A New Procedure for Generalized STAR Modeling using IAcM Approach

Authors

  • Utriweni Mukhaiyar Statistics Research Division, Institut Teknologi Bandung, Jalan Ganesha 10 Bandung, Jawa Barat 40132, Indonesia
  • Udjianna S. Pasaribu Statistics Research Division, Institut Teknologi Bandung, Jalan Ganesha 10 Bandung, Jawa Barat 40132, Indonesia

DOI:

https://doi.org/10.5614/itbj.sci.2012.44.2.7

Abstract

A new procedure of space-time modeling through the Invers of Autocovariance Matrix (IAcM) is proposed. By evaluating the IAcM behaviors on behalf of the Generalized Space-Time Autoregressive (GSTAR) process stationarity, we may find an appropriate model to space-time data series. This method can complete the Space-Time ACF and PACF methods for identifying space-time models. For study case, we apply the GSTAR models to the monthly tea production of some plantations in West Java, Indonesia.

References

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Borovkova, S.A., Lopuhaa, H.P., & Nurani, B., Consistency and Asymptotic Normality of Least Squares Estimators in Generalized Space- Time Models, Statistica Neerlandica, 62, pp. 482-508, 2008.

Laub, A.J., Matrix Analysis for Scientists & Engineers, SIAM, Philadelphia, 2005.

Wei, W.W.S., Time Series Analysis: Univariate and Multivariate Methods 2nd. Ed., Pearson Addison Wesley, Boston, 2006.

Box, G.E.P., Jenkins, G.M. & Reinsel, G., Time Series Analysis, Forecasting and Control, 3rd ed., Prentice Hall, New Jersey, 1994.

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