英文标题：
《Forecasting the U.S. Real House Price Index》
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作者：
Vasilios Plakandaras, Rangan Gupta, Periklis Gogas and Theophilos
  Papadimitriou
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最新提交年份：
2017
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英文摘要：
  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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中文摘要：
2006年美国房价突然大幅下跌，引发了2007年全球金融危机，重新激发了人们对预测经济稳定面临的这种紧迫威胁的兴趣。在本文中，我们提出了一种新的混合预测方法，该方法将信号处理领域的集成经验模式分解（EEMD）与源自机器学习的支持向量回归（SVR）方法相结合。我们用随机游走（RW）模型、贝叶斯自回归模型和贝叶斯向量自回归模型检验了该模型的预测能力。所提出的方法优于所有竞争模型，在有无样本外漂移预测的情况下，其误差为RW模型的一半。最后，我们认为，这种新方法可以作为预测房价突然下跌的预警系统，具有直接的政策影响。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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