Comparative Study of Estimation Power of Artificial Neural Networks and Autoregressive Time Series Models in Inflation Forecasting
Abstract
This article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. Using 37 years Iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. This study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series.
JEL Classifications: C51, C52, C53, E37
(2007). Comparative Study of Estimation Power of Artificial Neural Networks and Autoregressive Time Series Models in Inflation Forecasting. Journal of Economic Research (Tahghighat- E- Eghtesadi), 42(4), -.
MLA
. "Comparative Study of Estimation Power of Artificial Neural Networks and Autoregressive Time Series Models in Inflation Forecasting", Journal of Economic Research (Tahghighat- E- Eghtesadi), 42, 4, 2007, -.
HARVARD
(2007). 'Comparative Study of Estimation Power of Artificial Neural Networks and Autoregressive Time Series Models in Inflation Forecasting', Journal of Economic Research (Tahghighat- E- Eghtesadi), 42(4), pp. -.
VANCOUVER
Comparative Study of Estimation Power of Artificial Neural Networks and Autoregressive Time Series Models in Inflation Forecasting. Journal of Economic Research (Tahghighat- E- Eghtesadi), 2007; 42(4): -.
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