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Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series

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  • Alain Hecq
  • Ivan Ricardo
  • Ines Wilms

Abstract

This paper proposes a Matrix Error Correction Model to identify cointegration relations in matrix-valued time series. We hereby allow separate cointegrating relations along the rows and columns of the matrix-valued time series and use information criteria to select the cointegration ranks. Through Monte Carlo simulations and a macroeconomic application, we demonstrate that our approach provides a reliable estimation of the number of cointegrating relationships.

Suggested Citation

  • Alain Hecq & Ivan Ricardo & Ines Wilms, 2024. "Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series," Papers 2411.05601, arXiv.org.
    • Handle: RePEc:arx:papers:2411.05601
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    File URL: http://arxiv.org/pdf/2411.05601
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    References listed on IDEAS

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    1. Gutierrez, Luciano, 2003. "On the power of panel cointegration tests: a Monte Carlo comparison," Economics Letters, Elsevier, vol. 80(1), pages 105-111, July.
    2. Luciano Gutierrez, 2002. "On the power of panel cointegration tests: A Monte Carlo comparison. Economics Letters, 80(1):105-111," Econometrics 0211003, University Library of Munich, Germany, revised 20 May 2003.
    3. Chen, Rong & Xiao, Han & Yang, Dan, 2021. "Autoregressive models for matrix-valued time series," Journal of Econometrics, Elsevier, vol. 222(1), pages 539-560.
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