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Detecting Sparse Cointegration
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AI
DOI：10.1111/obes.70085.png)
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Abstract
En 中文
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.
Keywords:
adaptive lasso
cointegration
high dimensional data
unit roots
Journal
O
IF:
1.4
Papers:
80
Citations:
4.7K
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