Title :
Nonlinear Phillips curves in the Euro Area and USA? Evidence from linear and neural network models
Author :
McNelis, Paul D.
Author_Institution :
Georgetown Univ., Washington, DC, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
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 :
economic cybernetics; forecasting theory; neural nets; statistical analysis; Euro Area; USA; forecast horizons; inflation forecasting; linear models; neural network models; nonlinear Phillips curves; 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 :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007540
