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Detecting cointegrating relations in non-stationary matrix-valued time series

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  • Hecq, Alain
  • Ricardo, Ivan
  • Wilms, Ines

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

  • Hecq, Alain & Ricardo, Ivan & Wilms, Ines, 2025. "Detecting cointegrating relations in non-stationary matrix-valued time series," Economics Letters, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:ecolet:v:248:y:2025:i:c:s0165176525000424
    DOI: 10.1016/j.econlet.2025.112205
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    1. Liao, Zhipeng & Phillips, Peter C. B., 2015. "Automated Estimation Of Vector Error Correction Models," Econometric Theory, Cambridge University Press, vol. 31(3), pages 581-646, June.
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    5. Xu Cheng & P eter C. B. Phillips, 2009. "Semiparametric cointegrating rank selection," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 83-104, January.
    6. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
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    9. Wang, Di & Zheng, Yao & Li, Guodong, 2024. "High-dimensional low-rank tensor autoregressive time series modeling," Journal of Econometrics, Elsevier, vol. 238(1).
    10. Gutierrez, Luciano, 2003. "On the power of panel cointegration tests: a Monte Carlo comparison," Economics Letters, Elsevier, vol. 80(1), pages 105-111, July.
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