Analysis of Factors Affecting District/City GRDP in Kalimantan Island
Keywords:GRDP, Multicollinearity, Geographically Weighted Regression
The Gross Regional Domestic Product (GRDP) is the added value of production obtained from various sectors. The value of GRDP is one of the indicators to see and measure the economic growth of a region. When compared to other islands, Kalimantan Island has a GRDP value that is quite low. Therefore, regression analysis is needed to see what factors affect the value of GRDP. However, the problem that is often found is that the local conditions of each place are different. There are many things behind it, one of which is in terms of geography. This is often referred to as spatial heterogeneity. One of the spatial modeling techniques that overcomes spatial heterogeneity is Geographically Weighted Regression. Because the weighting is based on the location of the observation or the area, it is possible that modeling on more than one explanatory variable has multicolinearity. There are several methods that are able to overcome multicolinearity in the GWR model, including Ridge regression and Least Absolute Shrinkage and Selection Operator (LASSO). In this study, the best model is the Geographically Weighted Regression model with a coefficient of determination (R2) of 97.63% and an RMSE value of 258711464. The dominant factors affecting the value of GRDP at each location are the Human Development Index (IPM), the number of workers, and the percentage of households using electricity.
. [BPS]. Central Bureau of Statistics. 2018. Gross Regional Domestic Product (GRDP) of Provinces in Indonesia 2014-2018. Bps. Jakarta
. Anselin L. 1988. Spatial Econometrics: Methods and Models. Dordrecht(NL): Kluwer Academic.
. Firdaus M. 2011. Econometrics: An Applicative Approach. Bumi Aksara Jakarta.
. Myers RH.1990. Classical and Modern Regression with Applications. PWS KENT Publishing Company.
. Wulandari R, Saefuddin A, Afendi FM. 2017, Application of Geographically Weighted Gulud Regression and Geographically Weighted LASSO Regression on Data Containing Multicollinearity: The Case of Regional Original Income Data in 27 Regencies/Cities in West Java Province [Thesis]. Bogor (ID): Bogor Agricultural Institute.
. Yulita, Tiyas., et al., 2016. Geographically Weighted Ridge Regression and Geographically Weighted LASSO Modeling On Spatial Data with Multicollinearity. [Thesis]. Bogor (ID): Bogor Agricultural Institute.
. Draper NR, Smith H. 1998. Applied Regression Analysis. 3rd Ed. New York (US): John Wiley & Sons.
. Friday OR, Emenonye C. 2012. The Detention and Correction of Multicollinearity Effects in a Multiple Regression Diagnostics. Elixir Statistics 49:10108- 10112.
. Wheeler DC. 2007. Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression. Environment and Planning A 39: 2464-2481.
. Fotheringham USA, Brunsdon C, Charlton M. 2002. Geographically Weighted Regression the Analysis of Spatially Varying Relationships. England (GB): John Wiley and Sons.
. Leung Y, Mei CL, Zhang WX. 2000. Statistical Test for Spatial Nonstationarity Based on The Geographically Weighted Regression Model. Environment and Planning A 32: 9-32.
. Hoerl AE, Kennard RW.1970. Ridge Regression: Biased Estimation for Nonthorgonal Problems. Technometrics .12: 55-67.
. Hastie T, Tibshirani R, Friendman J. 2009. The Elements od Statistical Learning Data Mining, Inference, and Prediction. New York (US): Springer.
. Draper NR, Smith H. 1992. Applied Regression Analysis. Ed.2nd. Sumantri B, translator. Jakarta (ID): Gramedia. Translation from: John Wiley and Sons.
. Montgomery DC, Peck EA. 1992. Introduction to Linear Regression Analysis. Ed 2nd. New York (US): John Wiley & Sons.
. Wheeler DC. 2006. Diagnostic Tools and Remedial Methods for Collinearity in Liniear Regression Models with Spatially Varying Coefficients. The Ohio State University.
. Tibshirani R. 1996. Regression Shrinkage and Selection via The LASSO. Journal of the Royal Statistical Society Series B. 58(1): 267-288.
. Hastie T, Thibsirani R, Friedman J. 2008. The Elements of Statistical Learning Sata Mining, Inference and Prediction. Ed 2nd. Springe. Stanford University.
. Wheeler DC. 2009. Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso. Journal of Environment and Planning A 41 (3): 722-742.
. Walpole RE. 1982. Introduction to Statistics. Ed 3th. Sumantri B, translator. Jakarta (ID): Gramedia Jakarta.
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