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Determination of vector error correction models in high dimensions

Liang, Chong; Schienle, Melanie

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.

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Volltext §
DOI: 10.5445/IR/1000092474
Veröffentlicht am 21.03.2019
Zugehörige Institution(en) am KIT Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Forschungsbericht
Jahr 2019
Sprache Englisch
Identifikator ISSN: 2190-9806
KITopen-ID: 1000092474
Verlag KIT, Karlsruhe
Umfang 38 S.
Serie Working paper series in economics ; 124
Schlagworte High-dimensional time series, VECM, Cointegration rank and lag selection, Lasso, Credit Default Swap
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