Robust Regression on Stationary Time Series: A Self†Normalized Resampling Approach

Fumiya Akashi, Shuyang Bai and Murad S. Taqqu

Journal of Time Series Analysis, 2018, vol. 39, issue 3, 417-432

Abstract: This article extends the self†normalized subsampling method of Bai et al. (2016) to the M†estimation of linear regression models, where the covariate and the noise are stationary time series which may have long†range dependence or heavy tails. The method yields an asymptotic confidence region for the unknown coefficients of the linear regression. The determination of these regions does not involve unknown parameters such as the intensity of the dependence or the heaviness of the distributional tail of the time series. Additional simulations can be found in a supplement. The computer codes are available from the authors.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/jtsa.12295

Related works:
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:bla:jtsera:v:39:y:2018:i:3:p:417-432

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().