Economic growth is a main condition for the sustainability of regional economic development. Spatially, the highest
economic growth in Indonesia is dominated by provinces in Java. However, the economic growth rate of Central Java Province is the
lowest economic growth compared to other provinces. The Geographically and Temporally Weighted Regression (GTWR) method
performed to model the economic growth of the Central Java Provincial districts by accommodating the influence of spatial-temporal
heterogeneity. This modeling involves four explanatory variables e.g, number of labor force, local revenue, district minimum wage, and
human development index with response variable gross regional domestic product. The results of the analysis showed that GTWR
method has better coefficient determination (99.8%) with root mean squared error and Akaike's Information Criterion values of 0.84
and 1051.98. In general, HDI gives more influence to economic growth at each regency / city in Central Java during 2011-2015.
Published In:IJCSN Journal Volume 6, Issue 6
Date of Publication : December 2017
Pages : 800-805
Figures :06
Tables : 07
M. Sholihin : master student in Department of Statistics, Bogor
Agricultural University. His main interests is on spatial analysis and
statistical computation.
Dr. A.M. Soleh : Currently worked as a lecture in Department of
Statistics, Bogor Agricultural University. His main interests is on
statistical computation and data mining.
Dr. A. Djuraidah : Currently worked as a lecture in Department of
Statistics, Bogor Agricultural University. Her main interests is on
spatial analysis and statistical probability teory.
The GTWR method is better than the global regression
method on economic growth modeling in Central Java in
2011-2015. GTWR model is able to explain the R2
value
of 99.8% with the RMSE and AIC values of 0.8403 and
1051.98. The human development index (X4) variables
give greater influence to economic growth at each regency
/ city in Central Java during the period of 2011-2015.
This study is limited to the Gaussian kernel function in
constructing a weighted matrix. In the next study can be
done with other kernel functions to get better results.
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