Detecting sparse cointegration
Detecting sparse cointegration
We propose a two-step procedure for detecting sparse cointegration in high-dimensional single-equation 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 residual-based decision rule remains consistent regardless of the internal cointegration structure among the right-hand side predictors themselves. Monte Carlo experiments confirm the procedure’s robust finite-sample performance under endogeneity, serial correlation, and rank deficiency in the regressor matrix.
stat.ME, econ.EM
Gonzalo, Jesus
57637a0a-f7da-417f-9d2e-3a33a7082504
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Gonzalo, Jesus
57637a0a-f7da-417f-9d2e-3a33a7082504
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Gonzalo, Jesus and Pitarakis, Jean-Yves
(2026)
Detecting sparse cointegration.
Oxford Bulletin of Economics and Statistics.
(doi:10.48550/arXiv.2501.13839).
(In Press)
Abstract
We propose a two-step procedure for detecting sparse cointegration in high-dimensional single-equation 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 residual-based decision rule remains consistent regardless of the internal cointegration structure among the right-hand side predictors themselves. Monte Carlo experiments confirm the procedure’s robust finite-sample performance under endogeneity, serial correlation, and rank deficiency in the regressor matrix.
Text
2501.13839v2
- Author's Original
Text
Gonzalo_Pitarakis_OBES_Final_Accepted_Draft_April_2026
- Accepted Manuscript
More information
Submitted date: 4 March 2026
Accepted/In Press date: 8 April 2026
Keywords:
stat.ME, econ.EM
Identifiers
Local EPrints ID: 511259
URI: http://eprints.soton.ac.uk/id/eprint/511259
ISSN: 0305-9049
PURE UUID: b00f3b80-74b1-4326-8858-7ea4b2072bd6
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Date deposited: 11 May 2026 16:32
Last modified: 27 May 2026 01:39
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Author:
Jesus Gonzalo
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