This course offers a unified introduction to the principles and methods of statistical modeling and causal inference – two areas essential to data analysis in economics. The first part of this course introduces learning theory and a number of modern machine learning methods used for pattern recognition and predictive modeling. The second part introduces the theory of causal inference and surveys frequently used econometric techniques for causal effect learning and program evaluation. Finally, we discuss structural estimation and offer a unified perspective on the use of reduced-form and structural econometric methods.
Throughout the course, methods are demonstrated with applications to actual and simulated problems in various fields of applied economics, such as labor economics, industrial organization, finance, and marketing. The course spans the fields of econometrics, statistics, and computer science. Although the focus is on the analysis of economic data, the theories and the tools presented should be useful for a wide range of research areas in business and the social sciences.
For more information, see the course website at https://jiamingmao.github.io/data-analysis/
The goal of this course is to equip students with both a solid theoretical foundation, and the tools they need to conduct hands-on empirical research using state-of-the-art technology. The lecture materials are written to be both deep conceptually and easy to follow technically. Throughout the course, methods are demonstrated with applications to actual and simulated problems in various fields of applied economics, such as labor economics, industrial organization, finance, and marketing. Students will learn how to explore and analyze large high-dimensional datasets, choose appropriate methods for answering different types of queries, including associational, causal, and counterfactual, as well as gaining valuable computational skills.
You are expected to have familiarity with basic econometrics, statistics, and probability theory, as well as at least one programming/statistical computing language. We provide ample data analysis problems for you to work through in this course. The course lectures are written in R.
Undergraduate
Angrist, J. D. and J. Pischke. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
Cameron, A. C. and P. K. Trivedi. (2010). Microeconometrics using Stata (Revised ed.). Stata Press.
Hernán, M. A. and J. M. Robins (2019). Causal Inference. CRC Press.
James, G., D. Witten, T. Hastie, and R. Tibshirani. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
Morgan, S. L. and C. Winship. (2007). Counterfactuals and Causal Inference: Methods and Principles for Social Research.
Graduate
Abu-Mostafa, Y. S., M. Magdon-Ismail, and H. Lin. (2012). Learning from Data. AMLBook.
Cameron, A. C. and P. K. Trivedi. (2005). Microeconometrics: Methods and Applications. Cambridge University Press.
Hastie, T., R. Tibshirani, and J. Friedmand. (2008). The Elements of Statistical Learning (2nd ed.). Springer.
Pearl, J. (2009). Causality: Models, Reasoning and Inference (2nd ed.). Cambridge University Press.
Wooldridge, J. M. (2011). Econometric Analysis of Cross Section and Panel Data (2nd ed.). The MIT Press.