عنوان به زبان ديگر :
A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran
پديد آورندگان :
Jafari-Samimi Ahmad نويسنده , Shirazi Babak نويسنده , Fazlollahtabar Hamed نويسنده
چكيده لاتين :
In general gross domestic product (GDP) is a substantial element in macroeconomic
analysis. Policy makers of a country use variations ofGDP for long
run planning. Considering different economic conditions of a country,
forecasting is a useful tool to identify the variations of GDP for planning. In
this paper, quarterly GDP value during (1998-2003) is used as a base of
analysis. The quarterly GDP values of the year (2004 -2005) are forecasted
using Time series, Exponential smoothing and Neural network approaches.
The results are compared with actual quarterly GDP value and error
measurement are computed in each methods. Consequently statistical analyses
are accomplished to show the best method of forecasting. We have shown that .
neural network approach method is the best alternative to forecast the GDP of
Iran.
