Raul Guarini Riva PhD Candidate at Northwestern University

Resources for Economists

In this page I list some resources I collected over time that I think are useful for Economists, in special if you work either in Macroeconomics or more technical Finance questions. This page reflects 100% my own personal biases. Use/read/cite at your own discretion.

Coding

I like writing code and I do it better every day. We (empirical) economists use several tools and techniques that vary widely across areas, but some general principles apply and should be followed, IMHO.

  • Classes by Jesus Fernandez-Villaverde: special focus on the Computational Methods for Economists course. I don’t care if you don’t do Macro – this is useful for anyone looking for the best coding practices.
  • The Missing Semester of Your Compute Science Education: this is a series of online lectures taught by MIT graduate students. They cover the basics of terminals, version control, text editors, etc. Again, it does not really matter what language you use or your field of study – this is useful for everyone. From time to time I revisit this page.
  • Combining DropBox or GitHub: I hope you are already convinced you should be using some version control system like Git, and collaborating with others using GitHub. Sometimes, there is also the need to use DropBox (or Google Drive, Box, etc) to share large files like sharing data. This connection might be tricky, and this tutorial teaches you exactly what you should be doing.
  • Data Science for Economists: a class taught by Grant McDermott at the University of Oregon. This is a great class that teaches you how to use basic tools like your terminal, and Git. Grant teaches is with an R-centric flavor, but a lot of this material is language-agnostic. I recommend it to everyone.
  • Python and R Guides by Sean Higgins: Sean is a professor at Kellogg and he is super knowledgeable about empirical methods AND managing large projects with many collaborators. He has a series of guides on how to use Python, R, and even Stata for empirical work. I recommend you check his GitHub page and follow the guides.

Data Resources

Sometimes finding data is hard. These are niche-y data resources that I found over time. Remember to correctly cite the providers if you use their data.

  • Open Source Asset Pricing: a huge data warehouse with 300+ monthly series of several “anomaly” factors. There is a companion paper explaining the data.
  • Saketh Aleti’s High Frequency Data: this is a free dataset with 1-minute returns for a bunch of Asset Pricing factors. Saketh gave us a great starting point for high-frequency research that relies on risk factors. And he is a great guy as well. I don’t think it’s being updated regularly anymore.
  • NEFIN @ USP: these guys are a Finance research center at the University of São Paulo. They compute several interesting indicators for the Brazilian Market. In special, they compute the Fama-French 5 factors for Brazil.

Statistical Methods in Finance and Economics

Some material that might be helpful if you are generally curious about the intersection of Machine Learning and Economics/Finance.

  • A Similar Page by Dario Sansone: Dario is at the University of Exeter. He has curated over the years a very detailed page with many papers, online classes, and other resources about Machine Learning. He seems more interested in the usage in Economics and not so much in Finance. Super detailed and recommended, nonetheless.
  • Financial Machine Learning: this is a great review by Bryan Kelly and Dacheng Xiu on why/how to use Machine Learning in Finance. But notice that most of their discussion is for prediction purposes. See the page from Fernandez-Villaverde for a different perspective – machine learning to solve models.
  • Deep Learning for Economists: a great introduction by Melissa Dell. If you do not know where to start and you want to learn more about Deep Learning, this is a great place. Melissa teaches really well and you can see her teaching some of this material at EconDL.