Heterogeneous Switching in FAVAR Models
Essays in Honour of Fabio Canova
ISBN: 978-1-80382-832-9, eISBN: 978-1-80382-831-2
Publication date: 21 September 2022
Abstract
The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.
Keywords
Acknowledgements
Acknowledgements
We thank Fabio Canova, Marco Del Negro, Domenico Giannone, Yuriy Gorodnichenko, Andrew Levin, James Stock and the participants of several conferences, where this chapter was presented, for helpful comments. A previous version of this chapter was circulated under the title ‘Monetary Policy, Stock Market and Sectoral Comovement’. The views expressed in this chapter are those of the authors. No responsibility for them should be attributed to the IMF, its Executive Board, or IMF management, the Banco de España, or the Eurosystem.
Citation
Guérin, P. and Leiva-León, D. (2022), "Heterogeneous Switching in FAVAR Models", Dolado, J.J., Gambetti, L. and Matthes, C. (Ed.) Essays in Honour of Fabio Canova (Advances in Econometrics, Vol. 44B), Emerald Publishing Limited, Leeds, pp. 65-98. https://doi.org/10.1108/S0731-90532022000044B003
Publisher
:Emerald Publishing Limited
Copyright © 2022 Pierre Guérin and Danilo Leiva-León