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

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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 mar- ket movements - can produce forecasts of quarterly commodity price changes that are superior to those in the previous research. Following the traditional ADL lit- erature, our estimation strategy relies on a Vandermonde matrix to parameterize the weighting functions for higher-frequency observations. Accordingly, infer- ences can be obtained using ordinary least squares principles without Kalman fi ltering, non-linear optimizations, or additional restrictions on the parameters. Our fi ndings 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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Bibliographic Info

Paper provided by Institute of Economics, Academia Sinica, Taipei, Taiwan in its series IEAS Working Paper : academic research with number 11-A001.

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Length: 34 pages
Date of creation: Mar 2011
Date of revision: May 2011
Handle: RePEc:sin:wpaper:11-a001

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Keywords: Mixed frequency data; autoregressive distributed lag; commodity prices; forecasting;

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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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Cited by:
  1. 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 1250004-1-1.

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