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

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...

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Bibliographic Details
Published inEnergies (Basel) Vol. 16; no. 11; p. 4451
Main Authors Al Shammre, Abdullah Sultan, Chidmi, Benaissa
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 31.05.2023
Subjects
Accuracy
ARIMA-GARCH
Autoregressive models
Commodities
Crude oil
Crude oil prices
Economic development
Economic growth
Energy consumption
Energy prices
Forecasting
Forecasts and trends
FRED data
International finance
Machine learning
Natural gas
Nuclear energy
oil price forecasting
partial least squares
Researchers
Stochastic models
Time series
Variables
Volatility
Trinidad and Tobago
United Kingdom
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16114451