The package hdm for double selection inference with a simple example

By Gabriel Vasconcelos

In a late post I discussed the Double Selection (DS), a procedure for inference after selecting controls. I showed an example of the consequences of ignoring the variable selection step discussed in an article by Belloni, Chernozhukov and Hansen.

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Counterfactual estimation on nonstationary data, be careful!!!

By Gabriel Vasconcelos

In a recent paper, which can be downloaded here, Carvalho, Masini and Medeiros show that estimating counterfactuals in a non-stationary framework (when I say non-stationary it means integrated) is a tricky task. It is intuitive that the models will not work properly in the absence of cointegration (spurious case), but what the authors show is that even with cointegration, the average treatment effect (ATE) converges to a non-standard distribution. As a result, standard tests on the ATE will identify treatment effects in several cases when there is no effect at all.

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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.
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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.

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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).

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Online portfolio allocation with a very simple algorithm

By Yuri Fonseca

 

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.

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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.

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