This repository includes notebooks and exercises for the class
Research Methods and Quantitative Analysis
Please note that we will be using Python and Jupyter Notebooks for all our lectures and data analyses. There are two set up options:
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Using Google Colaboratory: this option doesn't require any installation and you can just follow the below links. Note that it might be helpful to install the google colab chrome extension.
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Installing Python / Jupyter Notebooks: this requires installation on your local machine and downnloading the files:
(Note that I will also put all files in our google classroom google drive)
If you have any questions or encounter technical issues please don't hesitate to contact me via email or via google classrooms.
Note that we will be using the first session to ensure proper technical setup, also.
- Exercise 1: link; NEW: solution
- uncertainty estimation & hypothesis testing: link
- linear regression analysis: link
- no exercises; first project will follow instead
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dataset (only required if you do not want to use Google Colab template but work on your local machine)
- pandas & tidy data: link
No lecture, but Q&A session regarding midterm project
- Classification: Logistic regression: link
- Clustering: K-means clustering: link
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Athey/Imbens (2019) - "Machine Learning Methods Economists Should Know About"
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Mullainathan/Spiess (2017) - "Machine Learning: An Applied Econometric Approach"
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Shmueli (2010) - "To Explain or to Predict?"
Please find final exercises here