Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/129574 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
Working Paper Series No. 14-21
Verlag: 
University of Mannheim, Department of Economics, Mannheim
Zusammenfassung: 
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.
Schlagwörter: 
VAR
Conditional heteroskedasticity
Residual-based moving block bootstrap
Pairwise bootstrap
Wild bootstrap
JEL: 
C30
C32
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
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