摘要翻译：
研究人员越来越希望估计时变参数(TVP)回归，因为它涉及大量的解释变量。包括先验信息以减轻过度参数化的担忧导致了许多使用贝叶斯方法。然而，贝叶斯马尔可夫链蒙特卡罗(MCMC)方法计算量很大。在本文中，我们发展了一个计算效率高的贝叶斯方法估计TVP模型使用积分旋转高斯近似(IRGA)。这利用了这样一个事实，即虽然回归子上的常数系数通常很重要，但大多数TVP通常不重要。由于高斯分布对旋转不变，我们可以将后验分为两部分：一部分涉及常系数，另一部分涉及TVPS。对后者采用近似方法，在此基础上，利用MCMC方法对前者进行精确估计。在涉及人工数据和大型宏观经济数据集的经验练习中，我们展示了IRGA方法的准确性和计算效益。
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英文标题：
《Bayesian Inference in High-Dimensional Time-varying Parameter Models
  using Integrated Rotated Gaussian Approximations》
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作者：
Florian Huber, Gary Koop, Michael Pfarrhofer
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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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英文摘要：
  Researchers increasingly wish to estimate time-varying parameter (TVP) regressions which involve a large number of explanatory variables. Including prior information to mitigate over-parameterization concerns has led to many using Bayesian methods. However, Bayesian Markov Chain Monte Carlo (MCMC) methods can be very computationally demanding. In this paper, we develop computationally efficient Bayesian methods for estimating TVP models using an integrated rotated Gaussian approximation (IRGA). This exploits the fact that whereas constant coefficients on regressors are often important, most of the TVPs are often unimportant. Since Gaussian distributions are invariant to rotations we can split the the posterior into two parts: one involving the constant coefficients, the other involving the TVPs. Approximate methods are used on the latter and, conditional on these, the former are estimated with precision using MCMC methods. In empirical exercises involving artificial data and a large macroeconomic data set, we show the accuracy and computational benefits of IRGA methods.
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PDF链接：
https://arxiv.org/pdf/2002.10274