Detecting Sparse Cointegration
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908865697677312 |
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| author | Gonzalo, Jesus Pitarakis, Jean-Yves |
| author_facet | Gonzalo, Jesus Pitarakis, Jean-Yves |
| contents | We propose a two-step procedure to detect cointegration in high-dimensional settings, focusing on sparse relationships. First, we use the adaptive LASSO to identify the small subset of integrated covariates driving the equilibrium relationship with a target series, ensuring model-selection consistency. Second, we adopt an information-theoretic model choice criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding dependence on asymptotic distributional assumptions. Monte Carlo experiments confirm robust finite-sample performance, even under endogeneity and serial correlation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13839 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Sparse Cointegration Gonzalo, Jesus Pitarakis, Jean-Yves Methodology Econometrics We propose a two-step procedure to detect cointegration in high-dimensional settings, focusing on sparse relationships. First, we use the adaptive LASSO to identify the small subset of integrated covariates driving the equilibrium relationship with a target series, ensuring model-selection consistency. Second, we adopt an information-theoretic model choice criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding dependence on asymptotic distributional assumptions. Monte Carlo experiments confirm robust finite-sample performance, even under endogeneity and serial correlation. |
| title | Detecting Sparse Cointegration |
| topic | Methodology Econometrics |
| url | https://arxiv.org/abs/2501.13839 |