摘要翻译：
时变参数(TVP)模型被广泛应用于时间序列分析中，以灵活地处理随时间变化的过程。然而，TVP模型的过拟合风险是众所周知的。这个问题可以通过适当的全局-局部收缩先验来解决，它将时变参数拉向静态参数。本文介绍了R包shrinkTVP（Knaus,Bitto-Nemling,Cadonna,and fr“Uhwirth-Schnatter 2019）,该包充分利用了最近文献的发展，特别是Bitto和fr”Uhwirth-Schnatter(2019)的发展，为TVP模型提供了收缩先验的完全贝叶斯实现。该封装shrinkTVP允许通过有效的马尔可夫链蒙特卡罗(MCMC)格式对参数进行后验模拟。此外，还提供了总结和可视化方法，以及通过测井预测密度评分来评估预测性能的可能性。计算密集型任务已在C++中实现，并与R接口。本文简要概述了该软件包中实现的模型和收缩先验值。此外，通过仿真和实际数据对核心功能进行了说明。
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英文标题：
《Shrinkage in the Time-Varying Parameter Model Framework Using the R
  Package shrinkTVP》
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作者：
Peter Knaus, Angela Bitto-Nemling, Annalisa Cadonna, Sylvia
  Fr\"uhwirth-Schnatter
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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：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
  Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TVP models is well known. This issue can be dealt with using appropriate global-local shrinkage priors, which pull time-varying parameters towards static ones. In this paper, we introduce the R package shrinkTVP (Knaus, Bitto-Nemling, Cadonna, and Fr\"uhwirth-Schnatter 2019), which provides a fully Bayesian implementation of shrinkage priors for TVP models, taking advantage of recent developments in the literature, in particular that of Bitto and Fr\"uhwirth-Schnatter (2019). The package shrinkTVP allows for posterior simulation of the parameters through an efficient Markov Chain Monte Carlo (MCMC) scheme. Moreover, summary and visualization methods, as well as the possibility of assessing predictive performance through log predictive density scores (LPDSs), are provided. The computationally intensive tasks have been implemented in C++ and interfaced with R. The paper includes a brief overview of the models and shrinkage priors implemented in the package. Furthermore, core functionalities are illustrated, both with simulated and real data.
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PDF链接：
https://arxiv.org/pdf/1907.07065