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

Abdullah Sultan Al Shammre and Benaissa Chidmi ()
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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

Energies, 2023, vol. 16, issue 11, 1-24

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.

Keywords: oil price forecasting; partial least squares; ARIMA-GARCH; FRED data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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