Abstract
The study aims to propose the neural network filter based on NNSFDI method as an alternative filter vis-a-vis to frequently applied filters in economics; namely, Baxter–King, Hodrick–Prescott, Christiano and Fitzgerald and Kalman filters. In this paper, it was used two different data which consist of the annual unemployment rates for 1923–2008 periods and the monthly inflation rates for 1964:02–2009:07 periods. The performance of the new method proposed and the mainstream filters were, in particular, evaluated based on the annual and monthly data. The empirical findings suggest that the newly proposed NNFSDI model provided better forecast results compared to Kalman, HP, BK and CF filters for different data sets when evaluated in the light of different error criteria such as MSE, RMSE and MAPE.
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