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Forecasting the U.S. Real House Price Index

Vasilios Plakandaras (), Rangan Gupta (), Periklis Gogas () and Theophilos Papadimitriou ()

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Abstract: The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.

New Economics Papers: this item is included in nep-big, nep-for and nep-ure
Date: 2017-07
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Published in Economic Modelling, vol. 45, pp. 259-267, 2015

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Related works:
Journal Article: Forecasting the U.S. real house price index (2015) Downloads
Working Paper: Forecasting the U.S. Real House Price Index (2014)
Working Paper: Forecasting the U.S. Real House Price Index (2014) Downloads
Working Paper: Forecasting the U.S. Real House Price Index (2014) Downloads
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