Papers › Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series
Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series
Alain Hecq, Ivan Ricardo, Ines Wilms
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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