摘要翻译：
在过去的十年里，大数据涌入计量经济学，要求新的统计方法来分析高维数据和复杂的非线性关系。解决维度问题的一种常见方法依赖于使用静态图形结构来提取感兴趣变量之间最重要的依赖关系。近年来，贝叶斯非参数方法以灵活有效的方式对复杂现象进行建模已成为热门，但在计量经济学方面的尝试却很少。本文提出了一种新的贝叶斯非参数(BNP)时变图形框架，用于对高维时间序列进行推理。我们在系数矩阵和协方差矩阵上引入了贝叶斯非参数相关先验规范，采用时间序列DPP的方法，如Nieto-Barajas等人的方法。（2012年）。继Billio等人之后。(2019)，我们的分层先验通过将向量自回归(VAR)系数聚类到组中，并通过向公共位置收缩每个组的系数来克服过参数化和过拟合问题。我们的BNP时变VAR模型是基于尖峰和板状结构，结合相关Dirichlet过程先验(DPP)，允许：(i)从时间序列中推断出时变Granger因果网络；(ii)对非零时变系数进行灵活建模和聚类；(iii)考虑潜在的非线性。为了评估模型的性能，我们通过考虑一个著名的宏观经济数据集来研究我们的方法的优点。此外，我们通过比较Dirac和扩散尖峰先验分布这两个替代规范来检验该方法的鲁棒性。
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英文标题：
《Bayesian nonparametric graphical models for time-varying parameters VAR》
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作者：
Matteo Iacopini and Luca Rossini
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最新提交年份：
2019
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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：
  Over the last decade, big data have poured into econometrics, demanding new statistical methods for analysing high-dimensional data and complex non-linear relationships. A common approach for addressing dimensionality issues relies on the use of static graphical structures for extracting the most significant dependence interrelationships between the variables of interest. Recently, Bayesian nonparametric techniques have become popular for modelling complex phenomena in a flexible and efficient manner, but only few attempts have been made in econometrics. In this paper, we provide an innovative Bayesian nonparametric (BNP) time-varying graphical framework for making inference in high-dimensional time series. We include a Bayesian nonparametric dependent prior specification on the matrix of coefficients and the covariance matrix by mean of a Time-Series DPP as in Nieto-Barajas et al. (2012). Following Billio et al. (2019), our hierarchical prior overcomes over-parametrization and over-fitting issues by clustering the vector autoregressive (VAR) coefficients into groups and by shrinking the coefficients of each group toward a common location. Our BNP timevarying VAR model is based on a spike-and-slab construction coupled with dependent Dirichlet Process prior (DPP) and allows to: (i) infer time-varying Granger causality networks from time series; (ii) flexibly model and cluster non-zero time-varying coefficients; (iii) accommodate for potential non-linearities. In order to assess the performance of the model, we study the merits of our approach by considering a well-known macroeconomic dataset. Moreover, we check the robustness of the method by comparing two alternative specifications, with Dirac and diffuse spike prior distributions.
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PDF链接：
https://arxiv.org/pdf/1906.02140