Codes and source files for papers
Below are the links to the code and source files that can be used to replicate the results of some papers I published.
- Arellano Bond Panel VAR, Eviews (Used in Góes, Carlos. Institutions and growth: A GMM/IV Panel VAR approach. Economics Letters, v. 138, p. 85-91, 2016).
- Pedroni Panel VAR with heterogeneous dynamics, Eviews (Used in Góes, C. 2016. “Testing Piketty’s Hypothesis on the Sources of Income Inequality: Evidence from a Panel with Heterogeneous Dynamics”. IMF Working Paper.)
- Panel Cointegration with Price Indices to test for Law of One Price, STATA+Eviews (Used in Góes & Matheson. Domestic market integration and the law of one price in Brazil. Applied Economics Letters (Print), v. 23, p. 1-5, 2016).
- Descriptive statistics and income-dependency of Brazilian public higher-ed students, using survey/census microdata, Stata (Used in Góes, C.; Duque, D. (2016). “Como as universidades públicas no Brasil perpetuam a desigualdade de renda: fatos, dados e soluções”. Nota de Política Pública n. 01/2016. São Paulo: Instituto Mercado Popular.)
- Analysis of political alliances and coallition at the mayoral level in Brazil, STATA+R (Used in Meira, R.; Góes, C. (2016). “Uma radiografia das eleições municipais brasileiras (1996-2016): fatos e dados”. Nota de Política Pública n. 02/2016. São Paulo: Instituto Mercado Popular.)
Tinyapps are some small scripts I have coded to support research or work, but are not related to research itself. I made them available both for teaching purposes (my students might get some inspiration) and to potentially make the life of my fellow researchers easier.
- PyComtrade: implements the International Trade Statistics database API, pulls up data for partners, and years requested, and organize them into a Pandas DataFrame.
- USPTO Scrapping: This script scrapes the data on patent counts by origin and type, available on the website of the U.S. Patent and Trademark Office (USPTO) as a html table, and organizes it in a Pandas DataFrame.
Tutorials are very annotated (but not necessarily structured) lessons on different topics. The annotations are meant to help the reader learn what the code is doing and learn by reading and replying it.
- Infinite discrete time deterministic neoclassical growth model, Python.
- Details of Value Function Iteration, Python.
- Automatic Download and Manipulation of World Bank Data, Python.
Other material in: