About

Peter Rossi is the James A. Collins Distinguished University Professor of Marketing, Economics, and Statistics at the UCLA Anderson School of Management. He received his PhD in Econometrics from the University of Chicago and his BA from Oberlin College. His research interests include pricing and promotion, target marketing, direct marketing, limited dependent variable models, and Bayesian statistical methods. Rossi has published extensively across multiple disciplines, including marketing, economics, statistics, and econometrics, with articles cited over 18,000 times. He is a co-author of the book Bayesian Statistics and Marketing and author of Bayesian Semi and Non-parametric Methods in Marketing and Micro-Econometrics. Rossi is also known for developing the R package bayesm, which is widely used in marketing and micro-econometrics. He founded the Kilts Center for Marketing at the University of Chicago and has served in various editorial roles, including founding editor of Quantitative Marketing and Economics. His work has significantly influenced targeted marketing practices, data-based pricing, and choice modeling, making him a prominent figure in his field.

Research topics

• Computer Science
• Mathematics
• Statistics
• Artificial Intelligence
• Machine Learning
• Data Mining
• Mathematical optimization
• Econometrics
• Algorithm
• Marketing
• Business
• Database
• Statistical physics

Selected publications

• Reply: “Revisiting Scalable Target Marketing…”

  Journal of Marketing Research · 2025-05-05

  articleSenior author

  Editor's Note After a critique of Bumbaca, Misra and Rossi (2020) was conditionally accepted by the journal, the editor invited the authors to provide a brief response. An Associate Editor and two reviewers evaluated the response. By publishing both the critique and response, we hope to underscore JMR 's commitment to ensuring the accuracy of the work we publish and maintaining transparency when corrections are necessary.

  PublisherDOI

• Bayesian Statistics and Marketing

  2024 · 9 citations

  1st authorCorresponding
  • Computer Science
  • Statistics
  • Econometrics

  Fine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.

  PublisherDOI

• State-Dependent Demand Estimation with Initial Conditions Correction

  Journal of Marketing Research · 2020 · 20 citations

  Senior authorCorresponding
  • Computer Science
  • Econometrics
  • Computer Science

  The authors analyze the initial conditions bias in the estimation of brand choice models with structural state dependence. Using a combination of Monte Carlo simulations and empirical case studies of shopping panels, they show that popular, simple solutions that misspecify the initial conditions are likely to lead to bias even in relatively long panel data sets. The magnitude of the bias in the state dependence parameter can be as large as a factor of 2–2.5. The authors propose a solution to the initial conditions problem that samples the initial states as auxiliary variables in a Markov chain Monte Carlo procedure. The approach assumes that the joint distribution of prices and consumer choices is in equilibrium, which is plausible for the mature consumer packaged goods products commonly used in empirical applications. In Monte Carlo simulations, the approach recovers the true parameter values even in relatively short panels. Finally, the authors propose a diagnostic tool that uses common, biased approaches to bound the values of the state dependence and construct a computationally light test for state dependence.

  PublisherDOI

• Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models

  Journal of Marketing Research · 2020 · 12 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Data Mining

  Many problems in marketing and economics require firms to make targeted consumer-specific decisions, but current estimation methods are not designed to scale to the size of modern data sets. In this article, the authors propose a new algorithm to close that gap. They develop a distributed Markov chain Monte Carlo (MCMC) algorithm for estimating Bayesian hierarchical models when the number of consumers is very large and the objects of interest are the consumer-level parameters. The two-stage and embarrassingly parallel algorithm is asymptotically unbiased in the number of consumers, retains the flexibility of a standard MCMC algorithm, and is easy to implement. The authors show that the distributed MCMC algorithm is faster and more efficient than a single-machine algorithm by at least an order of magnitude. They illustrate the approach with simulations with up to 100 million consumers, and with data on 1,088,310 donors to a charitable organization. The algorithm enables an increase of between $1.6 million and $4.6 million in additional donations when applied to a large modern-size data set compared with a typical-size data set.

  PublisherOA PDFDOI

• Inference for Product Competition and Separable Demand

  Marketing Science · 2019-07-01 · 19 citations

  articleOpen access

  We propose a methodology that estimates the composition of separable demand groups using retail scanner data.

  PublisherOA PDFDOI

• Inference for marketing decisions

  Handbook of economics marketing · 2019-01-01 · 6 citations

  book-chapterSenior authorCorresponding
  PublisherDOI

• Economic foundations of conjoint analysis

  Handbook of economics marketing · 2019-01-01 · 25 citations

  book-chapterSenior authorCorresponding
  PublisherDOI

• State-Dependent Demand Estimation with Initial Conditions Correction

  National Bureau of Economic Research · 2019-09-01 · 8 citations

  reportOpen accessSenior author

  We analyze the initial conditions bias in the estimation of brand choice models with structural state dependence. Using a combination of Monte Carlo simulations and empirical case studies of shopping panels, we show that popular, simple solutions that mis-specify the initial conditions are likely to lead to bias even in relatively long panel datasets. The magnitude of the bias in the state dependence parameter can be as large as a factor of 2 to 2.5. We propose a solution to the initial conditions problem that samples the initial states as auxiliary variables in an MCMC procedure. The approach assumes that the joint distribution of prices and consumer choices, and hence the distribution of initial states, is in equilibrium. This assumption is plausible for the mature consumer packaged goods products used in this and the majority of prior empirical applications. In Monte Carlo simulations, we show that the approach recovers the true parameter values even in relatively short panels. Finally, we propose a diagnostic tool that uses common, biased approaches to bound the values of the state dependence and construct a computationally light test for state dependence.

  PublisherOA PDFDOI

• Preface

  Handbook of economics marketing · 2019-01-01

  book-chapterSenior author
  PublisherDOI

• State-Dependent Demand Estimation with Initial Conditions Correction

  SSRN Electronic Journal · 2019-01-01

  articleOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Greg M. Allenby

  The Ohio State University

  59 shared

• Jean‐Pierre Dubé

  52 shared

• Timothy G. Conley

  37 shared

• Christian Hansen

  University of Chicago

  37 shared

• Günter J. Hitsch

  University of Chicago

  22 shared

• Robert E. McCulloch

  16 shared

• Éric Jacquier

  15 shared

• Judith A. Chevalier

  University of Nevada, Las Vegas

  12 shared

Awards & honors

• Fellow of the American Statistical Association
• Fellow of the Journal of Econometrics