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Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques

Niko Hauzenberger (), Florian Huber and Karin Klieber

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Abstract: 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.

Date: 2020-12, Revised 2021-12
New Economics Papers: this item is included in nep-big, nep-cba, nep-ets and nep-for
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Journal Article: Real-time inflation forecasting using non-linear dimension reduction techniques (2023) Downloads
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Handle: RePEc:arx:papers:2012.08155