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

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 likelih...

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Veröffentlicht in:	Econometric reviews 2022-09, Vol.41 (8), p.895-917
Hauptverfasser:	Brune, Barbara, Scherrer, Wolfgang, Bura, Efstathia
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Coefficient of variation Competitive advantage Convergence COVID-19 Econometrics EM-algorithm Kalman filter Kalman filters Regression coefficients Simulation Time series time-varying parameters vector error correction model
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