论文标题
使用消息传递算法的高维宏观经济预测
High-dimensional macroeconomic forecasting using message passing algorithms
论文作者
论文摘要
本文提出了对大型信息集和结构不稳定性的计量经济学分析的两个不同贡献。首先，它将带有时变系数，随机波动性和外源预测变量的回归模型视为数千个协变量的等效高维静态回归问题。该规范中的推论是使用贝叶斯分层先验进行的，这些贝叶斯分层先验缩小了系数的高维矢量，既可以零或时间不变。其次，它引入了因子图和消息传递的框架，作为设计有效的贝叶斯估计算法的一种手段。特别是，得出了具有低算法复杂性并且在微不足道的可行的广义近似消息传递（GAMP）算法。结果是一种全面的方法，可用于用任意数量的外源预测因子来估计随时间变化的参数回归。在美国价格通货膨胀的预测练习中，这种方法表明效果很好。
This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either towards zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a Generalized Approximate Message Passing (GAMP) algorithm is derived that has low algorithmic complexity and is trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well.
