QMB 7933: PhD Seminar in IS/IT: Economic Models in IS Spring 2016 Module 3

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1 QMB 7933: PhD Seminar in IS/IT: Economic Models in IS Spring 2016 Module 3 Instructor Liangfei Qiu liangfei.qiu@warrington.ufl.edu Department of ISOM, Warrington College of Business Administration Class Time Monday: 2:30 4:30 pm Class Location STZ 200 Office STZ 331 Office Hours by appointment COURSE DESCRIPTION In this course, we will cover economic models that are widely used in Information Systems (IS), including both causal empirical models and game theoretical models. We will focus on five broad issues: (1) Regression discontinuity, (2) Natural experiments, instrumental variables, and difference in differences, (3) Randomized field experiments, lab experiments, and matching, (4) Observational learning models, and (5) Incomplete information games. Optional Reference Textbooks Textbook (Empirical part) Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricistʹs companion. Princeton university press (a very nice and must read econometrics book) Imbens, G. W., & Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press (more complete and math intensive). Textbook (Game theory part) Mas Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic theory. New York: Oxford university press. Fudenberg, D., & Tirole, J. (1991). Game theory Cambridge, Massachusetts Class Website Announcements, assignments, course schedule, additional readings, and other information are available on Canvas at 1

2 Jan 4: Class 1 Introduction: basic knowledge on causal empirical identification and game equilibria Jan 11: Class 2 Regression Discontinuity Anderson, M., & Magruder, J. (2012). Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. The Economic Journal, 122(563), Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48, Hartmann, W., Nair, H. S., & Narayanan, S. (2011). Identifying causal marketing mix effects using a regression discontinuity design. Marketing Science, 30(6), Narayanan, S., & Kalyanam, K. (2015). Position Effects in Search Advertising and their Moderators: A Regression Discontinuity Approach. Marketing Science, 34(3), Jan 18: MLK Day No Class Jan 25: Class 3 Natural Experiments, Instrumental Variables, and Difference in Differences Zhang, X. M., & Zhu, F. (2010). Group size and incentives to contribute: A natural experiment at Chinese Wikipedia. American Economic Review. Mayzlin, D., Dover, Y., & Chevalier, J. (2014). Promotional Reviews: An Empirical Investigation of Online Review Manipulation. American Economic Review, 104(8), Xu, K., Chan, J., Ghose, A., & Han, S. (2015). Battle of the Channels: The Impact of Tablets on Digital Commerce. Management Science, Forthcoming. Zhang, X., & Wang, C. (2012). Network positions and contributions to online public goods: The case of Chinese Wikipedia. Journal of management information systems, 29(2), Tucker, C. (2008). Identifying formal and informal influence in technology adoption with network externalities. Management Science, 54(12), Chen, Y., Wang, Q., and Xie, J. (2011). Online social interactions: A natural experiment on word of mouth versus observational learning. Journal of Marketing Research, 48(2), Moretti, E. (2011). Social learning and peer effects in consumption: Evidence from movie sales. Review of Economic Studies, 78(1),

3 Tucker, C., & Zhang, J. (2011). How does popularity information affect choices? A field experiment. Management Science, 57(5), Feb 1: Class 4 Randomized Field Experiments, Lab Experiments, and Matching Aral, S., & Walker, D. (2011). Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Science, 57(9), Bapna, R., & Umyarov, A. (2015). Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks. Management Science. Rice, S. C. (2012). Reputation and uncertainty in online markets: an experimental study. Information Systems Research, 23(2), Aral, S., Muchnik, L. and Sundararajan A. (2009). Distinguish Influence Based Contagion from Homophily Driven Diffusion in Dynamic Networks. Proceedings of the National Academy of Sciences, 106(51), Fang, Z., Gu, B., Luo, X., and Xu, Y. (2015). Contemporaneous and Delayed Sales Impact of Location Based Mobile Promotions. Information Systems Research, Forthcoming. Feb 8: Class 5 Observational Learning Models and Incomplete Information Games Bikhchandani, S., D. Hirshleifer, and I. Welch. (1992). A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades. Journal of Political Economy, 100(5), Galeotti, A., Goyal, S., Jackson, M. O., Vega Redondo, F., & Yariv, L. (2010). Network games. Review of economic studies, 77(1), Duan, W., B. Gu, and A. B. Whinston. (2009). Informational Cascades and Software Adoption on the Internet: an empirical investigation. MIS Quarterly. 33(1), Shi, Z., and Whinston, A. B. (2013). Network Structure and Observational Learning: Evidence from a Location Based Social Network. Journal of Management Information Systems, 30(2), Cai, H., Chen, Y., and Fang, H. (2009). Observational Learning: Evidence from a Randomized Natural Field Experiment. American Economic Review, 99(3), Chen, Y., and Xie, J. (2005). Third Party Product Review and Firm Marketing Strategy. Marketing Science, 24(2),

4 Feb 15: Class 6 Research Proposal Presentations Optional Topics Combination of Machine Learning and Econometrics Bajari, P., Nekipelov, D., Ryan, S. P., & Yang, M. (2015). Machine Learning Methods for Demand Estimation. American Economic Review, 105(5), Lin, M., Lucas Jr, H. C., & Shmueli, G. (2013). Too big to fail: large samples and the p value problem. Information Systems Research, 24(4), Varian, H. R. (2014). Big data: New tricks for econometrics. The Journal of Economic Perspectives, Athey, S., & Imbens, G. (2015). A Measure of Robustness to Misspecification. American Economic Review, 105(5), Athey, S., & Imbens, G. (2015). Machine Learning Methods for Estimating Heterogeneous Causal Effects. arxiv preprint arxiv: Athey, S. (2015, August). Machine Learning and Causal Inference for Policy Evaluation. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 5 6). ACM. Bloniarz, A., Liu, H., Zhang, C. H., Sekhon, J., & Yu, B. (2015). Lasso adjustments of treatment effect estimates in randomized experiments. arxiv preprint arxiv: Bajari, P., Nekipelov, D., Ryan, S. P., & Yang, M. (2015). Demand estimation with machine learning and model combination (No. w20955). National Bureau of Economic Research. How to Write and Present Economic Models in IS How to Build an Economic Model in Your Spare Time: Writing Economic Theory Papers: Code and Data for the Social Sciences: A Practitioner s Guide Writing Tips for Ph. D. Students: Writing Tips For Economics Research Papers: 4

5 The Big 5 and Other Ideas For Presentations: Job Market Guide: EVALUATION OF YOUR PERFORMANCE The breakdown of your final grade is as follows: Deliverable Detail Points Class Participation 10 In Class Presentations (2 literature presentations + 1 research proposal presentation) 40 Research Proposal (Make an appointment with me to discuss your research ideas before you write your research proposal. me a final copy by Feb 15) 50 Total Points 100 5