/persp-model-econ_W20

Course site for MACS 30150 (Winter 2020) - Perspectives on Computational Modeling for Economics

Primary LanguageJupyter Notebook

MACS 30150 - Perspectives on Computational Modeling in Economics (Winter 2020)

Dr. Richard Evans Keertana Chidambaram
Email rwevans@uchicago.edu keertana@uchicago.edu
Office 1155 S 60th St., Room 217
Office Hours T 10:30am-12:30pm Th 1-3p, (224 Lounge)
GitHub rickecon keertanavc
  • Meeting day/time: MW 11:30am-1:20pm, Saieh Hall, Room 247
  • Office hours also available by appointment

Main text

Course description

This course is an economics-focused survey of modern computational modeling methods that are valuable to empirical, computational, and numerical research. The course begins with some basics of numerical derivatives and integrals. We then spend two days on dynamic programming, which is a very general and flexible way to pose a dynamic problem and which has powerful iterative global solution techniques. We then transition into a week-and-a-half of structural estimation methods. These methods generalize many of the specific estimation techniques that come after. Finally, we spend the last half of the course with a survey of some of the most common statistical learning/machine learning methods.

Grades

You will have 9 problem sets throughout the term. I will drop everybody's lowest problem set score. For this reason, problem sets will only account for 80 percent of your grade.

Assignment Quantity Points Total Points Percent
Problem Sets 9 10 80 80%
Midterm exam 1 20 20 20%
Total Points -- -- 100 100%

Late problem sets will be penalized 2 points for every hour they are late. For example, if an assignment is due on Monday at 11:30am, the following points will be deducted based on the time stamp of the last commit.

Example PR last commit points deducted
11:31am to 12:30pm -2 points
12:31pm to 1:30pm -4 points
1:31pm to 2:30pm -6 points
2:31pm to 3:30pm -8 points
3:30pm and beyond -10 points (no credit)

Assignment submission procedure

This folder on your fork of the class repository github.com/YourGitHubHandle/persp-model-econ_W19/ProblemSets/ is where you will submit your problem sets and project assignments. You will just commit and push your assignments to the appropriate folder ON YOUR FORK (NOT ON THIS MAIN COURSE REPOSITORY). For example, your files for PS1 should be committed to the PS1 folder on your fork of the class repository.

/persp-model-econ_W19/ProblemSets/PS1/YourFile.pdf

I will use a shell script to clone all class members' repositories at the time the assignments are due.

Disability services

If you need any special accommodations, please provide us with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.

Course schedule

Date Day Topic Readings Assignment
Jan. 6 M Model/theory building, data generating processes V1997, Slides PS1
Jan. 8 W Numerical derivatives Notes PS2
Jan. 13 M Numerical integration Notebk
Jan. 15 W Dynamic programming Notes PS3
Jan. 20 M No class (Martin Luther King, Jr. Day)
Jan. 22 W Dynamic programming
Jan. 27 M Maximum likelihood estimation (MLE) Notebk PS4
Jan. 29 W Maximum likelihood estimation (MLE)
Feb. 3 M Generalized method of moments (GMM) Notebk PS5
Feb. 5 W Generalized method of moments (GMM)
Feb. 10 M Evans Midterm
Feb. 12 W Statistical learning and linear regression JWHT Ch. 2, 3, Notebk PS6
Feb. 17 M Classification and logistic regression JWHT Chs. 2, 4, Notebk
Feb. 19 W Resampling methods (cross-validation and bootstrapping) Notebk PS7
Feb. 24 M Interpolation Notebk
Feb. 26 W Tree-based methods JWHT Ch. 8, Notebk PS8
Mar. 2 M Tree-based methods JWHT Ch. 8
Mar. 4 W Support vector machines JWHT Ch. 9, Notebk
Mar. 9 M Neural networks HTF Ch. 11, G Ch. 10 PS9
Mar. 11 W Neural networks Notebk

References and Readings

All readings are required unless otherwise noted. Adjustments can be made throughout the quarter; be sure to check this repository frequently to make sure you know all the assigned readings.

  • [A2019] Athey, Susan, ``The Impact of Machine Learning on Econometrics and Economics,'' keynote presentation, American Economics Association/American Finance Association joint luncheon at the Allied Social Sciences 2019 Annual Meeting, Atlanta, Georgia (January 5, 2019). [Slides]
  • [A2018] Athey, Susan, ``The Impact of Machine Learning on Economics,'' forthcoming in The Economics of Artificial Intelligence: An Agenda, eds. Ajay K. Agarwal, Joshua Gans, and Avi Goldfarb, National Bureau of Economic Research (forthcoming).
  • [G2017] Géron, Aurélien, Hands-On Machine Learning with Scikit-Learn & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly (2017).
  • [HTF2009] Hastie, Trevor, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition, Springer (2009).
  • [JWHT2013] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. New York: Springer.
  • [V2016] VanderPlas, Jake. (2016). Python Data Science Handbook. O'Reilly Media, Inc.
  • [V1997] Varian, Hal R., "How to Build an Economic Model in Your Spare Time," in Passion and Craft: Economists at Work, eds. Michael Szenberg, University of Michigan Press, 1997.