摘要翻译：
本文提出了单方程惩罚误差校正选择器(SPECS)作为具有大量潜在集成变量的动态单方程模型的自动估计过程。通过对经典的单方程误差修正模型的扩展，SPECS使研究人员能够对大型协整数据集建模，而不需要任何形式的对积分顺序或协整秩的预测试。在参数个数和时间序列观测值共同发散到无穷大的渐近情况下，我们证明了SPEPS能够一致地估计潜在DGP中可能出现的协整向量的适当线性组合。此外，SPECS还可以正确恢复参数空间中的稀疏模式，并具有与OLS oracle过程相同的极限分布。仿真研究表明，与忽略协整的高维模型相比，在临近预报环境下具有较强的选择能力和较好的预测性能。使用谷歌趋势对荷兰失业率进行临近预报的实证应用证实了我们的程序的强大的实际性能。
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英文标题：
《An Automated Approach Towards Sparse Single-Equation Cointegration
  Modelling》
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作者：
Stephan Smeekes and Etienne Wijler
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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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PDF链接：
https://arxiv.org/pdf/1809.08889