Forecasting Money Flow in East Java Using The Generalized Space-Time Autoregressive With Exogenous Variable Method

The forecast is more accurate when involving an exogen, for example, the generalized spatio temporal autoregressive (GSTAR) model with exogenous variables (GSTARX). This study aims to obtain appropriate statistical values to identify autoregressive orders for time dependencies and order effects with exogenous variables such as Eid al-Fitr and the consumer price index (CPI) in the GSTARX model. Additionally, this study seeks to obtain a GSTARX model suitable for forecasting. The case study is the inflows and outflows in four locations in East Java. The simulation results show that the statistical values for identifying VAR order for time dependencies use the matrix partial cross-correlation function (MPCCF). Meanwhile, effect orders from exogenous variables in the two-level GSTARX model show CCF at level one. In addition, GLS (general least squares) produces a more efficient estimator than the OLS (ordinary least squares). The results of the case study show that the two-level GSTAR model forming inflow and outflow data are GSTARX (31) and GSTARX ([1,3]1) respectively. In addition, the comparison of forecast accuracy shows that, in general, the smallest root mean square error (RMSE) value in the two-level GSTARX model is the inverse distance weight