltd, 91–101.Google Scholar
Williams, H. C. W. L. (1977) On the formation of travel demand models and economic evaluation measures of user benefit, Environment and Planning Part A, 9 (3), 285–344.CrossRefGoogle Scholar
Williams, R. (2006) Generalized ordered logit/partial proportional odds models for ordinal dependent variables, Stata Journal, 6 (1), 58–82.Google Scholar
Wilson, A. G., Hawkins, A. F., Hill, G. J. and Wagon, D. J. (1969) Calibrating and testing of the SELNEC transport model, Regional Studies, 3(3), 340–345.CrossRefGoogle Scholar
Winiarski, M. (2003) Quasi-Monte Carlo derivative valuation and reduction of simulation bias, MSc Thesis, Royal Institute of Technology (KTH), Stockholm.
Wong, S. K. M., and Wang, Z. W. (1993) Qualitative measures of ambiguity, in Hackerman, D. and Mamdani, A. (eds.), Proceedings of The Ninth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, 443–450.CrossRefGoogle Scholar
Wooldridge, J. (2010) Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, MA.Google Scholar
Yanez, M. F., Raveau, S., Rojas, M. and Ortúzar, J. de D. (2009) Modelling and forecasting with latent variables in discrete choice panel models, Proceedings of the European Transport Conference, Noordwijk Conference Centre, Leeuwenhorst.
Yanez, M. F., Bahamonde-Birke, F., Raveau, S. and Ortúzar, J. de D. (2010) The role of tangible attributes in hybrid discrete choice models, Proceedings of the European Transport Conference, Glasgow.
Yanez, M. F., Raveau, S. and Ortúzar, J. de D. (2010). Inclusion of latent variables in mixed logit models: modelling and forecasting, Transportation Research Part A: Policy and Practice, 44 (9), 744–753.Google Scholar
Yoon, S.-O. and Simonson, I. (2008) Choice set configuration as a determinant of preference attribution and strength, Journal of Consumer Research, 35, 324–336.CrossRefGoogle Scholar
Yu, J., Goos, P. and Vandeboek, M. (2006) The importance of attribute interactions in conjoint choice design and modeling, Department of Decision Sciences and Information Management Working Paper 0601.
Yu, J., Goos, P. and Vandeboek, M. (2008) A comparison of different Bayesian design criteria to compute efficient conjoint choice experiments, Department of Decision Sciences and Information Management Working Paper 0817.
Yu, J., Goos, P. and Vandeboek, M. (2009) Efficient conjoint choice designs in the presence of respondent heterogeneity, Marketing Science, 28 (1), 122–135.CrossRefGoogle Scholar
Zavoina, R. and McElvey, W. (1975) A statistical model for the analysis of ordinal level dependent variables, Journal of Mathematical Sociology, Summer, 103–120.Google Scholar
Zeelenberg, M. (1999) The use of crying over spilled milk: a note on the rationality and functionality of regret, Philosophical Psychology, 12 (3), 325–340.CrossRefGoogle Scholar
Zeelenberg, M. and Pieters, R. (2007) A theory of regret regulation 1.0, Journal of Consumer Psychology, 17 (1), 3–18.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×