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.
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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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