7 citations · 30 across the 13 of their papers we have counts for
6 papers · 1 filter
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…