Monthly Archives: April 2017

Realy, Realy Big VARs

By Gabriel Vasconcelos Overview If you have studied Vector Autorregressive (VAR) models you are probably familiar with the “curse of dimensionality” (CD). It is very frustrating to see how ordinary least squares (OLS) fails to produce reliable results even for … Continue reading

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New Publication: Real-time inflation forecasting with high-dimensional models: the case of Brazil

Check out our new publication on forecasting inflation using large datasets and statistical learning techniques. Real-time inflation forecasting with high-dimensional models: The case of Brazil International Journal of Forecasting (2017) Márcio Garcia, Marcelo C. Medeiros, Gabriel F. R. Vasconcelos Link

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American Bond Yields and Principal Component Analysis

By Yuri Fonseca The idea of this post is to give an empirical example of how Principal Component Analysis (PCA) can be applied in Finance, especially in the Fixed Income Market. Principal components are very useful to reduce data dimensionality … Continue reading

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A little R prank

By Gabriel Vasconcelos R functions The book Advanced R, by Hadley Wickham, shows a very interesting statement: “To understand R, two slogans are Helpful: Everything that exists in an object. Everything that happens is a function call.” – John Chambers

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Introducing the ArCo package

By Gabriel Vasconcelos What is the ArCo?? We recently launched the R package ArCo. It is an implementation of the Artificial Counterfactual method proposed by Carvalho, Masini and Medeiros (2016). This post will review some of its features and show … Continue reading

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LASSO, adaLASSO and the GLMNET package

By Gabriel Vasconcelos Motivation If you are close to the data science world you probably heard about LASSO. It stands for Least Absolute Shrinkage and Selection Operator. The LASSO is a model that uses a penalization on the size of … Continue reading

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