时变多元自回归指数模型
The Time-Varying Multivariate Autoregressive Index Model
摘要：
许多经济变量的条件均值和波动率都发生变化，时变向量自回归模型通常用于处理数据中的此类复杂性。不幸的是，当系列数量增加时，它们提出了越来越多的估计和解释问题。本文试图解决这个问题，提出了一个新的多元自回归指数模型，具有时变的手段和波动性。从技术上讲，我们开发了一种新的估计方法，将切换算法与Koop和Korobilis（2012）的遗忘因子策略相结合。这大大减少了计算负担，并允许使用动态模型选择或动态模型平均来实时选择或加权数据的公共分量和其他特征的数量，而没有进一步的计算成本。使用美国宏观经济数据，我们提供了一个结构分析和预测练习，证明了这个新模型的可行性和实用性。关键词：大型数据集，多元自回归指数模型，随机波动率，贝叶斯VAR。
英文摘要：
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.
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