GDP Forecasting: Machine Learning, Linear or Autoregression? - Maccarrone, Giovanni; Morelli, Giacomo; Spadaccini, Sara (Frontiers in Artificial Intelligence, 2021)
This paper evaluates the predictive power of different models to forecast the real U.S. GDP. Using quarterly U.S. GDP data from 1976 to 2020 we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore whether predictors such as the yield curve, its latent factors, and a set of macroeconomic variables can increase the level of forecasting accuracy. The predictions result to be improved only when considering longer forecast horizons. The use of machine learning algorithm provide additional guidance for data-driven decision making.