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

Author

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  • Barbara Brune
  • Wolfgang Scherrer
  • Efstathia Bura

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.

Suggested Citation

  • Barbara Brune & Wolfgang Scherrer & Efstathia Bura, 2022. "A state-space approach to time-varying reduced-rank regression," Econometric Reviews, Taylor & Francis Journals, vol. 41(8), pages 895-917, September.
    • Handle: RePEc:taf:emetrv:v:41:y:2022:i:8:p:895-917
    DOI: 10.1080/07474938.2022.2073743
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    Cited by:

    1. S. Yaser Samadi & Wiranthe B. Herath, 2023. "Reduced-rank Envelope Vector Autoregressive Models," Papers 2309.12902, arXiv.org.

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