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08/22/2023
Forecasting inflation using disaggregates and machine learning
This paper examines the effectiveness of several forecasting methods for...
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03/22/2023
Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage
We propose a model to forecast large realized covariance matrices of ret...
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10/01/2021
The Impacts of Mobility on Covid-19 Dynamics: Using Soft and Hard Data
This paper has the goal of evaluating how changes in mobility has affect...
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09/04/2021
Modeling the Evolution of Infectious Diseases with Functional Data Models: The Case of COVID-19 in Brazil
In this paper, we apply statistical methods for functional data to expla...
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04/07/2021
The Proper Use of Google Trends in Forecasting Models
It is widely known that Google Trends have become one of the most popula...
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02/22/2021
Bridging factor and sparse models
Factor and sparse models are two widely used methods to impose a low-dim...
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12/23/2020
Machine Learning Advances for Time Series Forecasting
In this paper we survey the most recent advances in supervised machine l...
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11/08/2020
Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction
The measurement of treatment (intervention) effects on a single (or just...
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09/29/2020
Online Action Learning in High Dimensions: A New Exploration Rule for Contextual ε_t-Greedy Heuristics
Bandit problems are pervasive in various fields of research and are also...
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09/28/2020
Lockdown effects in US states: an artificial counterfactual approach
We adopt an artificial counterfactual approach to assess the impact of l...
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12/19/2019