Real-time inflation forecasting using non-linear dimension reduction techniques

Hauzenberger, Niko and Huber, Florian and Klieber, Karin (2023) Real-time inflation forecasting using non-linear dimension reduction techniques. International Journal of Forecasting, 39 (2). pp. 901-921. ISSN 0169-2070 (https://doi.org/10.1016/j.ijforecast.2022.03.002)

[thumbnail of Hauzenberger-etal-IJF-2023-Real-time-inflation-forecasting-using-non-linear-dimension-reduction-techniques]
Preview
 Text. Filename: Hauzenberger_etal_IJF_2023_Real_time_inflation_forecasting_using_non_linear_dimension_reduction_techniques.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

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 with 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.

ORCID iDs

Hauzenberger, Niko ORCID logoORCID: https://orcid.org/0000-0002-2683-8421, Huber, Florian and Klieber, Karin;
CORE (COnnecting REpositories)