Utility Covariances and Context Effects in Conjoint MNP Models

References

• Akaike H., Petrov B. N., Csáki F. Information theory and an extension of the maximum likelihood principle. 2nd International Symposium on Information Theory (1973) (Akadémiai Kiadó, Budapest) 267–281Google Scholar
• Allenby G. M., Arora N., Ginter J. L. Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. (1995) 32:152–162Crossref, Google Scholar
• Allenby G. M., Ginter J. L. Using extremes to design products and segment markets. J. Marketing Res. (1995) 32:392–403Crossref, Google Scholar
• Allenby G. M., Lenk P. J. Modeling household purchase behavior with logistic normal regression. J. Amer. Statist. Assoc. (1994) 89(December):1218–1231Crossref, Google Scholar
• Amemiya T. Qualitative response models: A survey. J. Econom. Literature (1981) 19(December):1483–1536Google Scholar
• AptechGauss Applications (1995) (Aptech Systems, Inc., Maple Valley, WA) Google Scholar
• Bekker P. A., Merckens A., J.Wansbeek T.Identification, Equivalent Models, and Computer Algebra (1994) (Academic Press, San Diego, CA) Google Scholar
• Ben-Akiva M., Lerman S. R.Discrete Choice Analysis: Theory and Application to Travel Demand (1985) (MIT Press, Cambridge, MA) Google Scholar
• Börsch-Supan A., Hajivassiliou V. A. Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models. J. Econometrics (1993) 58(3):347–368Crossref, Google Scholar
• Börsch-Supan A., Hajivassiliou V. A., Kotlikoff L. J., Morris J. N. Health, children, and elderly living arrangements: A multiperiod, multinomial probit model with unobserved heterogeneity and autocorrelated errors, NBER. (1990) . Working paper 3343, Cambridge, MAGoogle Scholar
• Bunch D. S. Estimability in the multinomial probit model. Transportation Res. B (1991) 25B(1):1–12Crossref, Google Scholar
• Bunch D. S., Kitamura R. Multinomial probit model estimation revisited: Testing estimable model specifications, maximum likelihood algorithms, and probit integral approximations for trinomial models of household car ownership. (1991) . Working paper, University of California at DavisGoogle Scholar
• Carroll J. D., Green P. E. Psychometric methods in marketing research: Part 1, conjoint analysis. J. Marketing Res. (1995) 32(November):385–391Crossref, Google Scholar
• Chintagunta P. K. Estimating a multinomial probit model of brand choice using the method of simulated moments. Marketing Sci. (1992) 11(4):386–407Link, Google Scholar
• Chintagunta P. K., Honoré B. E. Investigating the effects of marketing variables and unobserved heterogeneity in a multinomial probit model. Internat. J. Res. Marketing (1996) 13(1):1–15Crossref, Google Scholar
• Cohen S. H. Perfect union: CBCA marries the best of conjoint and discrete choice models. Marketing Res. (1997) 9(1):12–17Google Scholar
• Currim I. S. Predictive testing of consumer choice models not subject to independence of irrelevant alternatives. J. Marketing Res. (1982) 19(2):208–222Crossref, Google Scholar
• Daganzo C. D.Multinomial Probit, The Theory and Its Applications to Demand Forecasting (1979) (Academic Press, New York) Google Scholar
• DeSarbo W. S., Ramaswamy V., Cohen S. H. Market segmentation with choice-based conjoint analysis. Marketing Lett. (1995) 6(2):137–148Crossref, Google Scholar
• Elrod T., Keane M. P. A factor-analytic probit model for representing the market structures in panel data. J. Marketing Res. (1995) 32(1):1–16Crossref, Google Scholar
• Elrod T., Louviere J. J., Davey K. S. An empirical comparison of rating-based and choice-based conjoint models. J. Marketing Res. (1992) 29(3):368–377Crossref, Google Scholar
• Geweke J., Keane M. P., Runkle D. Alternative computational approaches to inference in the multinomial probit model. Rev. Econom. Statist. (1994) 76(4):609–632Crossref, Google Scholar
• Gönül F., Srinivasan K. Modeling multiple sources of heterogeneity in multinomial logit models: Methodological and managerial issues. Marketing Sci. (1993) 12(3):213–229Link, Google Scholar
• Green P. E., Krieger A. M., Eliashberg J., Lilien G. L. Conjoint analysis with productpositioning applications. Handbooks in Operations Research and Management Science (1993) 5MarketingGoogle Scholar
• Green P. E., Srinivasan V. Conjoint analysis in consumer research: Issues and outlook. J. Consumer Res. (1978) 5(September):103–123Crossref, Google Scholar
• Hajivassiliou V. A., Maddala G. S., Rao C. R., Vinod H. D. Simulation estimation methods for limited dependent variable models. Handbook of Statistics (1993) 11:519–543Google Scholar
