Forecasting the U.S. real house price index

Show simple item record Plakandaras, Vasilios Gupta, Rangan Gogas, Periklis Papadimitriou, Theophilos 2016-05-05T07:10:29Z 2016-05-05T07:10:29Z 2015-02
dc.description.abstract The 2006 sudden and immense downturn inU.S. house prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paperwe 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), 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 price drops with direct policy implications. en_ZA
dc.description.department Economics en_ZA
dc.description.librarian hb2016 en_ZA
dc.description.sponsorship Vasilios Plakandaras, Dr. Papadimitriou Theophilos and Dr. Periklis Gogas were partly financed in this research by the European Union (European Social Fund — ESF) (MIS 380292) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) — Research Funding Program: THALES (MIS 380292), “Investing in knowledge society through the European Social Fund”. en_ZA
dc.description.uri en_ZA
dc.identifier.citation Plakandaras, V, Gupta, R, Gogas, P & Papadimitriou, T 2015, 'Forecasting the U.S. real house price index', Economic Modelling, vol. 45, pp. 259-267. en_ZA
dc.identifier.issn 0264-9993 (print)
dc.identifier.issn 1873-6122 (online)
dc.identifier.other 10.1016/j.econmod.2014.10.050
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2014 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Economic Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Economic Modelling, vol. 45, pp. 259-267, 2015. doi : 10.1016/j.econmod.2014.10.050. en_ZA
dc.subject House prices en_ZA
dc.subject Forecasting en_ZA
dc.subject Machine learning en_ZA
dc.subject Support Vector Regression en_ZA
dc.title Forecasting the U.S. real house price index en_ZA
dc.type Postprint Article en_ZA

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