Determination of vector error correction models in high dimensions
| Autor: | Chong Liang, Melanie Schienle |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Economics and Econometrics
Computational complexity theory Rank (linear algebra) Applied Mathematics Model selection 05 social sciences Estimator 01 natural sciences 010104 statistics & probability Lasso (statistics) Autoregressive model Dimension (vector space) 0502 economics and business 0101 mathematics Error detection and correction Algorithm 050205 econometrics Mathematics |
| Zdroj: | Journal of Econometrics. 208:418-441 |
| ISSN: | 0304-4076 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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