| Autor: |
Afifah, Dyah Laillyzatul, Rahardjo, Swasono, Kusumasari, Vita, Purwanto, Nusantara, Toto, Atikah, Nur, Kholifia, Nadia |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3049 Issue 1, p1-8, 8p |
| Abstrakt: |
The relationship between Gross Regional Domestic Product (GRDP) and unemployment can be presented in a simultaneous spatial equation to provide more comprehensive information. A spatial simultaneous equation is a system of interrelated equations in a set that includes spatial effects. In spatial simultaneous equations, endogenous variables in one equation can be explanatory variables in other equations. That can lead to endogeneity and result in biased and inconsistent estimators. One method that can overcome endogeneity is the Generalized Method of Moment (GMM). In this study, the spatial model used is the Spatial Durbin Error Model (SDEM). The Spatial Durbin Error Model is a spatial model that includes spatial lag on exogenous and error variables. The addition of spatial error effect aims to minimize the errors generated in the model so that the endogenous and explanatory variables generated in the model are closer to the original data. Based on this, the researcher will model GRDP and unemployment using a simultaneous equation system approach to the Spatial Durbin Error Model (SDEM) panel data using the GMM method. The spatial weights used in this study are Queen Contiguity and Customized weights. The results showed that the estimation of GMM in the simultaneous SDEM model with Customized weighting resulted in better estimation results than Queen Contiguity weighting. Unemployment and poverty variables have a negative and significant effect on GRDP. While the GRDP variable and the average length of schooling have a negative and significant effect on unemployment. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
