Examining regional factors on malnutrition rate in Indonesia using spatial autoregressive approach

Abstract

The most frequent issues in malnutrition rate modeling analysis are skewed distribution and spatial autocorrelation. Previous researches were generally focused on spatial autocorrelation between neighboring regions or auto relationships between malnutrition rates and significant factors across different quantiles of the malnutrition rate distribution, but rarely both. This study aims to estimate how contributing factors influence the malnutrition rate. The estimation is carried out by implementing the spatial autoregressive (SAR) approaches, including ordinary SAR, Robust SAR and SAR Quantile (SARQ), using 2021 data from the Health Ministry of Indonesia. The result shows that the SARQ outperforms the SAR and the Robust SAR in data fitness and prediction accuracy. The SARQ is also insensitive to outliers and skewed distribution. Estimation using SARQ provides effects of explanatory variables vary with the quantiles, while SAR and RSAR cannot do.