I hope that this becomes a time capsule. I've picked a projects that I worked on during college. I feel pretty proud of tracking the improvement on my coding skills. It's been nearly three years since I first took a Python class, and I have been it almost daily since them.
This is a summary of the main work I've done during my Economics undergraduate at Fundacao Getulio Vargas (2019 - 2022). Those were four years of intense learning, a lot of economic theory, and discoveries. Four years ago, I simply didn't know the universe of really interesting things I would discover over that journey. This repository is just a time mechine for it all.
My first contact with programming languages was actually in Python. I would just discover R months later. It was a 60h introductory course with the basic syntax and helpful libraries on explanatory data analysis (EDA).
The final project was composed of a simple task: clean a dataset and analyze it. By that time, I was enrolled in a research project on the Brazillian meat market, and I picked a related dataset.
So, I've downloaded a 12k rows FAO time series with meat production, by country, since 1990. That main challenge was to clean it, in such a way that I could present the main producers over time, by meat type. It took me some two weeks - and a lot of help from Stack Overflow - to make the interactive charts on Plotly.
Here you can find the script and the original database.
(Seriously, I have no idea what I was doing in that piece of script. Luckily, now I regularly comment codes and understandably call stuff)
It is so interesting to check it two years later. Progress since then is steady and it has been worth spending hours learning Python with a real-life example.
Then, I stepped into R. In early 2021 I took a 60h R course, and since then, I've been using it regularly. By that time, I was enrolled in two related projects: the course final one, and an undergraduate research (PIBIC). Both were about the same topic: the Economic History of Brazil, Chile, and Argentina, during the 70s and the 80s.
In both projects, I compiled those countries macroeconomic data, including time series on inflation, GDP, population, growth rates, international trade, and government spending. Sources include the World Bank, IBGE, Indec, INE, and the Fed.
(I can't believe I used double loops. If this project taught me something thing it was: that there is a package for everything you need)
This was the first contact I had with R. It is so interesting to see how my coding skills have improved since then. By that time, I didn't know many useful packages like {lubridate} and {tidylog}. I clearly remember that I spent a whole day trying to covert a wide-format table into a long one. I solved it with an inefficient double loop, that could be easily replaced by tidyr::pivot_longer( ).
Since then, I've learned a lot of new DataViz techniques and kept studying R. The next step is to learn machine learning and sophisticated DataViz tools.
In late 2021, I took a course on time series. In Econometrics 2, we learned AR, MA, ARMA, ARCH/GARCH, VAR, and PCA models. As a final project, we could apply one of into a real-life problem.
As I was a teacher assistant on a course on the Amazon, I tried to forecast Brazilian deforestation rate. So, I estimated a VAR model, using deforestation, cattle slaughtering, and government spending data.
Then, I run the diagnosis tests on the data. First, I used an augmented Dickey-Fuller test (ADF) to check if the time series were stationary. Then, I used the information criteria to select the optimal lag. In the end, the candidates were VAR models with one, four, five, and twelve lags.
To choose the best model, another four tests were put into practice: the Ljung-Box (LB) test to check the absence of serial autocorrelation, the Grager test to check the presence of Grange causality, and the Breusch-Pagan (BP) test to check the homoscedasticity of residuals.
After it all, the best performing model was a VAR(5), which was estimated by OLS. Unfortunately, all candidates failed the Granger test for causality, on the deforestation component. This led consistent, but inneficient models.
Lastly, I showed the OLS-estimated coefficients, impulse-response functions, and future deforestation estimates.
Here you can find the script, original datasets, and the final report.
Coefficients table. Only 14 of the 48 coefficients passed the significance test.
(In this project, alongside the ordinary EDA packages, I used {aTSA}, {vars}, {stats} and {lmtest} to perform the econometrics.)
I clearly remember spending days understanding the econometrics of time series. Econometrics with R helped me a lot. This was the first time I coined a model with real data. Unfortunately, the lack of Granger causality led to inefficient results.
In early 2022 I was intern at Observatório de Bioeconomia, the FGV depertament responsable to research environemnt-related topics. By that time, we have published a report on the Brazilian voluntary carbon market, and I was passionate about it. That's why I enrolled in an environmental economics course.
In this semester, we learned the theory and saw some of its applications. As a final project, we had to present a paper about one of the discussed topics. So, I presented Bayer and Aklin (2020). In this paper, the authors estimated the effectiveness of the EU Emissions Trading System using a Synthetic Control Method. Using UNFCCC and EU data, they found that in the absence of the ETS, between 2008 and 2016, the bloc's emissions would be nearly 4% higher than they were. That is, the EU would have emitted additional 1.2 bn tons of CO2.
To show the paper results, I used the common EDA packages and DataViz techniques. This was an opportunity to get in touch with causal inference literature and improve DataViz skills. The presentation slides were done in Canva, and it is all available here.
Lastly, I also worked with carbon markets on Law and Economics final project. This was a course mainly about the Economic Analysis of Law, and Coase Theorem was one of the main topics. According to Coase (1960), under perfect information, low transition costs, and full property rights, Pareto efficiency is always hit. In real life, this is the background of carbon trading systems.
In this course, the grading structure was comprised of a project in which each group should perform a hypothesis test. Having the EU ETS in mind, we asked ourselves: could Brasil import European carbon market rules? As half of the Brazilian emissions are related to deforestation and cattle raising, two activities that are hard to monitor, we suspected that Brazil wouldn't have an efficient carbon market if just importing EU rules.
Although we didn't use sophisticated econometrics, my group used official data to estimate that an EU-like Brazilian ETS wouldn't cover much of the country's emissions. Due to the high proportion of non-energy-related emissions in Brazil, if policymakers in Brasilia desire to create a tropical carbon market, they should think about a manner to tackle deforestation and tax cattle emissions.
Like I did on the environmental economics summary, I used R to make the data cleaning, and {ggplot2} to do the DataViz. This was an opportunity to deal with Coase literature, and it might be the seed of some future project.
Here you can check the code and the final report.
By now, in some months I`ll officially be an economist. I clearly remember how tough was the first year. Luckily I didn't give up and learned dozens of amazing things. I hope that this is just the beginning.