Comparison of FEM-LSDV Panel Regression with Classical Panel Regression Models in Analyzing Economic Growth in Indonesia - Andi, Harismahyanti A; Alimatun, Najiha; Yunita, Andi Isna; Ratmila, Ratmila; Nur'eni, Nur'eni (Journal of Applied Informatics and Computing, 2025)
This study evaluates the performance of multiple panel regression approaches in modeling the determinants of regional economic growth in Indonesia. It specifically compares three classical panel models: the Common Effect Model (CEM), the Random Effect Model (REM), and the Fixed Effect Model (FEM), alongside the Fixed Effect Model with the Least Squares Dummy Variable (FEM LSDV) approach. The analysis is based on panel data covering 34 provinces from 2019 to 2023, using key macroeconomic indicators such as inflation, investment, exports, money supply, open unemployment rate, and participation in the national health insurance program (JKN). The models are assessed using formal statistical tests, including the Chow and Hausman tests, and evaluated through performance metrics such as RMSE, AIC, and R-squared. The results show that the FEM LSDV model offers the best performance, with an R-squared value of 0.7039, RMSE of 0.5442, and an AIC of 365.55. Notably, the model identifies North Maluku Province as contributing positively and significantly to economic growth, while the year 2020 shows a significant negative impact, likely due to the economic disruptions caused by the COVID-19 pandemic. These findings demonstrate the effectiveness of the FEM LSDV approach in capturing both spatial and temporal heterogeneity in regional economic analysis and support its application in policy-oriented research.