Comparison of the forecast performances of linear time series and artificial neural network models within the context of Turkish inflation
Author(s)
Advisor
Sayan, SerdarDate
2001Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
This thesis compares a variety of linear and nonlinear models to find the one with the best
inflation forecast performance for the Turkish Economy. These comparisons are performed
by considering the type of series whether or not stationary. Different combination
techniques are applied to improve the forecasts. It is observed that the combination
forecasts based on nonstationary vector autoregressive (VAR) and artificial neural network
(ANN) models are better than the ones generated by other models. Furthermore, the
forecast values combined with ANN technique produce lower root mean square errors
(RMSE) than the other combination techniques.