Google Summer of Code 2020
This year I was selected to participate in the Google Summer of Code program. Google Summer of Code is a global program focused on introducing students to open source software development. It consists on a 3 month programming project with an open source organization. Specifically, I will work in a PySAL Project on Panel Data Spatial Econometrics.
This project aims to extend the functionality of the submodule of Spatial Regression Models of PySAL. This submodule is spreg, short for “spatial regression”. The goal is to deal with panel data econometric models. Spatial panels refer to data containing time series observations of a number of geographical units. Specifically, this project will focus on static panel models with fixed and random effects.
To prepare myself for this challenging project, I have been reading a lot of documentation on two topics: 1. How to work on GitHub for open source projects, and 2. Estimation of Spatial Panel data models. For this reason, I want to to share with you the resources that I have found most useful and valuable.
- You should look first for the development guidelines of the open source project you want to collaborate. In PySAL’s case, it is GitHub Standard Operating Procedures.
- The online book Pro Git is an amazing resource to learn the basic concepts of git.
- Then, I got some useful and quick help for branches and forks.
Spatial Panel resources
- Anselin, Luc, Julie Le Gallo and Hubert Jayet (2008). Spatial panel econometrics. In L. Matyas and P. Sevestre (Eds.), The Econometrics of Panel Data, Fundamentals and Recent Developments in Theory and Practice (3rd Edition), pp. 627-662. Berlin: Springer-Verlag.
- Elhorst, J. Paul (2014). Spatial Econometrics, From Cross-Sectional Data to Spatial Panels. Berlin: Springer-Verlag.
- Millo, G., & Piras, G. (2012). splm: Spatial Panel Data Models in R. Journal of Statistical Software, 47(1), 1 - 38. doi:http://dx.doi.org/10.18637/jss.v047.i01
- James LeSage and Robert Kelley Pace (2009). Introduction to Spatial Econometrics. Chapman and Hall/CRC.