英文标题：
《Real-time Inflation Forecasting Using Non-linear Dimension Reduction
Techniques》
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作者：
Niko Hauzenberger, Florian Huber, Karin Klieber
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最新提交年份：
2021
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英文摘要：
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.
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中文摘要：
在本文中，我们评估使用非线性降维技术对实时预测通货膨胀是否有好处。机器学习文献中的几种最新方法被用来将大维数据集映射到低维的潜在因素集。我们使用带有收缩先验的常数和时变参数（TVP）回归来模拟通货膨胀与潜在因素之间的关系。然后，我们的模型被用于实时预测美国月度通胀。结果表明，复杂的降维方法产生的通胀预测与基于主成分的线性方法相比具有很强的竞争力。在所考虑的技术中，自动编码器和平方主成分产生的因子对提前一个月和一个季度的通货膨胀具有很高的预测能力。随着时间推移，2019冠状病毒疾病的非线性表现在数据周期中的非线性控制中尤为重要，而在当前的COVID-19流行病中，这种控制是非常重要的。
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分类信息：

一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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