Summer Session 2019
Experimental Empirical Methods
- Instructor: Jiyong Park, UNCG (jiyong.park@uncg.edu)
- This session is designed for doctoral students to get familiar with empirical research primarily pursuing causal inference, rather than prediction. In doing so, identification strategies and their "etiquette" (Goldfarb and Tucker 2014) will be covered, especially focusing on quasi-experiments to mimic "gold standard" for causal inference.
- This session will follow the spirit of two Econometrics textbooks written by Joshua D. Angrist (MIT) and Jörn-Steffen Pischke (LSE).
- (Basic level) Mastering 'Metrics: The Path from Cause to Effect
- (Intermediate level) Mostly Harmless Econometrics: An Empiricist's Companion
- Goldfarb and Tucker (2014) provide a great summary of contemporary econometric approaches in keeping with Angrist and Pischke's.
- Goldfarb, A. and Tucker, C.E., 2014. Conducting Research with Quasi-Experiments: A Guide for Marketers. Rotman School of Management Working Paper No. 2420920 (https://ssrn.com/abstract=2420920).
- There is no prerequisite for this session, but please refer to the previous sessions for more information.
- KAIST MIS Summer Session 2018 (Research Design for Data Analytics)
- KAIST MIS Summer Session 2017 (Introduction to Economics of IS and Research Methodology)
Date
Topic
Reading List
Week 1 (7/17, 14:00~16:00)
- Research Design Matters: Rethinking Regressions
Angrist, J.D. and Pischke, J.S., 2017. Undergraduate Econometrics Instruction: Through Our Classes, Darkly. Journal of Economic Perspectives, 31(2), pp.125-44.
Reiss, P.C., 2011. Descriptive, Structural, and Experimental Empirical Methods in Marketing Research. Marketing Science, 30(6), pp.950-964.
Week 2 (7/24, 14:00~16:00)
- Identification Strategy with Quasi-Experiments
Goldfarb, A. and Tucker, C.E., 2014. Conducting Research with Quasi-Experiments: A Guide for Marketers. Rotman School of Management Working Paper No. 2420920 (https://ssrn.com/abstract=2420920).
Varian, H.R., 2016. Causal Inference in Economics and Marketing. Proceedings of the National Academy of Sciences (PNAS), 113(27), pp.7310-7315.
Mithas, S. and Krishnan, M.S., 2009. From Association to Causation via a Potential Outcomes Approach. Information Systems Research, 20(2), pp.295-313.
Week 3 (7/31, 14:00~17:00)
- All Quasi-Experiments are Wrong, but Some are Useful: Some Strategies to "Defend"
(Don't need to read all papers, but strongly recommended reading, at least, the "Introduction" to get the feel of research contexts)
(1) Endogenous Selection
Burtch, G., Carnahan, S. and Greenwood, B.N., 2018. Can You Gig it? An Empirical Examination of the Gig Economy and Entrepreneurial Activity. Management Science, 64(12), pp.5497-5520.
Rishika, R., Kumar, A., Janakiraman, R. and Bezawada, R., 2013. The Effect of Customers' Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation. Information Systems Research, 24(1), pp.108-127.
(2) Exogenous Selection
Adamopoulos, P., Ghose, A. and Todri, V., 2018. The Impact of User Personality Traits on Word of Mouth: Text-Mining Social Media Platforms. Information Systems Research, 29(3), pp.612-640.
Proserpio, D. and Zervas, G., 2017. Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews. Marketing Science, 36(5), pp.645-665.
(3) Exogenous Shock (a.k.a. Natural Experiment)
Gilje, E.P., 2019. Does Local Access to Finance Matter? Evidence from US Oil and Natural Gas Shale Booms. Management Science, 65(1), pp.1-18.
Danaher, B., Dhanasobhon, S., Smith, M.D. and Telang, R., 2010. Converting Pirates without Cannibalizing Purchasers: The Impact of Digital Distribution on Physical Sales and Internet Piracy. Marketing Science, 29(6), pp.1138-1151.
(4) Discontinuity
Li, X., 2018. Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold. Information Systems Research, 29(3), pp.739-754.
Cavusoglu, H., Phan, T.Q., Cavusoglu, H. and Airoldi, E.M., 2016. Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook. Information Systems Research, 27(4), pp.848-879.
(5) Artificial Shock (a.k.a. Instrument Variable)
Acemoglu, D. and Johnson, S., 2005. Unbundling Institutions. Journal of Political Economy, 113(5), pp.949-995.
Chan, J., Ghose, A. and Seamans, R., 2016. The Internet and Racial Hate Crime: Offline Spillovers from Online Access. MIS Quarterly, 40(2), pp.381-403.
Week 4 (8/7, 15:00~17:00)
- When Machine Learning Meets Econometrics
Zheng, E., Tan, Y., Goes, P., Chellappa, R., Wu, D.J., Shaw, M., Sheng, O. and Gupta, A., 2017. When Econometrics Meets Machine Learning. Data and Information Management, 1(2), pp.75-83. (Panel Discussion at CSWIM 2017; https://content.sciendo.com/view/journals/dim/1/2/article-p75.xml)
Mullainathan, S. and Spiess, J., 2017. Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), pp.87-106.
Einav, L. and Levin, J., 2014. Economics in the Age of Big Data. Science, 346(6210), p.1243089.
Athey, S. and Imbens, G.W., 2019. Machine Learning Methods That Economists Should Know About. Annual Review of Economics, 11.