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GDP Forecasting: Machine Learning, Linear or Autoregression?
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DOI：10.3389/frai.2021.757864.png)
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摘要
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This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly 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 the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.
Keyword:
k nearest neighborhood
machine learning
time series
GDP
forecasting strategies
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