Comparison of modelling and prediction of export values in West Java, Banten, and Central Java using the STIMA and GSTIMA (1,1,1)

Abstract

Export is the delivery and sale of goods from a country to abroad. The growth of export values can be seen from time to time and it differs between locations which are influenced by spatial interactions. The Space-Time Autoregressive Moving Average (STARMA) model is a model that combines the interdependence of time and location. However, the STARMA model is sometimes seen unrealistic as it assumes all parameters in all locations to be the same. Meanwhile, The Generalized Space-Time Autoregressive Moving Average (GSTARMA) model is more realistic because it produces different parameters for each location. This study aims to compare the STIMA and GSTIMA models and to forecast export values. The STIMA and GSTIMA models are the models with zero-order for AR and apply First Difference. In this study, the STIMA and GSTIMA models with weighted inverse distance are used to predict the value of exports in three interacting provinces that have dominant superior sectors in the industrial sector, namely the Provinces of West Java, Banten, and Central Java. The data used is export values from January 2014 – December 2018. The identification of the model revealed the 1st order cut-off on lag 1 of the STACF plot with the first data differencing. The selected order of spatial lag is lag 1 because these three provinces are located on the same island. This is confirmed through the VARMA approach where the AR(0) and MA(1) models have the smallest AIC values so that the models constructed are the STIMA(1,1,1) and GSTIMA(1,1,1). The results of this study indicated that the GSTIMA(1,1,1) model produce better prediction than the STIMA(1,1,1) as it has a smaller MAPE value, where each MAPE value is 14.23% for STIMA and 11.38% for GSTIMA. This result indicates the fulfillment of different parameter assumptions at each location under the existing phenomenon that the export management of each location has different characteristics.