Estimating High-Dimensional Time Series Models

Marcelo Medeiros () and Eduardo F.Mendes ()
Additional contact information
Eduardo F.Mendes: NORTHWESTERN UNIVERSITY

No 602, Textos para discussão from Department of Economics PUC-Rio (Brazil)

Abstract: We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse,high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows the method performs well in very general settings. Finally, we consider two applications: in the first one the goal is to forecast quarterlyUS inflation one-step ahead, and in the second we are interested in the excess return of the S&P500 index. The method used outperforms the usual benchmarks in the literature.

Pages: 33p
Date: 2012-08
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.econ.puc-rio.br/uploads/adm/trabalhos/files/td602.pdf (application/pdf)

Related works:
Working Paper: Estimating High-Dimensional Time Series Models (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rio:texdis:602

Access Statistics for this paper

More papers in Textos para discussão from Department of Economics PUC-Rio (Brazil) Contact information at EDIRC.
Bibliographic data for series maintained by ().