Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/191548
Authors: 
Liang, Chong
Schienle, Melanie
Year of Publication: 
2019
Series/Report no.: 
KIT Working Paper Series in Economics 124
Abstract: 
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.
Subjects: 
High-dimensional time series
VECM
Cointegration rank and lag selection
Lasso
Credit Default Swap
JEL: 
C32
C52
Document Type: 
Working Paper
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