Geographically and Temporally Weighted Regression (GTWR) for Modeling Economic Growth using R

Abstract
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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.

Coefficient determination, Economic growth, GTWR, Spatial-temporal heterogeneity

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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