The simulation studies for Generalized Space Time Autoregressive -X (GSTARX) model

Abstract

Generalized Space Time Autoregressive-X (GSTARX) is a model that involve the predictor variable (X) introduced by Pfeifer dan Deutsch. Generalized Space Time Autoregressive (GSTAR) is one of multivariate time series models that combine elements of time and location or spatial data or time series. X Variable in GSTAR is a symbol that has a metric and non-metric scale. For the case of unvariate time series using the predictor X with metric scale called the Transfer Function Model, while for non-metric scale called the Intervention Model and Calendar Variations. The literature studies showed that studies regarding the approach of multivariate time series by using GSTAR-X is still limited to models involving variable X with non-metric scale, so that in this research restricted use a variable X with a metric scale. GSTAR-X estimation method for using the Generalized Least Square (GLS), as well as the estimation method on the model Seemingly Unrelated Regression (SUR) that introduced by Zellner. The purpose of this research is to obtain a parameter estimation from GSTAR-X model with simulation study. Results of the simulation study showed that, if the residual of simulation are correlated, it will generate a error standard of parameters estimate values are small in GSTARX-SUR model than GSTARX-OLS so it can be said that the parameter estimation using GSTARX-SUR is more efficient than GSATRX-OLS.