DocumentCode
3413955
Title
Nonlinear phillips curves in the Euro area and USA? Evidence from linear and neural network models
Author
McNelis, Paul D.
Author_Institution
Dept. of Econoinics, Georgetown Univ., Washington, DC, USA
fYear
2003
fDate
20-23 March 2003
Firstpage
145
Lastpage
149
Abstract
The 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.
Keywords
economics; employment; forecasting theory; neural nets; Euro area; Phillips curve; US data; USA; forecast horizons; inflation forecasting; inflation predictor; linear forecasting methods; neural network methodology; nonlinear estimation; out-of-sample forecasting; unemployment; Economic forecasting; Equations; Feedforward neural networks; Intelligent networks; Neural networks; Neurons; Polynomials; Predictive models; USA Councils; Unemployment;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN
0-7803-7654-4
Type
conf
DOI
10.1109/CIFER.2003.1196254
Filename
1196254
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