Heterogeneous Switching in FAVAR Models

Pierre Guérin and Danilo Leiva-Leon ()

A chapter in Essays in Honour of Fabio Canova, 2022, vol. 44B, pp 65-98 from Emerald Group Publishing Limited

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: Markov switching; factor analysis; vector autoregressions; Bayesian methods; credit; business cycle; C32; C38; C55; E44; E50 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532022000044b003

DOI: 10.1108/S0731-90532022000044B003

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