By Gabriel Vasconcelos Introduction Today we are going to talk about quantile regression. When we use the lm command in R we are fitting a linear regression using Ordinary Least Squares (OLS), which has the interpretation of a model for … Continue reading →

By Gabriel Vasconcelos Introduction Vector Autorregresive (VAR) models are very popular in economics because they can model a system of economic variables and relations. Bayesian VARs are receiving a lot of attention due to their ability to deal with larger … Continue reading →

By Gabriel Vasconcelos and Yuri Fonseca The intuition Let us begin with a brief explanation about Benford’s law and why should it work as a fraud detector method. Given a set of numbers, the first thing we need to do … Continue reading →

By Gabriel Vasconcelos & Yuri Fonseca Introduction This post is the second of a series of examples of the BooST (Boosting Smooth Trees) model. You can see an introduction to the model here and the first example here. Our objective … Continue reading →

By Thiago Milagres Preallocating Memory This will be a short post about a simple, but very important concept that can drastically increase the speed of poorly written codes. It is very common to see R loops written as follows: This … Continue reading →

By Gabriel Vasconcelos and Yuri Fonseca Introduction This is the first of a series of post on the BooST (Boosting Smooth Trees). If you missed the first post introducing the model click here and if you want to see the … Continue reading →

By Gabriel Vasconcelos and Yuri Fonseca   We are happy to introduce our new machine learning method called Boosting Smooth Trees (BooST) (full article here). This model was a joint work with professors Marcelo Medeiros and Álvaro Veiga. The BooST … Continue reading →

By Henrique Helfer Hoeltgebaum Introduction I am happy to introduce the package HCmodelSets, which is now available on CRAN. This package implements the methods proposed by Cox, D.R. and Battey, H.S. (2017). In particular it performs the reduction, exploratory and … Continue reading →

By Gabriel Vasconcelos In this previous post I discussed some of the parameters we have to tune to estimate a boosting model using the xgboost package. In this post I will discuss the two parameters that were left out in … Continue reading →

By Yuri Fonseca Demand models In the previous post about pricing optimization (link here), we discussed a little about linear demand and how to estimate optimal prices in that case. In this post we are going to compare three different … Continue reading →

By Gabriel Vasconcelos Before we begin, I would like to thank Anuj for kindly including our blog in his list of the top40 R blogs! Check out the full list at his page, FeedSpot! Introduction Tuning a Boosting algorithm for … Continue reading →

By Gabriel Vasconcelos Overview There are several ways to do portfolio optimization out there, each with its advantages and disadvantages. We already discussed some techniques here. Today I am going to show another method to perform portfolio optimization that works … Continue reading →

By Gabriel Vasconcelos When dealing with forecasting models there is an issue that generates a lot of confusion, which is the difference between direct and recursive forecasts. I believe most people are more used to recursive forecasts because they are … Continue reading →

By Gabriel Vasconcelos So I decided to have a quick look at the tuber package to extract YouTube data in R. My cousin is a singer (a hell of a good one) and he has a YouTube channel (dan vasc), … Continue reading →

By Gabriel Vasconcelos Today, November 29, 2017 was a crazy day in the Bitcoin world and the craziness is still going on as I write this post. The price range was of thousands of Dollars in a few hours. Bitcoins … Continue reading →

By Gabriel Vasconcelos Overview When working with time-series forecasting we often have to choose between a few potential models and the best way is to test each model in pseudo-out-of-sample estimations. In other words, we simulate a forecasting situation where … Continue reading →

By Yuri Fonseca In this post we are going to make an Uber assignment simulation and calculate some metrics of waiting time through simulation. Setting Suppose we live in a 100×100 block city where each block takes 1 minute to … Continue reading →

By Gabriel Vasconcelos Regression Trees In this post I am going to discuss some features of Regression Trees an Random Forests. Regression Trees are know to be very unstable, in other words, a small change in your data may drastically … Continue reading →