Tools for non-linear time series forecasting in economics - An empirical comparison of regime switching vector autoregressive models and recurrent neural networks

被引：11
|
作者：
Binner, JM
Elger, T
Nilsson, B
Tepper, JA
机构：
[1] Aston Univ, Aston Business Sch, Dept Strateg Management, Birmingham B4 7ET, W Midlands, England
[2] Lund Univ, Dept Econ, S-22100 Lund, Sweden
[3] Nottingham Trent Univ, Fac Construct, Sch Comp & Technol, Nottingham, England
来源：
APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN FINANCE AND ECONOMICS | 2004年 / 19卷
关键词：
D O I：
10.1016/S0731-9053(04)19003-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is U.K. inflation and we utilize monthly data from 1969 to 2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast horizons. Both non-linear models perform significantly better than the VAR model.
引用
收藏
页码：71 / 91
页数：21
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