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Inference in VARs with Conditional Heteroskedasticity of Unknown Form
Brüggemann, Ralf
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Jentsch, Carsten
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Trenkler, Carsten
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Brüggemann,_Jentsch,_Trenkler_14-21.pdf
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URL:
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https://ub-madoc.bib.uni-mannheim.de/36858
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URN:
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urn:nbn:de:bsz:180-madoc-368583
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Dokumenttyp:
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Arbeitspapier
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Erscheinungsjahr:
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2014
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Titel einer Zeitschrift oder einer Reihe:
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Working Paper Series
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Band/Volume:
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14-21
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Ort der Veröffentlichung:
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Mannheim
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Rechtswissenschaft und Volkswirtschaftslehre > Zeitreihenökonometrie (Trenkler 2007-)
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MADOC-Schriftenreihe:
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Department of Economics > Working Paper Series
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Fachgebiet:
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330 Wirtschaft
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Fachklassifikation:
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JEL:
C30 , C32,
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Freie Schlagwörter (Englisch):
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VAR , Conditional heteroskedasticity , Residual-based moving block bootstrap , Pairwise bootstrap , Wild bootstrap
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Abstract:
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We derive a framework for asymptotically valid inference in stable vector autoregressive
(VAR) models with conditional heteroskedasticity of unknown form. We prove a joint central limit theorem for the VAR slope parameter and innovation covariance parameter estimators and address bootstrap inference as well. Our results are important for correct inference on VAR statistics that depend both on the VAR slope and the variance parameters as e.g. in structural impulse response functions (IRFs). We also show that wild and pairwise bootstrap schemes fail in the presence of conditional heteroskedasticity if inference on (functions) of the unconditional variance parameters is of interest because they do not correctly replicate the relevant fourth moments' structure of the error terms. In contrast, the residual-based moving block bootstrap results in asymptotically valid inference. We illustrate the practical implications of our theoretical results by providing simulation evidence on the finite
sample properties of different inference methods for IRFs. Our results point out that estimation uncertainty may increase dramatically in the presence of conditional heteroskedasticity.
Moreover, most inference methods are likely to understate the true estimation uncertainty
substantially in finite samples.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 Suche Autoren in
BASE:
Brüggemann, Ralf
;
Jentsch, Carsten
;
Trenkler, Carsten
Google Scholar:
Brüggemann, Ralf
;
Jentsch, Carsten
;
Trenkler, Carsten
ORCID:
Brüggemann, Ralf, Jentsch, Carsten and Trenkler, Carsten  ORCID: https://orcid.org/0000-0003-1846-1764
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