Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

被引:72
|
作者
Bork, Lasse [1 ]
Moller, Stig V. [2 ]
机构
[1] Aalborg Univ, Dept Business & Management, Aalborg, Denmark
[2] Aarhus Univ, Dept Econ & Business, DK-8000 Aarhus C, Denmark
关键词
Forecasting housing markets; Kalman filtering methods; Model change; Parameter shifts; Boom-bust cycle; PREDICTIVE ACCURACY; NESTED MODELS; TESTS;
D O I
10.1016/j.ijforecast.2014.05.005
中图分类号
F [经济];
学科分类号
02 ;
摘要
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantially. The states in which housing markets have been the most volatile are the states in which model change and parameter shifts have been needed the most. (c) 2014 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:63 / 78
页数:16
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