Determination of vector error correction models in high dimensions
Chong Liang and
Journal of Econometrics, 2019, vol. 208, issue 2, 418-441
We provide a shrinkage type methodology which allows for simultaneous model selection and estimation of vector error correction models (VECM) when the dimension is large and can increase with sample size. Model determination is treated as a joint selection problem of cointegrating rank and autoregressive lags under respective practically valid sparsity assumptions. We show consistency of the selection mechanism by the resulting Lasso-VECM estimator under very general assumptions on dimension, rank and error terms. Moreover, with computational complexity of a linear programming problem only, the procedure remains computationally tractable in high dimensions. We demonstrate the effectiveness of the proposed approach by a simulation study and an empirical application to recent CDS data after the financial crisis.
Keywords: High-dimensional time series; VECM; Cointegration rank and lag selection; Lasso; Credit default swap (search for similar items in EconPapers)
JEL-codes: C32 C52 (search for similar items in EconPapers)
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Working Paper: Determination of vector error correction models in high dimensions (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:208:y:2019:i:2:p:418-441
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