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Journal : Jurnal Ekonomi Kuantitatif Terapan

PEMODELAN REGRESI PANEL PADA DATA PENDAPATAN ASLI DAERAH (PAD) TERHADAP DANA ALOKASI UMUM (DAU) Caraka, Rezzy Eko
Jurnal Ekonomi Kuantitatif Terapan 2019: Vol. 12, No.1, Februari 2019 (pp. 1-107)
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEKT.2019.v12.i01.p06

Abstract

Data panel is a composite of the data time series (over time) and cross section (between individuals / space). To describe briefly the data panel,egg in cross section,and the value of one or more variables were collected for the sample unit at a time of time. In panel data, the same cross section units surveyed in some time. Panel data regression was used to determine the most appropriate regression model is used to model local revenue (PAD) of the general allocation fund (DAU) for seven districts / cities in Central Java province from 2008 to 2010 budgets. Models produced by REM obtained R2 values ??of 43.8893 % revenue (PAD) is influenced by the General Allocation Fund (DAU), while the rest influenced by other factors.
HOW BIG POVERTY IN CENTRAL JAVA: MIXED REGRESSIVE-SPATIAL AUTOREGRESSIVE MODELS Caraka, Rezzy Eko
Jurnal Ekonomi Kuantitatif Terapan 2018: Vol. 11, No.1, Februari 2018 (pp. 1-144)
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEKT.2018.v11.i01.p04

Abstract

Mixed Regressive-Spatial Autoregressive Models (MR-SAM) is one spatial model with an area approach that takes into account the spatial influence of lag on the dependent variable. The advantage of this model is we can know the location has spatial effect or not. In this paper uses MR-SAM to determine and analyze the factors that affect the category of the poor in Central Java. MR-SAM is one of parametric regression, before using the model we must fulfill assumptions. In a nutshell, at significant ?=5% number of poverty in central java can be explained (statistically significant) by GDP, number of people didn?t finish primary school, and number of people who didn?t finished high school.