Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach
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.
|Date of creation:||Mar 2011|
|Date of revision:||May 2011|
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- 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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