Geographically Weighted Panel Regression for Modeling The Percentage of Poor Population in West Sumatera

Abstract

  Geographically Weighted Panel Regression (GWPR) model applies panel regression to spatial data, and parameter estimation is carried out using spatial weight at each observation point. The purpose of this study is to determine the GWPR model and the factors that influence the percentage of poor people in each district/city in West Sumatra Province from 2015 to 2021. And the adaptive bisquare kernel function was used to provide spatial weighting, and Cross-Validation (CV) criteria were used to identify the optimal bandwidth.  The research data was secondary data sourced from the official website and West Sumatra published books in Sumatera Barat Dalam Angka from 2015 to 2021. The GWR model and the FEM panel data regression model are combined to create the GWPR model. The results of this study is there are a differences between models and factors that affecting the poor percentages in 19 districts/cityes of West Sumatra.