Niko Hauzenberger, Florian Huber, Karin Klieber
arXiv 15 Dec 2020 · Econometrics · publishedInternational Journal of Forecasting (2022) · 39 citations (OpenAlex)
arXiv:2012.08155 · PDF · DOI · OpenAlex · Extracted main text
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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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Stock and Watson (2002) Macroeconomic forecasting using diffusion indexes | 1.000 | 7 | 3 | 100% |
| 2 | Koop and Korobilis (2012) Forecating inflation using dynamic model averaging | 0.874 | 5 | 2 | 100% |
| 3 | Bai and Ng (2008) Forecasting economic time series using targeted predictors | 0.811 | 4 | 2 | 100% |
| 4 | Raftery et al (2010) Online prediction under model uncertainty via Dynamic Model Averaging: Application to a cold rolling mill | 0.811 | 4 | 2 | 100% |
| 5 | Chan et al (2020) Reducing the state space dimension in a large TVP-VAR | 0.754 | 7 | 4 | 43% |
| 6 | McCracken and Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.737 | 3 | 3 | 67% |
| 7 | Exterkate et al (2016) Nonlinear forecasting with many predictors using kernel ridge regression | 0.737 | 3 | 2 | 100% |
| 8 | Koop and Korobilis (2013) Large time-varying parameter VARs | 0.737 | 3 | 2 | 100% |
| 9 | Clark (2011) Real-time density forecasts from BVARs with stochastic volatility | 0.644 | 3 | 2 | 67% |
| 10 | Kelly et al (2019) Characteristics are covariances: A unified model of risk and return | 0.644 | 2 | 2 | 100% |
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