Invertibility of Generalized Space-Time Autoregressive Model with Random Weight

Yundari Yundari, Setyo Wira Rizki


The generalized linear process accomplishes stationarity and invertibility properties. The invertibility property must be having a series of convergence conditions of the process parameter. The generalized Space-Time Autoregressive (GSTAR) model is one of the stationary linear models therefore it is necessary to reveal the invertibility through the convergence of the parameter series. This article studies the invertibility of model GSTAR(1;1) with kernel random weight. The result shows that the model GSTAR(1;1) under kernel random weight fulfills the invertibility property and obtains a finite order of Generalized Space-Time Moving Average (GSTMA) process. The other result obtained is the time order of the finite orde  . On the Triangular kernel resulted in the relatively great value n, so that it does not apply to the kernel with a finite value n.


invertibility; generalized linear process; autoregression process; stationarity.

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