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Calculating Joint Confidence Bands for Impulse Response Functions using Highest Density Regions
This paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models.The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A Mont...
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| Pubblicato in: | MAGKS - Joint Discussion Paper Series in Economics (Band 16-2016) |
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| Autori principali: | , , |
| Natura: | Articolo |
| Lingua: | inglese |
| Pubblicazione: |
Philipps-Universität Marburg
2016
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| Soggetti: | |
| Accesso online: | PDF Full Text |
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| Riassunto: | This paper proposes a new non-parametric method of constructing joint confidence bands for impulse response functions of vector autoregressive models.The estimation uncertainty is captured by means of bootstrapping and the highest density region (HDR) approach is used to construct the bands. A
Monte Carlo comparison of the HDR bands with existing alternatives shows
that the former are competitive with the bootstrap-based Bonferroni and
Wald confidence regions. The relative tightness of the HDR bands matched
with their good coverage properties makes them attractive for applications.
An application to corporate bond spreads for Germany highlights the potential
for empirical work. |
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| Descrizione fisica: | 40 Seiten |
| ISSN: | 1867-3678 |
| DOI: | 10.17192/es2024.0508 |