Detecting sparse cointegration

Jean-Yves Pitarakis
Authors registered in the RePEc Author Service: Jesus Gonzalo

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

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.

Keywords: Cointegration; High; Dimensional; Data; Adaptive; Lasso; Unit; Roots (search for similar items in EconPapers)
JEL-codes: C32 C52 (search for similar items in EconPapers)
Date: 2025-01-27
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ets
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Working Paper: Detecting Sparse Cointegration (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:45708

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