ArCo Package v 0.2 is on

The ArCo package 0.2 is now available on CRAN. The functions are now more user friendly. The new features are:

  • Default function for estimation if the user does not inform the functions fn and p.fn. The default model is Ordinary Least Squares.
  • The user can now add extra arguments to the fn function in the call.
  • The data will be automatically coerced when possible.
Posted in R | Tagged , , , | 2 Comments

Dealing with S3 methods in R with a simple example

By Gabriel Vasconcelos

S3 objects

R has three object systems: S3, S4 and RC. S3 is by far the easiest to work with and it can make you codes much understandable and organized, especially if you are working on a package. The idea is very simple. First we must define a class to some object in R and then we define methods (functions) for this class based on generic functions that you may create or use the ones available.

Continue reading

Posted in R | Tagged , , , , | 3 Comments

Cross-Fitting Double Machine Learning estimator

By Gabriel Vasconcelos

Motivation

In a late post I talked about inference after model selection showing that a simple double selection procedure is enough to solve the problem. In this post I’m going to talk about a generalization of the double selection for any Machine Learning (ML) method described by Chernozhukov et al. (2016).

Continue reading

Posted in R | Tagged , , , , , | 3 Comments

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 objective is to point out an interesting direction to approach the problem of portfolio allocation.

Continue reading

Posted in R | Tagged , , , , | 6 Comments

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 set of variables that generate the data or at least the best approximation given the candidate variables. The LASSO has attracted a lot of attention lately because it allows us to estimate a linear regression with thousands of variables and the model select the right ones for us. However, what many people ignore is when the LASSO fails.

Continue reading

Posted in R | Tagged , , , , , | 21 Comments

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 Koopman (2012)), but extensive Monte Carlo simulation is required to numerically evaluate the conditional densities that define the estimation process of such models.

Continue reading

Posted in R | Tagged , , , , , | 3 Comments

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 one of them using information from the others. If your variables are highly correlated and the variable you want to predict is noisy you will have collinearity problems and in-sample overfitting because the model will try to fit the noise.

Continue reading

Posted in R | Tagged , , , , | Leave a comment