Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period
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, a...
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Veröffentlicht in: | Computational economics 2024-07, Vol.64 (1), p.335-375 |
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description | 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. |
doi_str_mv | 10.1007/s10614-023-10436-w |
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subjects | Artificial intelligence Behavioral/Experimental Economics Benchmark tests Computer Appl. in Social and Behavioral Sciences Datasets Econometrics Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Forecasting Housing market Hypothesis testing Inflation rates Interest rates Labor Labor market Least squares method Machine learning Math Applications in Computer Science Mean square errors Operations Research/Decision Theory Phillips curve Root-mean-square errors Securities markets Variables |
title | Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period |
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