Comparative Study of Estimation Power of Artificial Neural Networks and Autoregressive Time Series Models in Inflation Forecasting

Message:
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.
Language:
Persian
Published:
Journal of Economic Research, Volume:42 Issue: 81, 2008
Pages:
25 to 35
https://www.magiran.com/p510354  
سامانه نویسندگان
  • Author (1)
    Payam Hanafizadeh
    Professor Department of Information Technology and Operations Management, Allameh Tabataba'i University, Tehran, Iran
    Hanafizadeh، Payam
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