ECO 4370 / 6370 Computing in Economics

Fall 2026

Instructor: Zhan Gao
Course information: See Syllabus.

Lecture Notes

0. Course Overview
1. R Programming (Basic)
2. R Programming (Advanced)
3. Git(hub)
4. Data Wrangling and Tidying
5. Taming the Data Zoo: Cross-Sectional, Time Series and Panel
   • cps09mar.csv
   • PredictorData2018.xlsx
6. Taming the Data Zoo: Spatial Data and GIS
   • china_map_dt.Rdata
   • counties_treated.Rdata
7. Taming the Data Zoo: Text Data
8. Linear Regression
   • oj.csv
   • redfinLA3.csv
   • web-browsers.csv
9. Logistic Regression
   • spam.csv
   • tripadvisor.RData
   • llm-arena
10. CLT and Bootstrap
    • web-browsers.csv
11. Machine Learning Basics
12. Regularized Regression
    • browser-totalspend.csv
    • browser-domains.csv
    • browser-sites.txt
13. Trees and Random Forests
    • CAhousing.csv
    • nbc_demographics.csv
    • nbc_showdetails.csv
    • prostate.csv
14. Unsupervised Learning
    • protein.csv
    • rollcall-votes.csv
    • rollcall-members.csv
    • 2024-07-fredmd.csv
15. Causal Inference
    • causal_inference_data.zip (Oregon experiment)
    • abortion.dat
    • us_cellphone.csv
    • dw_experimental_data.csv

Homeowrk Assignments

Acknowledgments & References

• Part of the course content related to programming and data science tools is adapted from Grant McDermott's Data Science for Economists course and Zhentao Shi's course with the same title.
• Some portions of the course content related to machine learning and causal inference are built upon lecture notes developed by Michael Leung, Tim Armstrong and Roger Moon at USC based on the textbook Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions by Matt Taddy.
• Some portions of the course content related to regression models and regularized regression leverage on the lecture notes developed by Jacob Bien at USC for the course DSO 699.
• Other key references
• R for Data Science by Hadley Wickham and Garrett Grolemund
• Advanced R by Hadley Wickham
• An Introduction to Statistical Learning (with applications in R) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
• Mostly Harmless Econometrics by Joshua D. Angrist and Jörn-Steffen Pischke
• Applied Causal Inference Powered by ML and AI by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis
• For Python users:
• Python for Data Analysis by Wes McKinney
• Coding for Economists by Arthur Turrell
• An Introduction to Statistical Learning (with applications in Python) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani and Jonathan Taylor
• Detailed reference lists will be included in the lecture notes.