Quotation Hauzenberger, Niko, Huber, Florian, Klieber, Karin. 2020. Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques.


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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 these latent factors using state-of-the-art time-varying parameter (TVP) regressions with shrinkage priors. Using monthly real-time data for the US, our results suggest that adding such non-linearities yields forecasts that are on average highly competitive to ones obtained from methods using linear dimension reduction techniques. Zooming into model performance over time moreover reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques
Year 2020
URL https://arxiv.org/abs/2012.08155
JEL C11, C32, C40, C53, E31

Associations

Projects
The impact of fiscal policy on the term structure of interest rates within the Eurozone
People
Hauzenberger, Niko (Details)
Huber, Florian (Former researcher)
External
Klieber, Karin (Universität Salzburg, Austria)
Organization
Institute for Economic Geography and GIScience IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1145 Time series analysis (Details)
5323 Econometrics (Details)
5335 Political economic theory (Details)
5371 Macroeconomics (Details)
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