分位数因子增广神经网络分位数回归模型的构建及应用OACHSSCD

Macroeconomic forecasting provides vital reference for national macroeconomic regulation and corporate deci-sion-making.Although traditional factor-augmented regression models can effectively resolve forecasting problems in high-di-mensional data,they are difficult to fully depict the complex nonlinear relationships,distribution heterogeneity and heavy-tailed characteristics that are commonly present among economic variables.To this end,the paper proposes a Quantile Factor-Augment-ed Quantile Regression Neural Network Model,which combines the capacity of quantile factor model to describe the common in-formation at different quantiles with the nonlinear modeling capability of neural networks,in order to simultaneously take into ac-count the strong correlations,heavy-tailed distribution and intricate nonlinear relationships in high-dimensional data.Numerical simulation results demonstrate that the proposed model outperforms comparative models across all evaluation metrics under com-plex data conditions such as outliers and heavy-tailed distributions.Further empirical research is conducted by taking unemploy-ment rate forecasting as an example.The results show that the proposed model achieves high accuracy and favorable forecasting stability in both density prediction and point prediction.