Detecting sparse cointegration
Detecting sparse cointegration
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.
stat.ME, econ.EM
Gonzalo, Jesus
57637a0a-f7da-417f-9d2e-3a33a7082504
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
4 March 2026
Gonzalo, Jesus
57637a0a-f7da-417f-9d2e-3a33a7082504
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
[Unknown type: UNSPECIFIED]
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.
Text
2501.13839v2
- Author's Original
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Published date: 4 March 2026
Keywords:
stat.ME, econ.EM
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Local EPrints ID: 511259
URI: http://eprints.soton.ac.uk/id/eprint/511259
PURE UUID: b00f3b80-74b1-4326-8858-7ea4b2072bd6
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Date deposited: 11 May 2026 16:32
Last modified: 12 May 2026 01:40
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Author:
Jesus Gonzalo
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