MODELLING GROSS DOMESTIC REGIONAL BRUTO IN CENTRAL JAVA PROVINCEUSING SPATIAL REGRESSION

Abstract

Central Java is considered potential to trigger an increase in national economic growth, the economic characteristics of Central Java is determined by agriculture, industry and trade, hotels and restaurants (PHR).
Each region in Central Java has different characteristics, the western and southern regions are dominated by the agricultural sector, the northern region is dominated by PHR, while the east is dominated by the industrial sector.
Based on these characteristics, it is necessary to do a spatial data-based analysis on GRDP data so that the above
phenomena are modeled based on the economic characteristics of each region and know the relation of the
region to each other in the context of the growth of Gross Regional Domestic Product (GRDP). Spatial regression is one of the solutions of the above problems, this method of development of regression analysis, spatial regression not only see the global effect also see the local effect. In this study using spatial regression with lag in independent variables, this model is called spatial lag X (SLX). Data used in this research is data obtained from Central Bureau of Statistics (BPS) in 2015, including data of GRDP price applies to 35 districts and cities in Central Java Province for the year 2015. Besides data of GRDP, data of factors influencing GRDP price Such as Road infrastructure data, Human Capital, and Manpower, are also used in this study. Based on the analysis result, it can be concluded that the human capital parameters give significant influence on OLS and SLX model. While in the SLX model only the weighted variable of labor has significant effect. Furthermore the
best model is shown with the highest R2 value, the SLX model produces R2 of 0.64, so the best model obtained
is the SLX model. Thus, it can be concluded that the GRDP value in a region in Central Java is influenced by the value of the human capital of the region as well as the labor of the nearest region.
Keywords: Spatial Regression, Moran’s I, Gross Domestic Regional Bruto