Artikel
Testing identification via heteroskedasticity in structural vector autoregressive models
Tests for identification through heteroskedasticity in structural vector autoregressive analysis are developed for models with two volatility states where the time point of volatility change is known. The tests are Wald-type tests for which only the unrestricted model, including the covariance matrices of the two volatility states, has to be estimated. The residuals of the model are assumed to be from the class of elliptical distributions, which includes Gaussian models. The asymptotic null distributions of the test statistics are derived, and simulations are used to explore their small-sample properties. Two empirical examples illustrate the usefulness of the tests in applied work.
- Sprache
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Englisch
- Erschienen in
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Journal: The Econometrics Journal ; ISSN: 1368-4221 ; Volume: 24 ; Year: 2021 ; Issue: 1 ; Pages: 1-22 ; Oxford: Oxford University Press
- Klassifikation
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Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Ereignis
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Geistige Schöpfung
- (wer)
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Lütkepohl, Helmut
Meitz, Mika
Netšunajev, Aleksei
Saikkonen, Pentti
- Ereignis
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Veröffentlichung
- (wer)
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Oxford University Press
- (wo)
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Oxford
- (wann)
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2021
- DOI
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doi:10.1093/ectj/utaa008
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Lütkepohl, Helmut
- Meitz, Mika
- Netšunajev, Aleksei
- Saikkonen, Pentti
- Oxford University Press
Entstanden
- 2021
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