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
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;
Conference_Titel :
Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7654-4
DOI :
10.1109/CIFER.2003.1196254
