Abstract
In this study, the traditional lag structure selection method in the Mixed Data Sampling (MIDAS) regression model for forecasting GDP was replaced with a machine learning approach using the particle swarm optimization algorithm (PSO). The introduction of PSO aimed to automatically optimize the MIDAS model's mixed-frequency lag structures, improving forecast accuracy and resolving the "forecast accuracy" and "forecast cost" weighting problem. The Diebold-Mariano test results based on U.S. macroeconomic data show that when the forecast horizon is large, the forecast accuracy of the PSO-MIDAS model is significantly better than other benchmark models. Empirical results show that, compared to the benchmark MIDAS model, the forecast accuracy of both univariate and multivariate PSO-MIDAS models improves by an average of 10% when the forecast horizon exceeds 2 quarters, and the optimization effect is greater compared to other benchmark models. The innovative use of the PSO algorithm addresses the limitations of traditional lag structure selection methods and enhances the predictive potential of the MIDAS model.