Estimation spectrale avec mémoire longue et hétéroscédasticité conditionnelle

Marc Henry

doi:10.1016/S0764-4442(99)90013-7

Estimation spectrale avec mémoire longue et hétéroscédasticité conditionnelle

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

Marc Henry

Economics

Research output: Contribution to journal › Article › peer-review

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.

Translated title of the contribution	Spectral estimatioin with long memory conditional heteroscedasticity
Original language	French
Pages (from-to)	811-814
Number of pages	4
Journal	Comptes Rendus de l'Academie des Sciences - Series I: Mathematics
Volume	329
Issue number	9
DOIs	https://doi.org/10.1016/S0764-4442(99)90013-7
State	Published - 1999

All Science Journal Classification (ASJC) codes

General Mathematics

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10.1016/S0764-4442(99)90013-7

Cite this

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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.",

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year = "1999",

doi = "10.1016/S0764-4442(99)90013-7",

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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.

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

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VL - 329

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JO - Comptes Rendus de l'Academie des Sciences - Series I: Mathematics

JF - Comptes Rendus de l'Academie des Sciences - Series I: Mathematics

IS - 9

ER -

Estimation spectrale avec mémoire longue et hétéroscédasticité conditionnelle

Abstract

All Science Journal Classification (ASJC) codes

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