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Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models

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  • Abdullah Sultan Al Shammre

    (Economic Department, College of Business Administration, King Faisal University, Alahsa 31982, Saudi Arabia)

  • Benaissa Chidmi

    (Department of Agricultural & Applied Economics, Texas Tech University, Lubbock, TX 79424, USA)

Abstract

This paper investigates the forecasting accuracy of alternative time series models when augmented with partial least-squares (PLS) components extracted from economic data, such as Federal Reserve Economic Data, as well as Monthly Database (FRED-MD). Our results indicate that PLS components extracted from FRED-MD data reduce the forecasting error of linear models, such as ARIMA and SARIMA, but produce poor forecasts during high-volatility periods. In contrast, conditional variance models, such as ARCH and GARCH, produce more accurate forecasts regardless of whether or not the PLS components extracted from FRED-MD data are used.

Suggested Citation

  • Abdullah Sultan Al Shammre & Benaissa Chidmi, 2023. "Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models," Energies, MDPI, vol. 16(11), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4451-:d:1160822
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