论文标题
使用可扩展的马尔可夫链蒙特卡洛方法的大型时变参数回归的动态收缩率
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
论文作者
论文摘要
时变参数（TVP）回归模型可能涉及大量系数。需要仔细的事先启发才能产生明智的后部和预测性推断。此外，马尔可夫链蒙特卡洛（MCMC）方法的计算需求意味着它们的使用仅限于预测变量数量不大的情况。鉴于这两个问题，本文提出了一种新的动态收缩，这反映了TVP通常是稀疏的经验规律性（即时间变化可能仅在偶发性上发生，并且仅出于某些系数）。开发了一种可扩展的MCMC算法，该算法能够处理非常高维的TVP回归或TVP矢量自动化。在使用人工数据的练习中，我们证明了我们方法的准确性和计算效率。在涉及欧元区利率术语结构的应用中，我们在有效挑选少量参数更改之前发现了动态收缩，并且我们的方法可以很好地预测。
Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.
