William Suzuki's repositories
minicurso-macro-fearp
Here we tried to solve some exercises of the minicurso of macro empirics, simulated method of moments and indirect inference.
bayesian-richard-mcelreath-and-others
This repo gathers code from many sources, one of them is code inspired by Richard McElreath's book, and others are articles that use Bayesian models.
finance_article
Some R codes used in my finance article
gibbs_metropolis_python
The objective of this repo is to simulate Gibbs and Metropolis-Hastings algorithms in python from scratch
kaggle-learn
Some scripts on Kaggle courses
m5-kaggle
This is a repo with code for m5 kaggle competition
MCMC-MH-tests-and-simulations
We experiment and implement some algorithms of markov chain monte carlo and metropolis hastings. The objective here is to learn those methods while implmenting them.
ML_study_group
This repo gathers all files, Python, R and presentations used in the Machine Learning Study Group from the graduate group of FEARP, year 2019.
nighttime_luminosity
This repo contains a project about animations of nighttime luminosity from Brazilian cities.
python-hands-on
This repo have some tests and exercises inspired by Aurelien Geron's hands-on machine learning
python-with-QuantEcon
Learn Python with the course of Quantitative Economics from Sargent and Stachurski's site.
RegressionSteps
The three functions explained below work together to explore and present further actions and possibilities of linear regression models. My goal is to help users experiment with the explanatory variables of a linear regression model. As a result, these experiments will help the user reach conclusions about how the initial dependent variable will change, if one of the explanatory variables is changed as wished.
simulations_maximum_likelihood_estimator
We make simulations of maximum likelihood estimation and test methods for numerical optimization
Spatial-Analysis-of-Production-Function-a-Case-for-Brazil
The objective here is to explore the production function with municipality data for Brazil.
spsas_report
São Paulo School of Advanced Sciences hosted the SPSAS Learning from Data on August 2019. This is our report on the presentations. Some of the presenters are Abu-Mostafa, Ulisses Neto, Ling Liu, and Željko Ivezić.
trabalho-econometria3
Repo for final paper for time series econometrics in Ms course FEARP USP.
Tutorial-xts-package
This is a tutorial for the xts package. It will be used in a session of the graduate students' study group of FEA RP USP.