comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting

نویسندگان

پیام حنفی زاده

استادیار گروه مدیریت صنعتی، دانشگاه علامه طباطبائی، دانشکدة مدیریت و حسابداری حسین پورسلطانی

کارشناسی ارشد مدیریت فنّ آوری اطلاعات، دانشگاه علاّمه طباطبائی، دانشکدة مدیریت و حسابداری پریسا ساکتی

کارشناسی ارشد مدیریت فنّ آوری اطلاعات، دانشگاه علامه طباطبائی، دانشکدة مدیریت و حسابداری

چکیده

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

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