Forecasting US GDP in the Post-pandemic Age: An Evaluation of AR Model and Machine Learning Algorithms

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 models ability to utilize data after a sudden shock. Since AR model predict one years GDP based on the previous years 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 models 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.