Estimation spectrale avec mémoire longue et hétéroscédasticité conditionnelle

Translated title of the contribution: Spectral estimatioin with long memory conditional heteroscedasticity

Research output: Contribution to journalArticle

Abstract

Semiparametric spectral methods are valuable in that they yield inference on time series under very broad assumptions. This contribution analyzes the averaged periodogram statistic in the framework of a generalized linear process with (possibly long memory) conditional heteroscedasticity in the innovations. It is shown that the averaged periodogram statistic is appropriate for asymptotically normal estimation of the spectrum of a weakly dependent process at frequency zero and for consistent estimation of long memory and stationary cointegration in strongly dependent processes.

Original languageFrench
Pages (from-to)811-814
Number of pages4
JournalComptes Rendus de l'Academie des Sciences - Series I: Mathematics
Volume329
Issue number9
DOIs
StatePublished - Jan 1 1999

Fingerprint

Conditional Heteroscedasticity
Periodogram
Long Memory
Statistic
Semiparametric Methods
Consistent Estimation
Linear Process
Cointegration
Dependent
Spectral Methods
Time series
Zero
Framework
Innovation

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

Cite this

@article{c42851d2a21449278dbe27f850805b8f,
title = "Estimation spectrale avec m{\'e}moire longue et h{\'e}t{\'e}rosc{\'e}dasticit{\'e} conditionnelle",
abstract = "Semiparametric spectral methods are valuable in that they yield inference on time series under very broad assumptions. This contribution analyzes the averaged periodogram statistic in the framework of a generalized linear process with (possibly long memory) conditional heteroscedasticity in the innovations. It is shown that the averaged periodogram statistic is appropriate for asymptotically normal estimation of the spectrum of a weakly dependent process at frequency zero and for consistent estimation of long memory and stationary cointegration in strongly dependent processes.",
author = "Henry, {Marc Albert}",
year = "1999",
month = "1",
day = "1",
doi = "10.1016/S0764-4442(99)90013-7",
language = "French",
volume = "329",
pages = "811--814",
journal = "Comptes Rendus Mathematique",
issn = "1631-073X",
publisher = "Elsevier Masson",
number = "9",

}

TY - JOUR

T1 - Estimation spectrale avec mémoire longue et hétéroscédasticité conditionnelle

AU - Henry, Marc Albert

PY - 1999/1/1

Y1 - 1999/1/1

N2 - Semiparametric spectral methods are valuable in that they yield inference on time series under very broad assumptions. This contribution analyzes the averaged periodogram statistic in the framework of a generalized linear process with (possibly long memory) conditional heteroscedasticity in the innovations. It is shown that the averaged periodogram statistic is appropriate for asymptotically normal estimation of the spectrum of a weakly dependent process at frequency zero and for consistent estimation of long memory and stationary cointegration in strongly dependent processes.

AB - Semiparametric spectral methods are valuable in that they yield inference on time series under very broad assumptions. This contribution analyzes the averaged periodogram statistic in the framework of a generalized linear process with (possibly long memory) conditional heteroscedasticity in the innovations. It is shown that the averaged periodogram statistic is appropriate for asymptotically normal estimation of the spectrum of a weakly dependent process at frequency zero and for consistent estimation of long memory and stationary cointegration in strongly dependent processes.

UR - http://www.scopus.com/inward/record.url?scp=17144448570&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=17144448570&partnerID=8YFLogxK

U2 - 10.1016/S0764-4442(99)90013-7

DO - 10.1016/S0764-4442(99)90013-7

M3 - Article

AN - SCOPUS:17144448570

VL - 329

SP - 811

EP - 814

JO - Comptes Rendus Mathematique

JF - Comptes Rendus Mathematique

SN - 1631-073X

IS - 9

ER -