Comparison of spatial regression models for Gross Regional Domestic Product (GRDP): A case study in East Java, Indonesia

Abstract

The COVID-19 pandemic began at the end of 2019 in.Wuhan, China, spreading rapidly to all parts of the world. This virus was detected in Indonesia in February 2020, and then the government began to make social restriction policies in March 2020. All countries in the world experienced a fairly severe economic shock. The limited mobility of people impacts decreasing all forms of activities that require face-to-face contacts, such as tourism, transportation, hospitality, etc. It causes a decrease in economic growth which can be measured by the size of the gross regional domestic product. This analysis uses Gross Regional Domestic Product (GRDP) data as a Y variable, general allocation funds (X1), profit-sharing funds (X2), open unemployment rate (X3), labor force participation rate (X4), and the number of industries (X5). It will be analyzed using spatial regression. There are several spatial regressions such as Spatial Autoregressive (SAR) and Spatial Error Model (SEM), Spatial Durbin Model (SDM) and Spatial Durbin Error Model (SDEM). The SDM and SDEM are special cases of SAR and SEM. SDM involves spatial effects on independent and dependent variables, while SDEM involves spatial effects on dependent variable and error. So, we will analyzed using SDM and SDEM. From the results, SDEM is the best method used for the data, and all variables used are significant. The predicted GRDP values of districts/cities in East Java are classified into five classes. There are eight regencies/cities included in the class with declining GRDP due to the COVID-19 pandemic, namely Ngawi Regency, Bangkalan Regency, Pamekasan Regency, Blitar City, Probolinggo City, Pasuruan City, Mojokerto City, and Batu City.