Bayesian seemingly unrelated regression modeling of gross regional domestic product using direct Monte Carlo

Gross regional domestic product (GRDP) is an important indicator to determine the development level of the economic conditions of a region in a given period. There are three main sectors of GRDP in East Java, Indonesia that include agriculture sector, manufacturing industrial sector, and the trade, hotels and restaurants sectors, contributing 72 percent to the total East Java’s GRDP. These three main sectors are often used as an indicator of economic development in East Java. This paper aims to create a seemingly unrelated regression (SUR) model using Bayesian approach coupled with direct Monte Carlo (DMC) to analyze the GRDP by involving four influencing factors, i.e., amount of labor, labor wage, domestic investment and foreign investment. The results show that the amount of labor is the highest elasticity on the GRDP model of three main sectors. Foreign investment, on the other hand, is the lowest elasticity on the GRDP model of the agricultural sector. In the last two main sectors, domestic investment is the lowest elasticity in GRDP model of the manufacturing industry sector and trade, hotels and restaurants sectors.