Forecasting Iranian Inflation Rates Using .St ructura l, Time
Series, and Artificial Neural Networks Models
Abstract
In this paper, I develop three forecasting models: namely structural,
times series, and artificial neural networks; to forecast Iranian inflation
rates. The structural model uses aggregate demand and aggregate supply
approach, the time series model is based on the standard ARlMA
technique, and the artificial neural network applies multi-layer back propagation modeL The latter, which is rooted in physic, cognitive, and
computer sciences, mimics human brain to learn any complex pattern and
to forecast their future behavior-the results of the forecasting competition
show that the back propagation model is able to generate inflation•
forecasts much better than the traditional competitors
(2001). Forecasting Iranian Inflation Rates Using .St ructura l, Time
Series, and Artificial Neural Networks Models. Journal of Economic Research (Tahghighat- E- Eghtesadi), 36(1), -.
MLA
. "Forecasting Iranian Inflation Rates Using .St ructura l, Time
Series, and Artificial Neural Networks Models", Journal of Economic Research (Tahghighat- E- Eghtesadi), 36, 1, 2001, -.
HARVARD
(2001). 'Forecasting Iranian Inflation Rates Using .St ructura l, Time
Series, and Artificial Neural Networks Models', Journal of Economic Research (Tahghighat- E- Eghtesadi), 36(1), pp. -.
VANCOUVER
Forecasting Iranian Inflation Rates Using .St ructura l, Time
Series, and Artificial Neural Networks Models. Journal of Economic Research (Tahghighat- E- Eghtesadi), 2001; 36(1): -.
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