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arXiv · 2501.13839

Detecting Sparse Cointegration

Jesus Gonzalo·Jean-Yves Pitarakis
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Abstract

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.

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Jesus Gonzalo, Jean-Yves Pitarakis. 2025-01-23. Detecting Sparse Cointegration. https://arxiv.org/abs/2501.13839

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