Analysis of East Kalimantan's GRDP Elasticity in 2011-2023: A Bayesian Principal Components Log-Linear Regression Approach

Abstract

This study investigates the elasticity of East Kalimantan's Gross Regional Domestic Product (GRDP) at constant prices 2010 to five economic indicators using a Bayesian Principal Components Log-linear Regression (PCLR) model for the 2011-2023 period. The indicators used include the import volume of non-crude oil fuel, employed population, median value of the Composite Stock Price Index (CSPI), electricity consumption, and China's energy consumption. Principal component analysis yields the Economic Fundamental Indicator explaining 76.1436% and the Fuel Import Indicator explaining 20.6950% of the total data variability of 96.8386%. Estimation results show that a one-unit increase in the Economic Fundamental Indicator and Fuel Import Indicator will increase the GRDP at constant prices by 7.17% and 2.01% respectively. The model satisfies the residual normality assumption based on the Shapiro-Wilk test with a p-value of 0.9871 and shows no significant autocorrelation with a Durbin-Watson statistic of 1.4486. These findings imply the importance of strengthening regional economic fundamentals through labor market optimization, energy infrastructure development, increased integration with the national capital market, and economic diversification to reduce vulnerability to external shocks.

Keywords: GRDP at constant prices elasticity, Principal Components Log-linear Regression, East Kalimantan, Bayesian method