Innovative Application of Neural Networks to Fixed Effects Modeling

Traditional bidirectional fixed-effect models rely on simple linear regression coefficients, which may limit the predictive testing capabilities of the models. To solve this limitation, we introduce neural networks to enhance the predictive testing process of coefficients, thereby improving the prediction accuracy of the model and the robustness of the results.