SAR-FEM Spatial Panel Regression for Spatio-Temporal Modeling of Stunting Cases in Indonesia

Zakiyah Mar'ah; Isma Muthahharah; Sitti Masyitah Meliyana R

doi:10.37905/euler.v14i2.38016

SAR-FEM Spatial Panel Regression for Spatio-Temporal Modeling of Stunting Cases in Indonesia

Zakiyah Mar'ah, Isma Muthahharah, Sitti Masyitah Meliyana R

Abstract

Stunting remains a crucial issue in Indonesia, with a prevalence of 21.5\% in 2023. This study aims to model the number of stunting cases in toddlers in 34 provinces in Indonesia (2020--2022) using Spatial Panel Regression to address the weaknesses of traditional regression that ignore the effects of spatial and temporal dependencies. Predictor variables analyzed include the percentage of malnutrition, underweight, poor population, access to basic health facilities, and access to drinking water services. The selection of the best model specification was carried out using the Chow test, Hausman, and the Bayesian log-marginal posterior probabilities approach. The results of the diagnostic test confirmed the existence of spatial and temporal autocorrelation in stunting cases. Based on the Bayesian analysis, the Spatial Autoregressive (SAR) Fixed Effect (FEM) model was selected as the most optimal model with a log-marginal value of -21.360, a posterior probability of 0.630, and a coefficient of determination ($R^2$) of 0.880. Impact analysis shows that the percentage of underweight children and access to health facilities have a significant direct effect on stunting in a region. However, no significant indirect spillover effect from neighboring provinces was found. Therefore, policymakers are advised to formulate stunting management strategies that focus on precisely addressing local determinants in each region.

Keywords

Stunting; Spatial Autoregressive; Fixed Effect Model; Bayesian Approach; Spatial Panel; Spatio-Temporal

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DOI: https://doi.org/10.37905/euler.v14i2.38016