SPATIAL PANEL MODELING OF PROVINCIAL INFLATION IN INDONESIA TO MITIGATE ECONOMIC IMPACTS OF HEALTH CRISES

Probabilistic statistical modeling simplifies complex issues, including economic and health challenges, by applying inductive statistics. Spatial panel modeling, using Queen Contiguity weighting, has proven to be essential for analyzing inflation expenditure patterns during health crises, such as COVID-19 in Indonesia. This study highlights the impact of inflation on national economic stability and explores the inter-provincial relationships that influence inflation dynamics across expenditure groups. The purpose of this study is to develop a spatial panel model to address this gap, offering insights for policy and recovery strategies. The results reveal significant spatial interdependence in provincial inflation data, underscoring the role of spatial factors in economic analysis. Two models are identified: Spatial Autoregressive Model with Random Effects (SAR-RE) before the crisis and Spatial Error Model with Random Effects (SEM-RE) during the crisis. Transportation facilities consistently affect inflation, demonstrating the effectiveness of spatial panel modeling in guiding policies for economic stability and recovery.