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Markov-switching three-pass regression filter

Author

Listed:
  • Pierre Guérin

    (OECD)

  • Danilo Leiva-Leon

    (Banco de España)

  • Massimiliano Marcellino

    (Bocconi University, IGIER and CEPR)

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.

Suggested Citation

  • Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    • Handle: RePEc:bde:wpaper:1748
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    More about this item

    Keywords

    factor model; Markov-switching; forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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