Abstract
This article proposes a new unique impulse response function (IRF) measure, or MIRF, based on the popular vector autoregressive model to study interdependency of multivariate time series. Same as the orthogonal IRF, the estimator of MIRF has an analytical form with well-established asymptotics, and is invariant to ordering of series. Compared to alternative unique IRF measures, MIRF does not depend on extreme identifications, and the associated forecast error variance measure is explainable. An illustrative empirical example is also provided.
Original language | English |
---|---|
Pages (from-to) | 460-468 |
Number of pages | 9 |
Journal | International Review of Finance |
Volume | 23 |
Issue number | 2 |
Early online date | 3 Oct 2022 |
DOIs | |
Publication status | Published - Jun 2023 |
Bibliographical note
© 2022 The Author. International Review of Finance published by John Wiley & Sons Australia, Ltd on behalf of International Review of Finance Ltd. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- eigendecomposition
- forecast error variance decomposition
- impulse response function
- vector autoregressive