Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/249727 
Year of Publication: 
2020
Series/Report no.: 
Texto para discussão No. 679
Publisher: 
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, Rio de Janeiro
Abstract: 
In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are brie y reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.
Subjects: 
Machine learning
statistical learning theory
penalized regressions
regularization
sieve approximation
nonlinear models
neural networks
deep learning
regression trees
random forests
boosting
bagging
forecasting
JEL: 
C22
Document Type: 
Working Paper

Files in This Item:
File
Size
1.37 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.