Brune, B., Scherrer, W., & Bura, E. (2022). A state-space approach to time-varying reduced-rank regression. Econometric Reviews, 41(8), 895–917. https://doi.org/10.1080/07474938.2022.2073743
EM-algorithm; Kalman filter; time-varying parameters; vector error correction model
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Abstract:
We propose a new approach to reduced-rank regression that allows for time-variation in the regression coefficients. The Kalman filter based estimation allows for usage of standard methods and easy implementation of our procedure. The EM-algorithm ensures convergence to a local maximum of the likelihood. Our estimation approach in time-varying reduced-rank regression performs well in simulations, with amplified competitive advantage in time series that experience large structural changes. We illustrate the performance of our approach with a simulation study and two applications to stock index and Covid-19 case data.
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Project title:
Prognostizierung einer suffizienten Dimensions-Reduktions-Methodik: P 30690-N35 (Fonds zur Förderung der wissenschaftlichen Forschung (FWF)) Distribution Recovery for Invariant Generation of Probabilistic Programs: ICT19-018 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds)
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Research Areas:
Modeling and Simulation: 35% Fundamental Mathematics Research: 65%
