A state-space approach to time-varying reduced-rank regression

Barbara Brune, Wolfgang Scherrer and Efstathia Bura

Econometric Reviews, 2022, vol. 41, issue 8, 895-917

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.

Date: 2022
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DOI: 10.1080/07474938.2022.2073743

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