# Category Archives: R

## Online portfolio allocation with a very simple algorithm

By Yuri Resende Today we will use an online convex optimization technique to build a very simple algorithm for portfolio allocation. Of course this is just an illustrative post and we are going to make some simplifying assumptions. The … Continue reading

## When the LASSO fails???

By Gabriel Vasconcelos When the LASSO fails? The LASSO has two important uses, the first is forecasting and the second is variable selection. We are going to talk about the second. The variable selection objective is to recover the correct … Continue reading

## Non gaussian time-series, let’s handle it with score driven models!

By Henrique Helfer Motivation Until very recently, only a very limited classes of feasible non Gaussian time series models were available. For example, one could use extensions of state space models to non Gaussian environments (see, for example, Durbin and … Continue reading

## Complete Subset Regressions, simple and powerful

By Gabriel Vasconcelos The complete subset regressions (CSR) is a forecasting method proposed by Elliott, Gargano and Timmermann in 2013. It is as very simple but powerful technique. Suppose you have a set of variables and you want to forecast … Continue reading

## Bagging, the perfect solution for model instability

By Gabriel Vasconcelos Motivation The name bagging comes from boostrap aggregating. It is a machine learning technique proposed by Breiman (1996) to increase stability in potentially unstable estimators. For example, suppose you want to run a regression with a few … Continue reading

## Problems of causal inference after selecting controls

By Gabriel Vasconcelos Inference after model selection In many cases, when we want to estimate some causal relationship between two variables we have to solve the problem of selecting the right control variables. If we fail, our results will be … Continue reading

## 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