Detecting cointegrating relations in non-stationary matrix-valued time series
提出矩阵误差修正模型，识别矩阵值时间序列中沿行和列的协整关系，并用信息准则选择秩，通过模拟和宏观应用验证方法可靠性。
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. • Propose a matrix error correction model for matrix-valued time series. • Distinguish cointegration relations along the dimensions of the matrix-valued time series. • Utilize information criteria to determine row and column cointegration ranks. • Validate rank selection with Monte Carlo simulations and macroeconomic application.