Analisis Regresi Data Panel dalam Memodelkan Faktor yang Mempengaruhi Stunting di Indonesia - Ardiana Fatma Dewi; Hartina Husain (Journal of Mathematics, Computations and Statistics, 2024)
Most nations deal with stunting to some extent, but emerging nations and those in the lower middle class, like Indonesia, have it the worst. Accordingly, the stunting epidemic remains the top priority for the Indonesian government at the present time. Because of its many potential future effects, stunting is a major worry for the government. Determining what variables may impact stunting in Indonesia is one approach to bolstering this initiative. Panel data will be used in this study due to the fact that different provinces in Indonesia exhibit different features on an annual basis. In 2020 and 2022, researchers in 34 of Indonesia's provinces used panel data regression analysis to identify the variables that impact stunting rates. Based on these findings, a model using a fixed effect estimator was determined to be the most effective. The results of the Chow and Hausman tests support this idea, respectively showing that fixed-effect models outperform common-effect models and random-effect models. Both full and partial tests show that the number of stunted children under five in Indonesia is significantly affected by the variables of poverty rate and gross regional product per capita. The panel data regression model using the poverty rate and GDP per capita provides a strong explanation for the prevalence of stunting in children less than five years old, as shown by the model's coefficient of determination of 94.29%.