Monthly Archives: June 2017

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 … Continue reading

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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 … Continue reading

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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 … Continue reading

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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

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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

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