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Markov-switching three-pass regression filter

Pierre Guérin (), Danilo Leiva-Leon () and Massimiliano Marcellino

No 1748, Working Papers from Banco de España, Working Papers Homepage

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 fi lter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass regression fi lter (MS-3PRF), is suitable for data sets 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 fi nd 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.

Keywords: factor model; Markov-switching; forecasting (search for similar items in EconPapers)
JEL-codes: C22 C23 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-ore
Date: 2017-12
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Related works:
Working Paper: Markov-Switching Three-Pass Regression Filter (2017) Downloads
Working Paper: Markov-Switching Three-Pass Regression Filter (2016) Downloads
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