Penalized Time Series Regression

Publikation: Bidrag til bog/antologi/rapport/proceedingBidrag til bog/antologiForskningpeer review

DOI

  • Anders Bredahl Kock
  • Marcelo Medeiros, Pontifical Catholic University of Rio de Janeiro
  • ,
  • Gabriel Vasconcelos, Pontifical Catholic University of Rio de Janeiro

This chapter covers penalized regression in the framework of linear time series models and reviews the most commonly used penalized estimators in applied work, namely Ridge Regression, the Least Absolute Shrinkage and Selection Operator (Lasso), the Elastic Net, the adaptive versions of the Lasso as well as Elastic Net and the group Lasso. Other penalties are briefly presented. We discuss theoretical properties such as consistent variable selection, the oracle property, and oracle inequalities and list time series models in which penalized estimators have been shown to possess these. Potentially problematic aspects of (some of) these properties are also discussed. Practical issues, such as the selection of the penalty parameters and available computer implementations, are also covered. A Monte Carlo simulation is presented in order to compare different penalties in terms of estimation precision, model selection capability, and forecasting performance. Finally, an application to forecasting US monthly inflation is presented.

OriginalsprogEngelsk
TitelMacroeconomic Forecasting in the Era of Big Data
RedaktørerP. Fuleky
Antal sider36
UdgivelsesstedCham
ForlagSpringer
Udgivelsesår2020
Sider193-228
ISBN (trykt)978-3-030-31149-0
ISBN (Elektronisk)978-3-030-31150-6
DOI
StatusUdgivet - 2020
SerietitelAdvanced Studies in Theoretical and Applied Econometrics
Vol/bind52
ISSN1570-5811

Se relationer på Aarhus Universitet Citationsformater

ID: 187170273