Abstract
This study aims to evaluate the effect of business sector and export value on economic growth in Indonesia using Generalised Linear Models (GLM) and Integrated Nested Laplace Approximation (INLA) approaches with Gamma and Gaussian distributions. The results showed that agriculture, forestry and fishing (PKP), mining and quarrying (PP) and fiscal (PK) sectors have a significant impact on economic growth, while real estate (RE) and export value (NE) are not significant. Based on the Akaike Information Criterion (AIC), the best model is the GLM with Gaussian distribution, which provides a balance between model complexity and the ability to explain the data. The INLA method provided consistent results with informative confidence intervals, but did not outperform the GLM in model evaluation. This study provides insights for policy makers to prioritise the PKP, PP and PK sectors in promoting economic growth and recommends further research with additional variables and more complex analytical methods for more in-depth results.
Published Version
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