7 citations · 30 across the 13 of their papers we have counts for
15 papers
Forecasting inflation using disaggregates and machine learning
Gilberto Boaretto, Marcelo C. Medeiros
This paper examines the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts - also known in the literature as the…
Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage
Rafael Alves, Diego S. de Brito, Marcelo C. Medeiros +1
We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we deco…
Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach
Iuri H. Ferreira, Marcelo C. Medeiros
In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minu…
The Impacts of Mobility on Covid-19 Dynamics: Using Soft and Hard Data
Leonardo Martins, Marcelo C. Medeiros
This paper has the goal of evaluating how changes in mobility has affected the infection spread of Covid-19 throughout the 2020-2021 years. However, identifying a "clean" causal re…
Modeling the Evolution of Infectious Diseases with Functional Data Models: The Case of COVID-19 in Brazil
Julian A. A. Collazos, Ronaldo Dias, Marcelo C. Medeiros
In this paper, we apply statistical methods for functional data to explain the heterogeneity in the evolution of number of deaths of Covid-19 over different regions. We treat the c…
The Proper Use of Google Trends in Forecasting Models
Marcelo C. Medeiros, Henrique F. Pires
It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many pap…
Bridging factor and sparse models
Jianqing Fan, Ricardo Masini, Marcelo C. Medeiros
Factor and sparse models are two widely used methods to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting…
Machine Learning Advances for Time Series Forecasting
Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alter…
Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction
Jianqing Fan, Ricardo P. Masini, Marcelo C. Medeiros
Optimal pricing, i.e., determining the price level that maximizes profit or revenue of a given product, is a vital task for the retail industry. To select such a quantity, one need…
Online Action Learning in High Dimensions: A Conservative Perspective
Claudio Cardoso Flores, Marcelo Cunha Medeiros
Sequential learning problems are common in several fields of research and practical applications. Examples include dynamic pricing and assortment, design of auctions and incentives…
Lockdown effects in US states: an artificial counterfactual approach
Carlos B. Carneiro, Iúri H. Ferreira, Marcelo C. Medeiros +2
We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explor…
Short-Term Covid-19 Forecast for Latecomers
Marcelo Medeiros, Alexandre Street, Davi Valladão +2
The number of Covid-19 cases is increasing dramatically worldwide. Therefore, the availability of reliable forecasts for the number of cases in the coming days is of fundamental im…
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models
Mehmet Caner, Marcelo Medeiros, Gabriel Vasconcelos
We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe ratio esti…
Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations
Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted…
BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions
Yuri Fonseca, Marcelo Medeiros, Gabriel Vasconcelos +1
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boos…