论文标题
使用非线性尺寸降低技术进行实时通货膨胀预测
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques
论文作者
论文摘要
在本文中，我们评估使用非线性尺寸缩小技术是否有资金为实时预测通货膨胀有所回报。采用了机器学习文献中的几种方法，将大尺寸数据集映射到较低维度的潜在因素集中。我们使用收缩先验的恒定和时变参数（TVP）回归对通货膨胀和潜在因素之间的关系进行建模。然后，我们的模型被用于实时预测美国每月通货膨胀。结果表明，复杂的降低方法产生的通货膨胀预测对基于主成分的线性方法具有很高的竞争力。在考虑的技术中，自动编码器和平方主组件产生的因素具有高预测能力的一个月和四分之一预先通货膨胀的因素。随着时间的推移，放大模型性能表明，在商业周期的经济衰退事件或当前的COVID-19-19大流行期间，控制数据中的非线性关系至关重要。
In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive to linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.
