The Multivariate Generalized Space-Time Autoregressive (MGSTAR) model is a model that is used to forecast space-time data with several variables in several locations. MGSTAR model has been developed into hybrid MGSTAR-ANN model for non-linear cases and hybrid MGSTARX-RNN model for cases with calendar variation effect. The calendar variation effect is a non-metric exogenous variable. This study aims to propose the MGSTARX model for cases with metric exogenous variables. There are two steps of MGSTARX modeling. The first step is modeling the data that involve exogenous variables using two approaches, i.e., Time Series Regression (TSR) and Transfer Function (TF). Then, the residuals from the first step are modeled using MGSTAR. This study focused on a simulation study to evaluate the goodness of the MGSTARX model. The result shows that the MGSTARX model with the transfer function approach is more accurate and has the smallest RMSE for forecasting the data than the other model. In general, the MGSTARX model with an exogenous variable can improve the accuracy of forecasting. This result is in line with the results of the M5 Accuracy Competition. Further study is needed to expand the MGSTARX model for higher dimensions data and other patterns such as seasonal and non-linear.