Variables Selection Affecting Indonesian Human Development Index Using LASSO

Etis Sunandi, Titin Siswantining

Abstract


According to Statistics Indonesia, the Human Development Index (HDI) is a measure that reflects the level of human development achievement in a region, based on three basic dimensions: a long and healthy life, knowledge, and a decent standard of living. There are many factors that are suspected to influence HDI in Indonesia. Another hand, estimation of parameters in regression analysis using the Least Squares Method will experience problems, if the number of independent variables is greater than the number of observations. One method that can be used to overcome this problem is to use the Least Absolute Shrinkage and Selection Operator (LASSO) method.  The purpose of this study is the selection of variables that affect Indonesia's Human Development Index (HDI) in 2023 using the LASSO. The LASSO method is known as a model used to select independent variables while overcoming multicollinearity problems. The ridge regression model is used as a comparison model. The results showed that LASSO Analysis is better than Ridge Regression. This can be seen from the Mean Squared Error of Prediction (MSEP) of LASSO (0.34) is smaller than the ridge regression (3.61). In addition, the r-squared value of LASSO is higher, which is 97.6%.

Keywords


LASSO, MSEP, R-Squared, Ridge Regression

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References


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DOI: http://dx.doi.org/10.12962%2Fj27213862.v8i2.22891

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