MKT 620 – ADVANCED QUANTITATIVE MODELS IN MARKETING
This course will focus on acquiring the skills required to build, solve and estimate advanced
models in Marketing, with an emphasis on recently developed methodological advances.
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Prerequisites: Students are expected to have graduate training in Microeconomics, and
Econometrics, and Statistics.
Spring Semester, Wednesdays 4:00-7:00 pm
Professors
o Dina Mayzlin
 HOH 619 with office hours by appointment
 213-740-3360
o Sha Yang
 HOH 803 with office hours by appointment
 213-740-3902
Introduction
This course is intended for marketing PhD students interested in quantitative research methods,
both theoretical and empirical. In the theory part of the course, students learn techniques for
building and analyzing analytical models and will develop a sense for the power and limits of
analytical modeling. In the empirical part of the course, students will learn how to develop and
estimate various state-of-art models of consumer choice and firm decision.
Course Requirements (and Grade Weight %)
 Students will be required to read academic research papers then present and critique them to
the class. Each student is expected to prepare up to 5 such presentations throughout the
semester. (40%)
 Students must complete a referee report due in week 7. (10%)
 The second half of this course will teach students advanced empirical modeling and
estimation techniques in marketing. Students will learn how to develop and estimate various
state-of-art models of consumer choice and firm decision. Grade for this portion will be
determined by an 8-10 page research proposal (50%), written paper critiques or exercises
(30%) and class participation (20%).
Class Topics
1. Introduction to Analytical Modeling
2. Models of Persuasion
3. Dynamic Investment Games
4. The Role of Advertising
5. Advertising Content
6. Word of Mouth (empirical)
7. Field Experiments
8. Introduction to Empirical Research in Marketing
9. Unobserved heterogeneity
10. Consumer learning
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11. Consumer decision making
12. Simultaneous move game
13. Social network
14. Search engine advertising
Part I – Analytical Modeling and Theory Testing (Sessions 1-7)
Session 1: Introduction to Analytical Modeling
1) Ted O'Donoghue and Matthew Rabin (1999), “Doing It Now or Later,” The American Economic
Review, Vol. 89, No. 1 (Mar., 1999), pp. 103-124
2) Birger Wernerfelt, (1994) “Selling Formats for Search Goods.” Marketing Science 13(3):298-309
Session 2: Models of Persuasion
1) Crawford, V. and J. Sobel (1982), “Strategic Information Transmission,” Econometrica, 52(6),
1431-1451.
2) Kamenica, E., and M. Gentzkow (2011): “Bayesian Persuasion,” American Economic Review,
101(6), 2590-2615.
Session 3: Dynamic Investment Games (taught by Prof Selove)
1) Johannes Horner (2004). “A Perpetual Race to Stay Ahead,” Review of Economic Studies, Vol.
71, No. 4, pp. 1065-1088.
2) Matthew Selove (2014). “A Dynamic Model of Competitive Entry Response,” Marketing
Science, Vol. 33, No. 3, pp. 353–363.
Session 4: The Role of Advertising
1) Milgrom, P. & J. Roberts (1986), “Price and Advertising as a Signal of Quality,” Journal of
Political Economy, Vol. 94: 796-812.
2) Greg Shaffer and Florian Zettelmeyer (2009), “Comparative Advertising and In-Store Displays,”
Marketing Science 28 (6): 1144-1156
Session 5: Advertising Content
1) Anderson, S. & R. Renault (2006), “Advertising Content,” The American Economic Review, 96
(1), 93-113.
2) Dina Mayzlin and Jiwoong Shin (2011), “Uninformative Advertising as an Invitation to Search,”
Marketing Science, 30 (4) , 666-685
Session 6: Word of Mouth (empirical)
1) David Godes, José C. Silva, (2012) “Sequential and Temporal Dynamics of Online Opinion,”
Marketing Science, 31(3):448-473.
2) Mayzlin, Dina, Yaniv Dover, and Judith Chevalier (2014) "Promotional Reviews: An Empirical
Investigation of Online Review Manipulation." American Economic Review, 104(8): 2421-55.
Session 7: Field Experiments
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1) Andrey Fradkin, Elena Grewal, David Holtz, and Matthew Pearson (2014) “Bias and Reciprocity
in Online Reviews: Evidence From Field Experiments on Airbnb,” Stanford working paper.
2) Steve Tadelis and Florian Zettelmeyer (2014) “Information Disclosure as a Matching
Mechanism: Theory and Evidence from a Field Experiment” (forthcoming AER)
3) Thomas Blake, Chris Nosko, and Steven Tadelis (2014) “Consumer Heterogeneity and Paid
Search Effectiveness: A Large Scale Field Experiment” (forthcoming Econometrica)
Part II – Empirical Modeling (Sessions 8-14)
Session 8: Basic Concepts in Bayesian Estimation
Rossi, Peter E. and Greg M. Allenby (2003), "Bayesian Statistics and Marketing," Marketing Science,
22, 304-328.
