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Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach

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  • Yu-chin Chen

    (University of Washington)

  • Wen-Jen Tsay

    (Institute of Economics, Academia Sinica)

Abstract

This paper presents a generalized autoregressive distributed lag (GADL) model for conducting regression estimations that involve mixed-frequency data. As an example, we show that daily asset market information - currency and equity market movements - can produce forecasts of quarterly commodity price changes that are superior to those in the previous literature. Following the traditional ADL literature, our estimation strategy relies on a Vandermonde matrix to parameterize the weighting functions for higher-frequency observations. Accordingly, inferences can be obtained under ordinary linear least squares principles without Kalman filtering or non-linear optimizations. Our findings provide an easy-to-use method for conducting mixed data-sampling analysis as well as for forecasting world commodity price movements.

Suggested Citation

  • Yu-chin Chen & Wen-Jen Tsay, "undated". "Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach," Working Papers UWEC-2011-06, University of Washington, Department of Economics.
    • Handle: RePEc:udb:wpaper:uwec-2011-06
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    References listed on IDEAS

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    1. Marcy Burchfield & Henry G. Overman & Diego Puga & Matthew A. Turner, 2006. "Causes of Sprawl: A Portrait from Space," The Quarterly Journal of Economics, Oxford University Press, vol. 121(2), pages 587-633.
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    Cited by:

    1. Chen, Yu-chin & Turnovsky, Stephen J. & Zivot, Eric, 2014. "Forecasting inflation using commodity price aggregates," Journal of Econometrics, Elsevier, vol. 183(1), pages 117-134.
    2. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
    3. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
    4. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    5. Chen Yu-Chin & Rogoff Kenneth, 2012. "Are The Commodity Currencies An Exception To The Rule?," Global Journal of Economics (GJE), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 1-28.
    6. repec:dau:papers:123456789/15216 is not listed on IDEAS
    7. Evgenia Passari, 2015. "Commodity Currencies Revisited," Post-Print hal-01453266, HAL.
    8. Denisa Georgiana Banulescu & Ferrara Laurent & Marsilli Clément, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," Working Papers hal-03563168, HAL.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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