Forecasting US GDP in the Post-pandemic Age: An Evaluation of AR Model and Machine Learning Algorithms
Year: 2025
Type: article
Abstract: The AR (1) model outperforms the random forest, KNN, gradient boosting, and Xgboost algorithms by yielding a lower MAE in the prediction result. The reason behind may be attributed to AR model’s ability to utilize data after a sudden shock. Since AR model predict one years’ GDP based on the previous year’s GDP, after a sudden shock, the effect of the shock will be captured when predicting the GDP of the year after the shock. But the machine learning algorithms evaluated in this research are trained using data in a fixed time frame. They are not able to utilize data in the testing data set and adjust accordingly. By evaluating the performance of the models, this research reveals the importance of a model’s ability to handle information of a sudden shock in GDP forecasting and that AR model possess such ability while random forest, KNN, gradient boosting, and Xgboost do not.
Author: Yifeng Jin
Institution: Emory University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409619897