Markov‐switching three‐pass regression filter: FB3-5/2017-13E-PDF

“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 isstructural 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"--Abstract, p. ii.

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Department/Agency Bank of Canada.
Title Markov‐switching three‐pass regression filter
Series Title Bank of Canada staff working paper,
Publication Type Series - View Master Record
Language [English]
Format Electronic
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Note "April 2017."
Date 2017.
Number of Pages ii, 38 p. :
Catalogue Number
  • FB3-5/2017-13E-PDF
Subject Terms Economic forecasting, Statistical analysis