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Forecasting the U.S. Real House Price Index

  • Vasilios Plakandaras

    ()

    (Department of Economics, Democritus University of Thrace, Greece)

  • Rangan Gupta

    ()

    (Department of Economics, University of Pretoria)

  • Periklis Gogas

    ()

    (Department of Economics, Democritus University of Thrace, Greece)

  • Theophilos Papadimitriou

    ()

    (Department of Economics, Democritus University of Thrace, Greece)

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.

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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201418.

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Length: 25 pages
Date of creation: May 2014
Date of revision:
Handle: RePEc:pre:wpaper:201418
Contact details of provider: Postal: PRETORIA, 0002
Phone: (+2712) 420 2413
Fax: (+2712) 362-5207
Web page: http://www.up.ac.za/economics

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