Utility covariances and context effects in conjoint MNP models

Marketing Science

Volumn 17, Issue 3, 1998, Pages 236-252

  Haaijer, Rinus ^a   Wedel, Michel ^b   Vriens, Marco ^b   Wansbeek, Tom ^b  

^a UNIVERSITY OF GRONINGEN   (Netherlands)

^b NONE

Author keywords

Conjoint choice experiments; Multinomial probit; Random taste variation; Random utility

Indexed keywords

EID: 0032218129     PISSN: 07322399     EISSN: None     Source Type: Journal    
DOI: 10.1287/mksc.17.3.236     Document Type: Article

References (56)

1. Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. B. N. Petrov, F. Csáki, eds. 2nd International Symposium on Information Theory. Akadémiai Kiadó, Budapest. 267-281.
2. Allenby, G. M., N. Arora, J. L. Ginter 1995. Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. 32 152-162.
3. -, J. L. Ginter 1995. Using extremes to design products and segment markets. J. Marketing Res. 32 392-403.
4. -, P. J. Lenk 1994. Modeling household purchase behavior with logistic normal regression. J. American Statistical Association. 89 December 1218-1231.
5. Amemiya, T. 1981. Qualitative response models: A survey. J. Economic Literature. 19 December 1483-1536.
6. Aptech 1995. Gauss Applications. Aptech Systems, Inc., Maple Valley, WA.
7. Bekker, P. A., A. Merckens, T. J. Wansbeek 1994. Identification, Equivalent Models, and Computer Algebra. Academic Press, San Diego, CA.
8. Ben-Akiva, M., S. R. Lerman 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge, MA.
9. Börsch-Supan, A., V. A. Hajivassiliou 1993. Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models. J. Econometrics. 58 3 347-368.
10. -,-, L. J. Kotlikoff, J. N. Morris 1990. Health, children, and elderly living arrangements: A multiperiod, multinomial probit model with unobserved heterogeneity and autocorrelated errors, NBER, Working paper 3343, Cambridge, MA.
11. Bunch, D.S. 1991. Estimability in the multinomial probit model. Transportation Res. B. 25B 1 1-12.
12. -, R. Kitamura 1991. Multinomial probit model estimation revisited: Testing estimable model specifications, maximum likelihood algorithms, and probit integral approximations for trinomial models of household car ownership. Working paper, University of California at Davis.
13. Carroll, J. D., P. E. Green 1995. Psychometric methods in marketing research: Part 1, conjoint analysis. J. Marketing Res. 32 November 385-391.
14. Chintagunta, P. K. 1992. Estimating a multinomial probit model of brand choice using the method of simulated moments. Marketing Sci. 11 4 386-407.
15. -, B. E. Honoré 1996. Investigating the effects of marketing variables and unobserved heterogeneity in a multinomial probit model. International J. Res. in Marketing. 13 1 1-15.
16. Cohen, S. H. 1997. Perfect union: CBCA marries the best of conjoint and discrete choice models. Marketing Res. 9 1 12-17.
17. Currim, I. S. 1982. Predictive testing of consumer choice models not subject to independence of irrelevant alternatives. J. Marketing Res. 19 2 208-222.
18. Daganzo, C. D. 1979. Multinomial Probit, The Theory and Its Applications to Demand Forecasting. Academic Press, New York.
19. DeSarbo, W. S., V. Ramaswamy, S. H. Cohen 1995. Market segmentation with choice-based conjoint analysis. Marketing Letters. 6 2 137-148.
20. Elrod, T., M. P. Keane 1995. A factor-analytic probit model for representing the market structures in panel data. J. Marketing Res. 32 1 1-16.
21. -, J. J. Louviere, K. S. Davey 1992. An empirical comparison of rating-based and choice-based conjoint models. J. Marketing Res. 29 3 368-377.
22. Geweke, J., M. P. Keane, D. Runkle 1994. Alternative computational approaches to inference in the multinomial probit model. Rev. Economics and Statistics. 76 4 609-632.
23. Gönül, F., K. Srinivasan 1993. Modeling multiple sources of heterogeneity in multinomial logit models: Methodological and managerial issues. Marketing Sci. 12 3 213-229.
24. Green, P. E., A. M. Krieger 1993. Conjoint analysis with product-positioning applications. J. Eliashberg, G. L. Lilien, eds. Handbooks in Operations Research and Management Science, Vol. 5, Marketing.
25. -, V. Srinivasan 1978. Conjoint analysis in consumer research: Issues and outlook. J. Consumer Res. 5 September 103-123.
