FORECASTING IN ONE-DIMENSIONAL AND GENERALIZED INTEGRATED AUTOREGRESSIVE BILINEAR TIME SERIES MODELS

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 serie...

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Bibliographic Details
Published inGlobal journal of mathematical sciences Vol. 11; no. 1/2; p. 27
Main Author Ojo, J F
Format Journal Article
LanguageEnglish
Published Calabar Global Journal Series 01.01.2012
Subjects
Comparative studies
Errors
Estimating techniques
Forecasting
Mathematical analysis
Mathematical models
Mean square values
Nonlinearity
Optimization
Time series
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Summary: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. [PUBLICATION ABSTRACT]
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ISSN:1596-6208