Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach
University of Washington - Department of Economics
Academia Sinica - Institute of Economics
April 29, 2011
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 research. 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 using ordinary least squares principles without Kalman filtering, non-linear optimizations, or additional restrictions on the parameters. Our findings provide an easy-to-use method for conducting mixed data-sampling analysis as well as for forecasting world commodity price movements.
Number of Pages in PDF File: 35
Keywords: Mixed Frequency Data, Autoregressive Distributed Lag, Commodity Prices, Forecasting
JEL Classification: C22, C53, F31, F47, Q02
Date posted: March 10, 2011 ; Last revised: May 1, 2011
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