26. Hajivassiliou, V. A. 1993. Simulation estimation methods for limited dependent variable models. G. S. Maddala, C. R. Rao, H. D. Vinod, eds. Handbook of Statistics, Vol. 11, 519-543.
27. -, D. McFadden 1990. The method of simulated scores for the estimation of LDV models. Working paper, Yale University, New Haven, CT.
28. -,-, P. Ruud 1993. Simulation of multivariate normal rectangle probabilities and their derivatives: Theoretical and computational results. Working paper, Yale University, New Haven, CT.
29. Hausman, J. A., D. A. Wise 1978. A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preferences. Econometrica. 46 2 403-426.
30. Heckman, J. J., G. Sedlacek 1985. Heterogeneity, aggregation, and market wage function: An empirical model of self-selection in the labor market. J. Political Economy. 93 6 1077-1125.
31. Huber, J., J. W. Payne, C. Puto 1982. Adding asymmetrically dominant alternatives: Violations of regularity and the similarity hypotheses. J. Consumer Res. 9 90-98.
32. -, C. Puto 1983. Market boundaries and product choice: Illustrating attraction and substitution effects. J. Consumer Res. 10 31-14.
33. Kamakura, W. A. 1989. The estimation of multinomial probit models: A new calibration algorithm. Transportation Sci. 23 4 253-265.
34. -, R. K. Srivastava 1984. Predicting choice shares under conditions of brand interdependence. J. Marketing Res. 21 November 420-434.
35. Keane, M. P. 1992. A note on identification in the multinomial probit model. J. Business and Economic Statistics. 10 2 193-200.
36. -, 1997. Current issues in discrete choice modeling. Marketing Letters. 8 3 307-322.
37. Lee, L.-F. 1992. On efficiency of methods of simulated moments and maximum simulated likelihood estimation of discrete response models. Econometric Theory. 8 518-552.
38. Lenk, P. J., W. S. DeSarbo, P. E. Green, M. R. Young 1996. Hierarchical bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. 15 2 173-191.
39. Louviere, J. J. 1988. Conjoint analysis modeling of stated preferences. A review of theory, methods, recent developments and external validity. J. Transport Economics and Policy. January 93-119.
40. -, G. Woodworth 1983. Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. J. Marketing Res. 20 4 350-367.
41. Maddala, G. S. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge, UK.
42. Mahajan, V., P. E. Green, S. M. Goldberg 1982. A conjoint model for measuring self and cross-price/demand relationships. J. Marketing Res. 19 3 334-342.
43. McCulloch, R., P. E. Rossi 1994. An exact likelihood analysis of the multinomial probit model. J. Econometrics. 64 207-240.
44. McFadden, D. 1976. Quantal choice analysis: A survey. Annals of Economic and Social Measurement. 5 4 363-390.
45. -1989. A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica. 57 5 995-1026.
46. Mühleisen, M. 1991. On the use of simulated estimators for panel models with limited dependent variables. Working paper, University of Munich, Germany.
47. Nowlis, S. M., I. Simonson 1997. Attribute-task compatibility as a determinant of consumer preference reversals. J. Marketing Res. 34 May 205-218.
48. Papatla, P. 1996. A multiplicative fixed-effects model of consumer choice. Marketing Sci. 15 3 243-261.
49. Pudney, S. 1989. Modeling Individual Choice: The Econometrics of Corners, Kinks and Holes. Basil Blackwell Inc., New York.
50. Rossi, P. E., G. M. Allenby 1993. A Bayesian approach to estimating household parameters. J. Marketing Res. 30 171-182.
51. -, R. E. McCulloch, G. M. Allenby 1996. The value of purchase history data in target marketing. Marketing Sci. 15 4 321-340.
52. Schwarz, G. 1978. Estimating the dimension of a model. Annals of Statistics. 6 461-164.
53. Simonson, I. 1989. Choice based on reasons: The case of attraction and substitution effects. J. Consumer Res. 16 158-174.
54. -, A. Tversky 1992. Choice in context: Tradeoff contrasts and extremeness aversion. J. Marketing Res. 29 281-295.
55. Tversky, A. 1972. Elimination by aspects: A theory of choice. Psychological Rev. 79 281-299.
56. Vriens, M., M. Wedel, T. Wilms 1996. Metric conjoint segmentation methods: A Monte Carlo comparison. J. Marketing Res. 32 1 73-85.