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

  • Yu-chin Chen

    (University of Washington)

  • Wen-Jen Tsay

    (Institute of Economics, Academia Sinica)

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.

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Paper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2011-06.

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Handle: RePEc:udb:wpaper:uwec-2011-06
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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, MIT Press, vol. 121(2), pages 587-633, May.
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