Title :
Nonlinear Phillips curves in the Euro area and USA? Evidence from linear and neural networks models
Author :
McNelis, Paul D.
Author_Institution :
Dept. of Econ., Georgetown Univ., Washington, DC, USA
Abstract :
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.
Keywords :
economic forecasting; feedforward neural nets; inflation (monetary); macroeconomics; nonlinear estimation; unemployment; Euro area; USA; feedforward network; inflation forecasting; inflation predictor; linear forecasting; neural network methodology; nonlinear Phillips curves; nonlinear estimation; out-of-sample forecasting; unemployment; Least squares approximation; Macroeconomics; Neural networks; Neurons; Nonlinear equations; Polynomials; Predictive models; Statistics; Testing; Unemployment;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1201871
