000	02210cam  2200325za 4500
001	9.835931
003	CaOODSP
005	20221107150759
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008	170428s2017    oncd    ob   f000 0 eng d
040	|aCaOODSP|beng
041	|aeng|bfre
043	|an-cn---
086	1 |aFB3-5/2017-13E-PDF
100	1 |aGuérin, Pierre,|d1984-
245	10|aMarkov‐switching three‐pass regression filter |h[electronic resource] / |cby Pierre Guérin, Danilo Leiva‐Leon and Massimiliano Marcellino.
260	|a[Ottawa] : |bBank of Canada, |c2017.
300	|aii, 38 p. : |bcol. charts
490	1 |aBank of Canada staff working paper, |x1701-9397 ; |v2017-13
500	|a"April 2017."
504	|aIncludes bibliographical references (p. 23-26).
520	3 |a“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.
546	|aIncludes abstract in French.
692	07|2gccst|aEconomic forecasting
692	07|2gccst|aStatistical analysis
700	1 |aMarcellino, Massimiliano.
700	1 |aLeiva-Leon, Danilo.
710	2 |aBank of Canada.
830	#0|aStaff working paper (Bank of Canada)|x1701-9397 ; |v2017-13|w(CaOODSP)9.806221
856	40|qPDF|s3.17 MB|uhttps://publications.gc.ca/collections/collection_2017/banque-bank-canada/FB3-5-2017-13-eng.pdf