Detecting Sparse Cointegration

Jesus Gonzalo and Jean-Yves Pitarakis

Papers from arXiv.org

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.

Date: 2025-01
New Economics Papers: this item is included in nep-dcm and nep-ets
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http://arxiv.org/pdf/2501.13839 Latest version (application/pdf)

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Working Paper: Detecting sparse cointegration (2025)
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