• Hajivassiliou V. A., McFadden D. The method of simulated scores for the estimation of LDV models. (1990) . Working paper, Yale University, New Haven, CTGoogle Scholar
• Hajivassiliou V. A., McFadden D., Ruud P. Simulation of multivariate normal rectangle probabilities and their derivatives: Theoretical and computational results. (1993) . Working paper, Yale University, New Haven, CTGoogle Scholar
• Hausman J. A., Wise D. A. A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preferences. Econometrica. (1978) 46(2):403–426Crossref, Google Scholar
• Heckman J. J., Sedlacek G. Heterogeneity, aggregation, and market wage function: An empirical model of self-selection in the labor market. J. Political Econom. (1985) 93(6):1077–1125Crossref, Google Scholar
• Huber J., Payne J. W., Puto C. Adding asymmetrically dominant alternatives: Violations of regularity and the similarity hypotheses. J. Consumer Res. (1982) 9:90–98Crossref, Google Scholar
• Huber J., Puto C. Market boundaries and product choice: Illustrating attraction and substitution effects. J. Consumer Res. (1983) 10:31–44Crossref, Google Scholar
• Kamakura W. A. The estimation of multinomial probit models: A new calibration algorithm. Transportation Sci. (1989) 23(4):253–265Link, Google Scholar
• Kamakura W. A., Srivastava R. K. Predicting choice shares under conditions of brand interdependence. J. Marketing Res. (1984) 21(November):420–434Crossref, Google Scholar
• Keane M. P. A note on identification in the multinomial probit model. J. Bus. Econom. Statist. (1992) 10(2):193–200Crossref, Google Scholar
• Keane M. P. Current issues in discrete choice modeling. Marketing Lett. (1997) 8(3):307–322Crossref, Google Scholar
• Lee L.-F. On efficiency of methods of simulated moments and maximum simulated likelihood estimation of discrete response models. Econometric Theory (1992) 8:518–552Crossref, Google Scholar
• Lenk P. J., DeSarbo W. S., Green P. E., Young M. R. Hierarchical bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci.15(2):173–191Link, Google Scholar
• Louviere J. J. Conjoint analysis modeling of stated preferences. J. Transport Econom. Policy (1988) January):93–119A review of theory, methods, recent developments and external validityGoogle Scholar
• Louviere J. J., Woodworth G. Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. J. Marketing Res. (1983) 20(4):350–367Crossref, Google Scholar
• Maddala G. S.Limited-Dependent and Qualitative Variables in Econometrics (1983) (Cambridge University Press, Cambridge, UK) Crossref, Google Scholar
• Mahajan V., Green P. E., Goldberg S. M. A conjoint model for measuring self and cross-price/demand relationships. J. Marketing Res. (1982) 19(3):334–342Crossref, Google Scholar
• McCulloch R., Rossi P. E. An exact likelihood analysis of the multinomial probit model. J. Econometrics (1994) 64:207–240Crossref, Google Scholar
• McFadden D. Quantal choice analysis: A survey. Ann. Econom. Soc. Measurement (1976) 5(4):363–390Google Scholar
• McFadden D. A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica (1989) 57(5):995–1026Crossref, Google Scholar
• Mühleisen M. On the use of simulated estimators for panel models with limited dependent variables. (1991) . Working paper, University of Munich, GermanyGoogle Scholar
• Nowlis S. M., Simonson I. Attribute-task compatibility as a determinant of consumer preference reversals. J. Marketing Res. (1997) 34(May):205–218Crossref, Google Scholar
• Papatla P. A multiplicative fixed-effects model of consumer choice. Marketing Sci. (1996) 15(3):243–261Link, Google Scholar
• Pudney S.Modeling Individual Choice: The Econometrics of Corners, Kinks and Holes (1989) (Basil Blackwell, Inc., New York) Google Scholar
• Rossi P. E., Allenby G. M. A Bayesian approach to estimating household parameters. J. Marketing Res. (1993) 30:171–182Crossref, Google Scholar
• Rossi P. E., McCulloch R. E., Allenby G. M. The value of purchase history data in target marketing. Marketing Sci. (1996) 15(4):321–340Link, Google Scholar
• Schwarz G. Estimating the dimension of a model. Ann. Statist. (1978) 6:461–464Crossref, Google Scholar
• Simonson I. Choice based on reasons: The case of attraction and substitution effects. J. Consumer Res. (1989) 16:158–174Crossref, Google Scholar
• Simonson I., Tversky A. Choice in context: Tradeoff contrasts and extremeness aversion. J. Marketing Res. (1992) 29:281–295Crossref, Google Scholar
• Tversky A. Elimination by aspects: A theory of choice. Psych. Rev. (1972) 79:281–299Crossref, Google Scholar
• Vriens M., Wedel M., Wilms T. Metric conjoint segmentation methods: A Monte Carlo comparison. J. Marketing Res. (1996) 32(1):73–85Crossref, Google Scholar