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

Alain Hecq; Ivan Ricardo; Ines Wilms

doi:10.1016/j.econlet.2025.112205

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

Alain Hecq
, Ivan Ricardo^*
, Ines Wilms

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	112205
Number of pages	5
Journal	Economics Letters
Volume	248
DOIs	https://doi.org/10.1016/j.econlet.2025.112205
Publication status	Published - 1 Mar 2025

Keywords

Cointegration rank
Error correction model
Information criteria
Matrix-valued time series

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10.1016/j.econlet.2025.112205Licence: CC BY

Cite this

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title = "Detecting cointegrating relations in non-stationary matrix-valued time series",

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.",

keywords = "Cointegration rank, Error correction model, Information criteria, Matrix-valued time series",

author = "Alain Hecq and Ivan Ricardo and Ines Wilms",

note = "data source: . The data is obtained from the Organization for Economic Co-operation and Development (OECD) at https://data-explorer.oecd.org/ ",

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language = "English",

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AU - Hecq, Alain

AU - Ricardo, Ivan

AU - Wilms, Ines

N1 - data source: . The data is obtained from the Organization for Economic Co-operation and Development (OECD) at https://data-explorer.oecd.org/

PY - 2025/3/1

Y1 - 2025/3/1

N2 - 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.

AB - 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.

KW - Cointegration rank

KW - Error correction model

KW - Information criteria

KW - Matrix-valued time series

U2 - 10.1016/j.econlet.2025.112205

DO - 10.1016/j.econlet.2025.112205

M3 - Article

SN - 0165-1765

VL - 248

JO - Economics Letters

JF - Economics Letters

M1 - 112205

ER -

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

Abstract

Keywords

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