稀疏单方程协整模型的自动化方法
An Automated Approach Towards Sparse Single-Equation Cointegration Modelling
摘要：
在本文中，我们提出了单方程惩罚误差校正（SPECS）作为一个自动估计过程的动态单方程模型与大量的潜在（共）集成变量。通过扩展经典的单方程误差校正模型，SPECS使研究人员能够对大型协整数据集进行建模，而无需对整合顺序或协整秩进行任何形式的预测试。在一个渐近制度中，参数和时间序列观测的数量共同发散到无穷大，我们表明，SPECS是能够一致地估计一个适当的线性组合的协整向量，可能会发生在潜在的DGP。此外，SPECS示出，使正确的恢复稀疏模式的参数空间和相同的限制分布的OLS Oracle程序。模拟研究表明，强大的选择能力，以及优越的预测性能的背景下，临近预报相比，高维模型，忽略协整。经验应用nowcasting荷兰失业率使用谷歌趋势证实了我们的程序的强大的实际性能。
英文摘要：
In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. Under an asymptotic regime in which both the number of parameters and time series observations jointly diverge to infinity, we show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP. In addition, SPECS is shown to enable the correct recovery of sparsity patterns in the parameter space and to posses the same limiting distribution as the OLS oracle procedure. A simulation study shows strong selective capabilities, as well as superior predictive performance in the context of nowcasting compared to high-dimensional models that ignore cointegration. An empirical application to nowcasting Dutch unemployment rates using Google Trends confirms the strong practical performance of our procedure.
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