Markov-Switching Three-Pass Regression Filter
Pierre Guérin (),
Danilo Leiva-Leon () and
No 591, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University
We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-Switching three-pass regression filter (MS-3PRF), is suitable for datasets with large cross-sectional dimensions since estimation and inference are straightforward, as opposed to existing regime-switching factor models, where computational complexity limits applicability to few variables. In a Monte- Carlo experiment, we study the finite sample properties of the MS-3PRF and find that it performs favorably compared with alternative modelling approaches whenever there is structural instability in factor loadings. As empirical applications, we consider forecasting economic activity and bilateral exchange rates, finding that the MS-3PRF approach is competitive in both cases. Keywords: Factor model, Markov-switching, Forecasting. JEL Classification Code: C22, C23, C53.
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Working Paper: Markov-Switching Three-Pass Regression Filter (2017)
Working Paper: Markov-switching three-pass regression filter (2017)
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