https://github.com/matteocourthoud/Machine-Learning-for-Economic-Analysis

Material for the exercise sessions of master course Machine Learning for Economic Analysis @UZH
https://github.com/matteocourthoud/Machine-Learning-for-Economic-Analysis

course data-science econometrics economics machine-learning phd python statistics

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Material for the exercise sessions of master course Machine Learning for Economic Analysis @UZH

• Host: GitHub
• URL: https://github.com/matteocourthoud/Machine-Learning-for-Economic-Analysis
• Owner: matteocourthoud
• License: mit
• Created: 2020-09-19T08:15:16.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-02-28T13:09:12.000Z (over 4 years ago)
• Last Synced: 2023-04-05T13:21:31.953Z (about 3 years ago)
• Topics: course, data-science, econometrics, economics, machine-learning, phd, python, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 89.7 MB
• Stars: 44
• Watchers: 4
• Forks: 23
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Machine Learning for Economic Analysis

⚠️ **WORK IN PROGRESS!** ⚠️

Welcome to my notes for the Machine Learning for Economic Analysis course by [Damian Kozbur](https://www.econ.uzh.ch/en/people/faculty/kozbur.html) @UZH!

The exercise sessions are entirely coded in [Python](https://www.python.org/downloads/) on Jupyter Notebooks. The examples heavily borrow from [*An Introduction to Statistical Learning*](https://hastie.su.domains/ISLR2/ISLRv2_website.pdf) by James, Witten, Tibshirani, Friedman and its advanced version [*Elements of Statistical Learning*](https://hastie.su.domains/Papers/ESLII.pdf) by Hastie, Tibshirani, Friedman. Other recommended free resources are the documentation of the Python library [scikit-learn](https://scikit-learn.org/) and Bruce Hansen's [*Econometrics*](https://www.ssc.wisc.edu/~bhansen/econometrics/) book.

Please, if you find any typos or mistakes, [open a new issue](https://help.github.com/articles/creating-an-issue/). Or even better, fork the repo and [submit a pull request](https://help.github.com/articles/creating-a-pull-request-from-a-fork/). I am happy to share my work and I am even happier if it can be useful.

## Content

1. [OLS Regression](https://matteocourthoud.github.io/course/ml-econ/01_regression/)

   - ISLR, chapter 3

   - ESL, chapter 3

   - Econometrics, chapters 3 and 4

2. [Instrumental Variables](https://matteocourthoud.github.io/course/ml-econ/02_iv/)

   - Econometrics, chapter 12.1-12.12

3. [Nonparametric Regression](https://matteocourthoud.github.io/course/ml-econ/03_nonparametric/)

   - ISLR, chapter 7

   - ESL, chapter 5

   - Econometrics, chapters 19 and 20

4. [Cross-Validation](https://matteocourthoud.github.io/course/ml-econ/04_crossvalidation/)

   - ISLR, chapter 5

   - ESL, chapter 7

5. [Lasso and Forward Regression](https://matteocourthoud.github.io/course/ml-econ/05_regularization/)

   - ISLR, chapter 6

   - ESL, chapters 3 and 18

   - Econometrics, chapter 29.2-29.5

6. [Convexity and Optimization](https://matteocourthoud.github.io/course/ml-econ/06_convexity/)

7. [Trees and Forests](https://matteocourthoud.github.io/course/ml-econ/07_trees/)

   - ISLR, chapter 8

   - ESL, chapters 9, 10, 15, 16

   - Econometrics, chapter 29.6-29.9

8. [Neural Networks](https://matteocourthoud.github.io/course/ml-econ/08_neuralnets/)

   - ESL, chapter 11

9. [Post-Double Selection](https://matteocourthoud.github.io/course/ml-econ/09_postdoubleselection/)

  	- Econometrics, chapter 3.18

  	- Belloni, Chen, Chernozhukov, Hansen (2012)

  	- Belloni, Chernozhukov, Hansen (2014)

  	- Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2018)

10. [Unsupervised Learning](https://matteocourthoud.github.io/course/ml-econ/10-unsupervised/)

  	- ISLR, chapter 10

  	- ESL, chapter 14

## Pre-requisites

Students should be familiar with the following concepts:

-  Matrix Algebra

   - Econometrics, appendix A.1-A.10

-  Conditional Expectation and Projection

 -  Econometrics, chapter 2.1-2.25

-  Large Sample Asymptotics

 -  Econometrics, chapter 6.1-6.5

-  Python basics

   - [Quant-Econ Tutorial](https://python.quantecon.org/index_learning_python.html)

## Readings

- Athey, S., & Imbens, G. W. (n.d.). *Machine Learning Methods Economists Should Know About*. 62.

- Belloni, A., Chen, H., Chernozhukov, V., & Hansen, C. B. (2012). Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain. *Econometrica*, *80*(6), 2369–2429. https://doi.org/10.3982/ECTA9626

- Belloni, A., Chernozhukov, V., & Hansen, C. (2014). Inference on Treatment Effects after Selection among High-Dimensional Controls. *The Review of Economic Studies*, *81*(2), 608–650. https://doi.org/10.1093/restud/rdt044

- Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. *The Econometrics Journal*, *21*(1), C1–C68. https://doi.org/10.1111/ectj.12097

- Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015). Characterizing the spatial structure of defensive skill in professional basketball. *The Annals of Applied Statistics*, *9*(1), 94–121. https://doi.org/10.1214/14-AOAS799

- Gentzkow, M., Shapiro, J. M., & Taddy, M. (2019). Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech. *Econometrica*, *87*(4), 1307–1340. https://doi.org/10.3982/ECTA16566

- Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017). Human Decisions and Machine Predictions. *The Quarterly Journal of Economics*. https://doi.org/10.1093/qje/qjx032

- Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction Policy Problems. *American Economic Review*, *105*(5), 491–495. https://doi.org/10.1257/aer.p20151023

- Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. *Journal of Economic Perspectives*, *31*(2), 87–106. https://doi.org/10.1257/jep.31.2.87

- Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. *Journal of the American Statistical Association*, *113*(523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839

## Thanks

These exercise sessions heavily borrow from

- [Jordi Warmenhoven's](https://github.com/JWarmenhoven) git repo [ISLR-python](https://github.com/JWarmenhoven/ISLR-python)

- [Quant-Econ](https://quantecon.org/python-lectures/) website

- Prof. [Damian Kozbur](https://www.econ.uzh.ch/en/people/faculty/kozbur.html) past UZH [PhD Econometrics Class](https://matteocourthoud.github.io/econometrics/)

- Clark Science Center [Machine Learning couse](http://www.science.smith.edu/~jcrouser/SDS293/)

- UC Berkeley [Convex Optimization and Approximation class](https://ee227c.github.io/) by [Moritz Hardt](http://mrtz.org/)

- [Morvan Zhou](https://github.com/MorvanZhou/) and [Yunjey Choi](https://github.com/yunjey/) [pytorch](https://github.com/MorvanZhou/PyTorch-Tutorial) [tutorials](https://github.com/yunjey/pytorch-tutorial)

- [Daniel Godoy](https://medium.com/@dvgodoy) excellent article on Pytorch in [Medium's towardsdatascience](https://towardsdatascience.com/understanding-pytorch-with-an-example-a-step-by-step-tutorial-81fc5f8c4e8e)

## Contacts

If you have any issue or suggestion for the course, please feel free to [pull edits](https://github.com/matteocourthoud/Machine-Learning-for-Economic-Analysis-2020/pulls) or contact me [via mail](mailto:matteo.courthoud@uzh.ch). All feedback is greatly appreciated!