Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis

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• 作者：Hossein Hassani ; Zara Ghodsi ; Rangan Gupta ; Mawuli Segnon
• 关键词：Home sales ; Forecasting ; Singular spectrum analysis ; Classical and Bayesian (vector) autoregressive models
• 刊名：Computational Economics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：49
• 期：1
• 页码：83-97
• 全文大小：
• 刊物类别：Business and Economics
• 刊物主题：Economic Theory/Quantitative Economics/Mathematical Methods; Computer Appl. in Social and Behavioral Sciences; Operation Research/Decision Theory; Behavioral/Experimental Economics; Math Applications
• 出版者：Springer US
• ISSN：1572-9974
• 卷排序：49

文摘

Accurate forecasts of home sales can provide valuable information for not only, policy makers, but also financial institutions and real estate professionals. Given this, our analysis compares the ability of two different versions of singular spectrum analysis (SSA) methods, namely recurrent SSA (RSSA) and vector SSA (VSSA), in univariate (UV) and multivariate (MV) frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy and its four census regions (Northeast, Midwest, South and West). We compare the performance of the SSA-based models with classical and Bayesian variants of the autoregressive (AR) and vector AR models. Using an out-of-sample period of 1979:8–2014:6, given an in-sample period of 1973:1–1979:7, we find that the UVVSSA is the best performing model for the aggregate US home sales, while the MV versions of the RSSA is the outright favorite in forecasting home sales for all the four census regions. Our results highlight the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.