Abstract
In this paper, forecast of one-dimensional integrated autoregressive bilinear is compared with forecast of generalized integrated autoregressive bilinear model. We describe the method for estimation of these models and the forecast. It is also pointed out that for this class of non-linear time series models; it is possible to obtain optimal forecast. The estimation technique is illustrated with respect to a time series, and the optimal forecast of these time series are calculated. A comparison of these forecasts is made using the two models under study. The mean square error for forecast in generalized integrated autoregressive bilinear model is smaller than the mean square error for forecast in one-dimensional integrated autoregressive bilinear model. Though the two models are adequate for forecast when compared with the real series but forecast with generalized integrated autoregressive bilinear model is more adequate.Keywords: Optimal Forecast, Non-Linear Time Series Models, Bilinear Models, Estimation Technique, Mean Square Error.
Highlights
The bilinear time series models have attracted considerable attention during the last years
As a result the performance of generalized integrated autoregressive bilinear time series models is better when it is used for forecasting
Two bilinear time series models that were capable of achieving stationary for all non linear series were considered
Summary
The bilinear time series models have attracted considerable attention during the last years. Ojo (2011) proposed one-dimensional integrated autoregressive bilinear time series model that could achieve stationary for all non linear time series. Ojo and Shangodoyin (2010) proposed generalized integrated autoregressive bilinear time series model that could achieve stationary for all non linear time series.
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