Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909464996610048 |
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| author | Hecq, Alain Ricardo, Ivan Wilms, Ines |
| author_facet | Hecq, Alain Ricardo, Ivan Wilms, Ines |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05601 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series Hecq, Alain Ricardo, Ivan Wilms, Ines Econometrics Methodology 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. |
| title | Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2411.05601 |