Resources
The goal of the class is for you to become familiar and proficient with some essential tools that are used in most empirical analysis.
While learning to implement all the methods we cover by hand is a great excercise to learn what they do, and how they work, it may not be a feasible practice in most real-world work, unless you decide to follow that path (Econometrics/applied Econometrics).
For this purpose, the main software we will use in this class (as evidence from all the code shared in the slides) its Stata. A self contained program that is yet flexible enough to add custom add on programs/commands.
Nevertheless, if you are new to Stata, there are quite few resources you may want to look into using this software
Stata
- Stata Free-Webinars: https://www.stata.com/training/webinar/
- Stata Past Recorded Webinars: https://www.stata.com/training/webinar_series/past-webinar-recordings/
- Stata Video-Tutorials: https://www.stata.com/links/video-tutorials/
- General Learning resources: https://www.stata.com/links/resources-for-learning-stata/
- Excellent Stata tutorial for beginners: https://grodri.github.io/stata/index
- Our own Tutorial! Stata Basics
- Also, you may want to check the following for a quick reference on
But of course, Stata is not free. There are other resources you may want to explore, if you are interested in doing econometric analysis, but no longer have access to Stata. These are R, Python and Julia.
R, Julia, Python
These software are free, but usually require add-ons from different sources to estimate specialized models. They also have a steep, or rather steep-er (than Stata) learning curve. However, it is smart to learn other languages, at least to implement basic analysis. One resource you may find very convinient is the following:
- R, Python, Julia: http://www.upfie.net/
This site and its author(s) have put together a set of companion books to go along with the Textbook “Introductory Econometrics: A Modern Approach”. These books are rather inexpensive, providing some of the authors own insights, with full code in all three languages, that replicate the examples in the textbook.
- Example Codes: http://www.upfie.net/code.html
The authors also suggest other resources that could be of interest
- Further Resources: http://www.upfie.net/links.html
Quarto
Quarto is not a programming language. Rather an interpreter that converts plain text to nicely formating documents, presentations, websites, etc. This site, for instance, was built using Quarto.
Because of this, I’m encouraging the use of Quarto, combined with nbstata/python, to produce answers to ALL homeworks or group works. So it will be easy to check and cross check your work with the code.
To use this, you need to have R-Studio here, or Visual Studio Code here with Quarto plug-in (if you use VSC) in your computers. You will also need python and nbstata.
A good place to start learning how to use Quarto for dynamic documents its here (for R-studio) or here (for VCS).
I also have a small example using Quarto with Stata here.
Try it on, and let me know if you have any problems.
Other Resources: Data
If you are looking for small and easy to work datasets for your projects, you may want to check the following:
frause: Its a repository I created to load data from the web intoStata. Contains all datasets used in Intro to Ecometrics book by Wooldridge.datasciencedojo: This is a nice website that provides access to raw data that could be used for your projects. See their webpage.