Title: A state-space approach to time-varying reduced rank regression
Authors: Barbara Brune - TU Wien (Austria) [presenting]
Wolfgang Scherrer - Vienna University of Technology (Austria)
Efstathia Bura - Vienna University of Technology (Austria)
Abstract: A new approach is proposed to reduced-rank regression that allows for time-variation in the regression coefficients. The Kalman filter-based estimation allows for the 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 an 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.