Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/197938 
Year of Publication: 
2017
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2017-13
Publisher: 
Bank of Canada, Ottawa
Abstract: 
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 favourably compared with alternative modelling approaches whenever there is structural instability in factor loadings. For empirical applications, we consider forecasting economic activity and bilateral exchange rates, finding that the MS-3PRF approach is competitive in both cases.
Subjects: 
Econometric and statistical methods
JEL: 
C22
C23
C53
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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