论文标题
随时间变化的多元自回归指数模型
The Time-Varying Multivariate Autoregressive Index Model
论文作者
论文摘要
许多经济变量的条件均值和波动性具有变化，并且时间变化的矢量自回归模型通常用于处理数据中的复杂性。不幸的是，当系列数量增加时，它们会提出越来越多的估计和解释问题。本文试图解决此问题，提出了一种新的多元自回归索引模型，该模型具有不同的均值和波动性。从技术上讲，我们开发了一种新的估计方法，该方法将切换算法与Koop和Korobilis的遗忘因素策略（2012）混合在一起。这大大减轻了计算负担，并允许使用动态模型选择或动态模型平均无需进一步的计算成本，实时选择或权重，即数据的数量和数据的其他功能。使用美国宏观经济数据，我们提供了结构分析和预测练习，以证明该新模型的可行性和实用性。 关键字：大数据集，多元自动回归索引模型，随机波动率，贝叶斯量。
Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they present increasing estimation and interpretation problems. This paper tries to address this issue proposing a new Multivariate Autoregressive Index model that features time varying means and volatility. Technically, we develop a new estimation methodology that mix switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows to select or weight, in real time, the number of common components and other features of the data using Dynamic Model Selection or Dynamic Model Averaging without further computational cost. Using USA macroeconomic data, we provide a structural analysis and a forecasting exercise that demonstrates the feasibility and usefulness of this new model. Keywords: Large datasets, Multivariate Autoregressive Index models, Stochastic volatility, Bayesian VARs.
