Error Assumptions on Generalized STAR Model
Journal Title: Journal of Mathematical and Fundamental Sciences - Year 2017, Vol 49, Issue 2
Abstract
For GSTAR models, the least squares estimation method is commonly used since errors are assumed be uncorrelated. However, this method is not appropriate when errors are correlated, either in time or spatially. For these cases, the generalized least squares (GLS) method can be applied. GLS is more powerful since it has an error parameter that can act as a controller of the model to produce an efficient estimator. In this study, R Software was used to estimate GSTAR parameters. The resulted model was applied to real data, i.e. the monthly tea production of five plantations in West Java, Indonesia. The best model for forecasting was the GSTAR(1;1) model with temporally correlated error assumption.
Authors and Affiliations
Yundari Yundari, Udjianna Sekteria Pasaribu, Utriweni Mukhaiyar
Keywords
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- EP ID EP314902
- DOI 10.5614/j.math.fund.sci.2017.49.2.4
- Views 111
- Downloads 0
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