论文标题
使用集成旋转高斯近似值的高维时变参数模型中的贝叶斯推断
Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations
论文作者
论文摘要
研究人员越来越希望估计涉及大量解释变量的时变参数（TVP）回归。包括以前的信息来减轻过度参数化问题，导致许多人使用贝叶斯方法。但是，贝叶斯马尔可夫链蒙特卡洛（MCMC）方法在计算上可能非常苛刻。在本文中，我们开发了使用集成旋转高斯近似（IRGA）估算TVP模型的计算高效贝叶斯方法。这利用了这样一个事实，尽管回归器上的恒定系数通常很重要，但大多数TVP通常并不重要。由于高斯分布是旋转不变的，因此我们可以将后部分为两个部分：一个涉及恒定系数，另一个涉及TVP。在后者上使用了近似方法，在这些方法上，使用MCMC方法估算了前者的条件。在涉及人造数据和大型宏观经济数据集的经验练习中，我们显示了IRGA方法的准确性和计算益处。
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.
