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Title: Markov-switching three-pass regression filter Authors:  Pierre Guerin - Bank of Canada (Canada) [presenting]
Massimiliano Marcellino - Bocconi University (Italy)
Danilo Leiva-Leon - Banco de Espa\~na (Spain)
Abstract: A new approach is introduced 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 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 a panel of exchange rates, finding that the MS-3PRF approach is competitive in both cases.