Yang, Sha, Yi Zhao and Ravi Dhar (2010), “Modeling the Under Reporting Bias in Panel Survey
Data”, Marketing Science, 29 (3), 525-539.
Gilbride, Timothy and Greg Allenby (2004), “A Choice Model with Conjunctive, Disjunctive, and
Compensatory Screening Rules,” Marketing Science, 23 (3), 391-406.
Kamakura, Wagner A., Kim, Byung-Do and Jonathan Lee. (1996). “Modeling Preference and
Structural Heterogeneity in Consumer Choice,'' Marketing Science, 15, 152-172.
Session 9: Structural Modeling and Estimation of Consumer Demand
Hanemann, M. W. (1984), “Discrete/Continuous Model of Consumer Demand,” Econometrica, 52,
541-561.
Bhat, Chandra (2005), “A multiple discrete–continuous extreme value model: formulation and
application to discretionary time-use decisions,” Transportation Research Part B, 39(8), 679–
707.
Session 10: Modeling Endogeneity
Yang, Sha, Yuxin, Chen and Greg M. Allenby (2003) “Bayesian Analysis of Simultaneous Demand
and Supply,” Quantitative Marketing and Economics, 1, 251-275.
Jiang, R, P. Manchanda and P. Rossi (2009), “Bayesian Analysis of Random Coefficient Logit
Models Using Aggregate Data,” Journal of Econometrics, 149, 136-148.
Chen, Yuxin, Sha Yang and Ying Zhao (2008), “A Simultaneous Model of Consumer Brand Choice
and Negotiated Price” Management Science, 54(3), 538-549.
Session 11: Learning Models
Yang, Sha, Yi Zhao, Tulin Erdem and Daeyoung Koh (2012), “Modeling Consumer Choice with
Dyadic Learning: An Intra-household Analysis,” working paper.
Zhao, Yi, Sha Yang, Vishal Narayan and Ying Zhao (2013), “Modeling Consumer Learning from
Online Product Reviews,” Marketing Science, 32(1), 153-169.
Chylinski, Roberts and Hardie (2012), “Consumer Learning of New Binary Attribute Importance
Accounting for Priors, Bias, and Order Effects,” Marketing Science, 31(4), 549-566.
Session 12: Estimating Simultaneous Move Game
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Yang, Sha, Yi Zhao, Tulin Erdem, and Ying Zhao (2010) “Modeling the Intra-Household Behavioral
Interaction,” Journal of Marketing Research, 47 (3), 470-484.
Bresnahan, Timothy F. and Peter C. Reiss (1991), “Empirical Models of Discrete Games,” Journal of
Econometrics. 48. 57-81.
Yang, Sha, Shijie Lu and Xianghua Lu (2014), “Modeling Competition and Its Impact in Paid-Search
Advertising,” Marketing Science, 33(1), 134-153.
Session 13: Estimating Matching Models
Choo, E., A. Siow. 2006. Who marries whom and why. J. Political Economy, 14(1) 175–201.
Fox, J. T. 2010. Estimating matching games with transfers. Working paper, University of Michigan.
Session 14: Modeling Social Network Influence and Formation
Yang, Sha and Greg M. Allenby (2003), “Modeling Interdependent Consumer Preferences,” Journal
of Marketing Research, 40(3), 282-294.
Narayan, Vishal and Sha Yang (2009), “Modeling the Formation of Dyadic Relationships in Online
Communities,” Working Paper, Cornell University.
Statement for Students with Disabilities
Any student requesting academic accommodations based on a disability is required to register with
Disability Services and Programs (DSP) each semester. A letter of verification for approved
accommodations can be obtained from DSP. Please be sure the letter is delivered to me (or to TA) as
early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m.–5:00 p.m., Monday
through Friday. The phone number for DSP is (213) 740-0776.
Statement on Academic Integrity
USC seeks to maintain an optimal learning environment. General principles of academic honesty include
the concept of respect for the intellectual property of others, the expectation that individual work will be
submitted unless otherwise allowed by an instructor, and the obligations both to protect one’s own
academic work from misuse by others as well as to avoid using another’s work as one’s own. All students
are expected to understand and abide by these principles. Scampus, the Student Guidebook, contains the
Student Conduct Code in Section 11.00, while the recommended sanctions are located in Appendix A:
http://www.usc.edu/dept/publications/SCAMPUS/gov/. Students will be referred to the Office of Student
Judicial Affairs and Community Standards for further review, should there be any suspicion of academic
dishonesty. The Review process can be found at: http://www.usc.edu/student-affairs/SJACS/.
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