Detecting Sparse Cointegration

Jesús Gonzalo and Jean-Yves Pitarakis

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

Abstract: We propose a two-step procedure for detecting sparse cointegration in high-dimensional singleequation models. First, we employ the adaptive lasso to identify the subset of integrated covariates driving the long-run equilibrium relationship. Second, we adopt an information-theoretic criterion to distinguish between stationarity and nonstationarity in the resulting residuals, avoiding reliance on asymptotic distributions. A key theoretical contribution is demonstrating that this residualbased decision rule remains consistent regardless of the internal cointegration structure among the right-hand side predictors themselves. Monte Carlo experiments confirm the procedure'srobust finite-sample performance under endogeneity, serial correlation, and rank deficiency in the regressor matrix.

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: 2026-04-21
New Economics Papers: this item is included in nep-ets
References: Add references at CitEc
Citations:

Downloads: (external link)
https://e-archivo.uc3m.es/rest/api/core/bitstreams ... a6024613aa50/content (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:49894

Access Statistics for this paper

More papers in UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía
Bibliographic data for series maintained by Ana Poveda ().