Forecasting Performance Comparison With Panel Data Models: Environmental Kuznets Curve Analysis

In this study, forecast analysis was conducted on the basis of different panel data structures and predictors. Panel data models, constructed within the framework of the Environmental Kuznets Curve, were developed using two separate unit groups: the G20 country group as heterogeneous panel data and the G8 country group as homogeneous panel data for the period 1990-2020. Subsequently, out-of-sample forecasts were obtained using a fixed effects predictor, a random effects predictor, and combined forecasting methods, and the performances of these forecasts were compared. Forecast values were estimated for out-of-sample 1 year, 3 years, and a 3-year average. Forecast performances were evaluated using the mean squared error and root mean squared error. As a result, it was found that, in line with the literature, homogeneous predictors exhibited better performance. In addition, it was observed that the forecast obtained with the fixed effects predictor in the homogeneous panel data structure performed better, whereas the forecast obtained with the random effects predictor in the heterogeneous panel data structure performed better. The combined forecast in the homogeneous panel data structure was better than the forecast obtained with the random effects predictor, whereas in the heterogeneous panel data structure, the forecast obtained with the fixed effects predictor performed worse than the combined forecasting method. In this study, the combined forecasting method developed by Huang (2019) was examined, and its performance was compared with other forecasting methods. Another perspective of this study was to examine the performance of the combined forecasting method under different heterogeneity and endogeneity conditions.