Abstract
I build an innovative Dynamic Autoregressive Model (DAR) in forecasting time series, and make comparison with a Static Autoregressive Model (SAR). DAR model requires re-evaluating optimal orders and coefficients at each period, while SAR models simply treats them as constants. The optimal length of the base (historical data for building autoregressive models) has been also investigated. Results show that on average DAR models outperform SAR models by about 1% up to double digits percent. With increase of the length of the base, adjusted R-Squares for both models are diminishingly increasing, and errors (in percentage) differences are vanishing.
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