<![CDATA[This study evaluates and compares seven panel data model specifications in capturing temporal and cross-sectional variation using both simulated and empirical data. Panel data is employed for its ability to simultaneously account for heterogeneity across units and temporal dependence over time. In the first stage, Monte Carlo simulations assess model performance under controlled temporal structures, including AR(1), AR(2) and MA(3) processes. In the second stage, the models are applied empirically to poverty data across regencies and cities in East Java from 2012 to 2022. Simulation results are indicate that models explicitly incorporating stochastic temporal dynamics achieve the lowest RMSE, while specifications treating time merely as a covariate consistently underperform. Empirical results show that two-way fixed effects models controlling for persistent unit heterogeneity and common year effects provide the best predictive performance. Overall, findings highlight that appropriately modelling temporal variation is crucial for accurate panel data predictions, and the comparative evaluation offers guidance for selecting suitable model specifications in applied settings.]]>
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