Nonlinear Phillips curves in the Euro Area and USA? Evidence from linear and neural network models

被引：4

作者：

McNelis, PD ^{[1
]}

机构：

[1] Georgetown Univ, Dept Econ, Washington, DC 20057 USA

来源：

2003 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING, PROCEEDINGS | 2003年

关键词：

neural networks; Phillips curve; out-of-sample forecasting;

D O I：

10.1109/CIFER.2003.1196254

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

This paper applies neural network methodology to inflation forecasting in the Euro-area and the USA. Neural network methodology outperforms linear forecasting methods for the Euro Area at forecast horizons of one, three, and six month horizons, while the linear model is preferable for US data. The nonlinear estimation shows that unemployment is a significant predictor of inflation for the Euro Area. Neither model detects a significant effect of unemployment on inflation for the US data.

引用

页码：145 / 149

页数：5

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