Analisis Pengaruh Tingkat Pengangguran Terbuka (TPT) Terhadap Indikator Kemiskinan di Jawa Timur

Abstract

Regression analysis is a data analysis method that is used for a relationship between two or more variables. The purpose of this regression analysis is to investigate a pattern of relationship between predictor variables and respondent variables. Kernel Nonparametric Regression Estimator is an analytical estimator that is similar to the estimator in other nonparametric regressions, the difference is that this kernel estimator is more specialized in the problem of using its bandwidth method. The purpose of this study is to determine the effect of the open unemployment rate on poverty indicators in East Java. In this study, the kernel regression method will be used to model the effect of poverty indicators. Based on the results of the discussion, it can be concluded that the optimal bandwidth selection value is 0.65. The best nonparametric kernel regression model, the most important thing is the selection of the optimal bandwidth value, not in the selection of the kernel function, because the estimation results for the Gaussian kernel function using the optimal bandwidth are said to be very close together so it can be said that the use of the kernel function with the optimal bandwidth will result in a regression curve the same one.