Penerapan Regresi Data Panel Pada Model Produk Domestik Regional Bruto Sumatera Utara Tahun 2020-2023

Abstract

Panel data regression is a method that combines cross section data and time series data, cross section data is data collected at a certain time from many observation units, while time series data is data collected from one observation unit that is observed periodically within a certain period of time. The panel regression model can be obtained from three estimates, namely the Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). An important indicator to see the economic condition in a region at a certain period of time is the value of Brutto Regional Domestic Product (GRDP), panel data regression can be used to model the GRDP of North Sumatra. GRDP is the total value of production of goods and services in a region. This study aims to model the GRDP of North Sumatra in 2020-2023 with several independent variables including regional own-source revenue (PAD), human development index (IPM), open unemployment rate (TPT), population, government expenditure (PP), district/city minimum wage and malnutrition babies. The results of this study show that the model that best fits the GDRP of North Sumatra is fixed effect with the following model: Y = -35563,74+16,74156 PAD+660,0981 IPM + 1,667042 PP Based on the fixed effect model, it is found that the model used produces an R-square value of 0,9995 which means that 99,95% of North Sumatra's GRDP value can be explained by the variables used in the model, while the remaining 0.05% can be explained by other variables not included in the model.