By Gabriel Vasconcelos In many cases, especially if you are dealing with forecasting models, it is natural to use a large set of models to forecast the same variable and then select the best model using some error measure. For … Continue reading →

By Yuri Fonseca Basic idea In this post we will discuss briefly about pricing optimization. The main idea behind this problem is the following question: As manager of a company/store, how much should I charge in order to maximize my … Continue reading →

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 … Continue reading →

By Gabriel Vasconcelos In a recent paper that 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 … Continue reading →

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 … Continue reading →

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 … Continue reading →

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 →

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 →

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 →

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 →

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 →

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 →

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 →

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 →

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 →

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 →