Tyson D. Van Alfen

Occasionally I find or create resources that I think may be helpful to others. Links to the resources are included below.



I created this program to convert SIC codes to any of the Fama French industries listed on Ken French's site. I'm sure everyone has those obnoxious lines of code to do this somewhere, but this should save you some space in your .do files. You can install the program directly from Stata using ssc install sicff, or you can download this .zip file that contains the .ado file as well as the help file for the program.


According to the Gtools GitHub page, this is "Faster Stata for big data". It dramatically speeds up commands such as reshape, duplicates, xtile, distinct, and egen to name a few. I highly recommend it. ssc install gtools


Created by Sergio Correia, this is "a Stata package that runs linear and instrumental-variable regressions with many levels of fixed effects, by implementing the estimator of Correia (2015)". This package is incredibly useful when estimating multiple fixed effects, multi-way clustering, and more. Install this program directly by running ssc install reghdfe from the Stata command prompt. See here for more information.


According to the GitHub page, "Parallel lets you run Stata faster, sometimes faster than MP itself." I have not used this package yet, but it looks promising. Do not use ssc install for this package. Instead install the version from GitHub which is more up to date.


Created by Paul Geertsema, this program will assist you in running many small regressions (think rolling regressions). Stata's default method is orders of magnitude slower when the number of regressions is large. See his paper on SSRN for an explanation of the code. There is unfortunately no help file, but here is the .ado file.



Linearmodels is a great tool for panel regressions, IV estimators, and more in Python. See the GitHub page. It does not seem to be as powerful as Stata's RegHDFE, but the developers of Linearmodels are actively adding features. pip install linearmodels


If you are interested in web crawling, scraping, and parsing, these three Python packages are indispensable. Requests is an HTTP library for Python that can be simple for easy applications, yet powerful when it needs to be. If you need data from the web start here. Once you have access to the pages you'll probably want to use Beautiful Soup to parse the important data from the HTML. If the data you want is hidden behind some Javascript then Selenium might be the tool that you need.


Pandas is an impressive open-source package for Python that allows you to implement easy-to-use data structures and tools. Its features and capabilities are many, so I would refer anyone who is interested in using Python for data management or analysis to the Pandas tutorials.


The following links contain a template and supporting files for compiling a dissertation that meets the graduate school requirements at the University of Kentucky. Zip file and supporting information. Many thanks go to David Moore and Erik Stokes for providing the initial template and starting points. Hopefully this saves future students a few hours.