Orthogonal Impulse Response Analysis in Presence of Time-Varying Covariance

Valentin Patilea () and Hamdi Raïssi ()
Additional contact information
Valentin Patilea: Campus de Ker-Lann, CREST Ensai
Hamdi Raïssi: Pontificia Universidad Católica de Valparaíso

Chapter Chapter 18 in Research Papers in Statistical Inference for Time Series and Related Models, 2023, pp 419-443 from Springer

Abstract: Abstract In this paper, the orthogonal impulse response functions (OIRFs) are studied in the non-standard but quite common case where the covariance of the error vector is not constant in time. The usual approach for taking such covariance behavior into account consists in applying the standard tools to sub-periods of the whole sample. We underline that such a practice may lead to severe upward bias. We propose a new approach intended to give what we argue to be a more accurate summary of the time-varying OIRFs. This consists in averaging the Cholesky decomposition of nonparametric covariance estimators. In addition, an index is developed to evaluate the heteroscedasticity effect on the OIRFs analysis. The asymptotic behavior of the proposed estimators is investigated.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-981-99-0803-5_18

Ordering information: This item can be ordered from
http://www.springer.com/9789819908035

DOI: 10.1007/978-981-99-0803-5_18

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().