Block Bootstrap Theory for Multivariate Integrated and Cointegrated Processes. (11th September 2014)

• Record Type: Journal Article
• Authors: Jentsch, Carsten; Politis, Dimitris N.; Paparoditis, Efstathios
• Other Names: Cavaliere Giuseppe guestEditor.; Politis Dimitris N. guestEditor.; Rahbek Anders guestEditor.
• Is Part Of: Journal of time series analysis. Volume 36:Number 3(2015:May)
• Page Start: 416
• Page End: 441
• Publication Date: 2014-09-11
• Subjects: Block bootstrap -- bootstrap consistency -- spurious regression -- functional limit theorem -- continuous‐path block bootstrap -- model‐based block bootstrap subclass; Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12088
• Languages: English
• ISSNs: 0143-9782

Abstract

Abstract : We develop some asymptotic theory for applications of block bootstrap resampling schemes to multivariate integrated and cointegrated time series. It is proved that a multivariate, continuous‐path block bootstrap scheme applied to a full rank integrated process succeeds in estimating consistently the distribution of the least squares estimators in both the regression and the spurious regression case. Furthermore, it is shown that the same block resampling scheme does not succeed in estimating the distribution of the parameter estimators in the case of cointegrated time series. For this situation, a modified block resampling scheme, the so‐called residual‐based block bootstrap, is investigated, and its validity for approximating the distribution of the regression parameters is established. The performance of the proposed block bootstrap procedures is illustrated in a short simulation study. Copyright © 2014 Wiley Publishing Ltd