Abstract
Twenty-five machine learning (ML) methods and ordinary least squares regression (OLS) are trained to detect in-sample U.S. annual inflation rates up to a year in advance. The FRED-MD monthly dataset with 134 economic and financial variables from 1959 to April 2022 is used for training, validation, and forecasting. Out of these twenty-five ML methods, top-ten (by root mean square error or RMSE) are chosen to forecast the out-of-sample annual inflation rate. The ML methods are more accurate than the OLS in forecasting the annual inflation rate. The OLS does not appear in the top-10 list in any forecasting period. The ML methods robustly classify the labor market as the top factor in forecasting inflation. The labor market has a significantly higher impact on inflation than the housing or stock market.
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Notes
All 134 variables: https://s3.amazonaws.com/real.stlouisfed.org/wp/2015/2015-012.pdf.
Definition and details of CPIAUCSL from the FRED website: https://fred.stlouisfed.org/series/CPIAUCSL.
List of ML methods at scikit-learn: https://scikit-learn.org/stable/index.html.
Ensemble methods: https://en.wikipedia.org/wiki/Ensemble_learning.
25 ML regression methods: https://www.mathworks.com/help/stats/choose-regression-model-options.html.
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Malladi, R.K. Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period. Comput Econ (2023). https://doi.org/10.1007/s10614-023-10436-w
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DOI: https://doi.org/10.1007/s10614-023-10436-w