The Role of Machine Learning in Forecasting U.S. GDP Growth after the COVID-19 Pandemic

Abstract

The COVID-19 pandemic resulted in one of the most recent economic shocks, impacting global trade, financial markets, and consumer behavior. In the US, GDP suffered a historic downturn in 2020, followed by an unbalanced recovery. Precise GDP growth forecasting has become increasingly essential for policymakers, businesses, and investors making decisions in the post-pandemic economy. Classic models, including Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR), and Dynamic Stochastic General Equilibrium (DSGE), have been popularly employed for GDP forecasting. Machine learning (ML) provides a dominant alternative, with the potential to handle enormous amounts of real-time data, sense non-linear patterns, and handle economic shocks more effectively than traditional approaches. This paper delves into the potential of ML in GDP forecasting, touching on some key techniques, including neural networks, ensemble learning, and deep learning. This paper assessed the accuracy of two machine learning models, Random Forest (RF) and Long Short-Term Memory (LSTM), in forecasting U.S. GDP growth during the post-COVID-19 pandemic. Although ML-based forecasting holds prominent advantages, challenges, including data quality, explainability, and ethical issues, must be resolved for increased usage in economic decision-making.