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

TL;DR

This paper introduces a Matrix Error Correction Model that detects cointegration relations in matrix-valued time series, allowing for separate relations along rows and columns, with demonstrated reliability through simulations and macroeconomic data.

Contribution

It presents a novel approach for identifying cointegration in matrix-valued time series, including separate relations along rows and columns, and uses information criteria for rank selection.

Findings

Reliable estimation of cointegration ranks demonstrated

Effective in macroeconomic applications

Outperforms existing methods in simulations

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.