论文标题
使用贝叶斯非参数模型预测美国通货膨胀
Forecasting US Inflation Using Bayesian Nonparametric Models
论文作者
论文摘要
通货膨胀与预测因素（例如失业率）之间的关系可能是非线性的，其强度会随着时间而变化，预测误差误差可能会受到较大的，不对称的冲击。受这些关注的启发，我们开发了一个用于通货膨胀预测的模型，在条件平均值和使用高斯和迪里奇过程的误差中均不参数。我们讨论这两种功能在产生准确的通货膨胀预测中可能很重要。在涉及CPI通货膨胀的预测练习中，我们发现我们的方法在整体和左尾都具有重大的好处，其条件平均值的非参数建模特别重要。
The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial benefits, both overall and in the left tail, with nonparametric modeling of the conditional mean being of particular importance.
