Determination of vector error correction models in high dimensions
Determination of vector error correction models in high dimensions
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DOI：
10.1016/j.jeconom.2018.09.018
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发表时间：
2019-02-01
影响因子：
6.3
通讯作者：
Schienle, Melanie
中科院分区：
文献类型：
--
作者：
Liang, Chong;Schienle, Melanie
我们提供了一个收缩型的方法，允许同时进行模型选择和估计的向量误差校正模型（VECM）时，尺寸是大的，可以增加样本量。模型的确定被视为一个联合选择问题的协整秩和自回归滞后下各自的实际有效的稀疏性假设。我们显示的一致性的选择机制所产生的Lasso-VECM估计下非常一般的假设尺寸，秩和误差项。此外，计算复杂性的线性规划问题，该过程仍然是计算上易于处理的高维。我们证明了所提出的方法的有效性，通过模拟研究和实证应用到最近的CDS数据后，金融危机。(C)2018爱思唯尔出版社
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. (C) 2018 Published by Elsevier B.V.