Peter Rossi

Distinguished Professor, James Collins Professor of Marketing, Statistics and Economics

Phone: (773) 294-8616

peter.rossi@anderson.ucla.edu

B4.12

Biography

Peter E. Rossi is James Collins Professor of Marketing, Statistics and Economics at UCLA Anderson School of Management. He has published widely in marketing, economics, statistics and econometrics including Quantitative Marketing and Economics, Marketing Science, Journal of Marketing Research, American Economic Review, Journal of the American Statistical Association, Econometrica, Journal of Political Economy, Journal of Econometrics, Biometrika, Journal of Business and Economic StatisticsRand Journal of Economics, and Journal of Economic Theory.  These articles have more than 5600 Google Scholar cites.  He is a co-author of Bayesian Statistics and Marketing, John Wiley Series in Probability and Statistics (2005). Professor Rossi founded the Kilts Center for Marketing, Booth School of Business, University of Chicago while on faculty there.

A fellow of the American Statistical Association and the Journal of Econometrics, he is founding editor, Quantitative Marketing and Economics, past Associate Editor for Journal of the American Statistical Association, Journal of Econometrics, and Journal of Business and Economic Statistics.

His work in the area of target marketing presaged many of the developments in targeting today as practiced in electronic couponing and by web-based retailers.  His work in data-based pricing and methods for estimation of high-dimensional demand systems influenced the development of analytic pricing tools in use today.

Education

Ph.D. Econometrics, 1984, University of Chicago
MBA Management Science, 1980, University of Chicago
B.A. Mathematics and History, 1976, Oberlin College

Interests

Pricing and Promotion, Target Marketing, Direct Marketing, Micro-Marketing, Limited Dependent Variable Models, Bayesian Statistical Methods
  • P. Rossi, J.P. Dube and G. J. Hitsch. (2010). State Dependence and Alternative Explanations for Consumer Inertia. RAND Journal of Economics, [ Link ]
  • P. Rossi, G. M. Allenby and M. J. Garratt. (2010). A Model for Trade-Up and Change in Considered Brands. Marketing Science, Vol. 29, No. 1, January-February, pp. 40-56.. [ Link ]
  • P. Rossi, G. J. Hitsch and J. P. Dube. (August, 2009). Do Switching Costs Make Markets Less Competitive?. Journal of Marketing Research, Vol. XLVI. [ Link ]
  • P. Rossi, G. J. Hitsch and J. P. Dube. (August, 2009). Commentaries and Rejoinder to Shin and Sudhir and to Cabral. Journal of Marketing Research, Vol. XLVI. [ Link ]
  • P. Rossi. (Forthcoming). Both Network Effects and Quality are Important. Journal of Marketing Research, [ Link ]
  • P. Rossi, R. Jiang and P. Manchanda. Bayesian Analysis of Random Coefficient Logit Models Using Aggregate Data. Journal of Econometrics, Vol. 149, April, pp. 136-148. [ Link ]
  • P. Rossi, S. Chandukala, J. Kim, T. Otter, and G. Allenby. (Forthcoming). Choice Models in Marketing: Economic Assumptions, Challenges and Trends. Foundations and Trends in Marketing, [ Link ]
  • P. Rossi and G. Allenby. (August, 2008). Teaching Bayesian Statistics to Marketing and Business Students. The American Statistician, Vol. 62, No. 3, 195-198. [ Link ]
  • P. Rossi, T. Conley, C. Hansen and R. McCulloch. (2008). A Semi-Parametric Bayesian Approach to the Instrumental Variable Problem. Journal of Econometrics, 144, 276-305. [ Link ]
  • P. Rossi, J. P. Dube, G. J. Hitsch, and M. Vitorino. (2008). Category Pricing with State Dependent Utility. Marketing Science, Vol. 27, No. 3, May-June, pp. 417-429. [ Link ]
  • P. Rossi, J. Kim and G. Allenby. Product Attributes and Models of Multiple Discreteness. Journal of Econometrics, 138, pp. 208-230. [ Link ]
  • P. Rossi, R. Grover and M. Vriens. Hierarchial Bayes Models: A Practitioner's Guide. The Handbook of Marketing Research, R. Grover and M.Vriens (eds.). [ Link ]
  • P. Rossi, P. Chintagunta, T. Erdem and M. Wedel. (2006). Structural Modeling in Marketing: Review and Assessment," with P. Chintagunta, T. Erdem and M. Wedel (2006), Marketing Science, Vol. 25, No. 6, November-December, 581-605.. Marketing Science, Vol. 25, No. 6, November-December, 581-605. [ Link ]
  • P. Rossi, Z. Gilula and R. McCulloch. (2006). A Direct Approach to Data Fusion. Journal of Marketing Research, Vol. XLIII, February, 73-83. [ Link ]
  • P. Rossi and G. Allenby. (2010). Bayesian Applications in Marketing!. [ Download ] [ Show Abstract ]
    In this chapter, we review applications of Bayesian methods to marketing problems. Key aspects of marketing applications include the discreteness of response or outcome data and relatively large numbers of cross-sectional units, each with possibly low information content. Discrete response data require the development of non-standard likelihoods and low information content requires careful use of informative priors. One particularly important form of informative prior is embodied in hierarchical models. Given the importance of the prior, it is important to assure flexibility in the prior specification. Non-standard likelihoods and flexible priors make marketing a very challenging area for Bayesian applications.
  • J.P. Dube, G. Hitsch and P. Rossi. (February, 2010). State Dependence and Alternative Explanations for Consumer Inertia. [ Link ] [ Download ] [ Show Abstract ]
    For many consumer packaged goods products, researchers have documented inertia in brand choice, a form of persistence whereby consumers have a higher probability of choosing a product that they have purchased in the past. Using data on margarine and refrigerated orange juice purchases, we show that the finding of inertia is robust to flexible controls for preference heterogeneity and not due to autocorrelated taste shocks. Thus, the inertia is at least partly due to structural, not spurious state dependence. We explore three economic explanations for the observed structural state dependence: preference changes due to past purchases or consumption experiences which induce a form of loyalty, search, and learning. Our data are consistent with loyalty, but not with search or learning. Properly distinguishing among the different sources of inertia is important for policy analysis, because the alternative sources of inertia imply qualitative differences in firm's pricing incentives and lead to quantitatively different equilibrium pricing outcomes.