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

Alain Hecq; Ivan Ricardo; Ines Wilms

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arXiv · 2411.05601

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

Alain Hecq·Ivan Ricardo·Ines Wilms

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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.

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Alain Hecq, Ivan Ricardo, Ines Wilms. 2024-11-08. Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series. https://arxiv.org/abs/2411.05601

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