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 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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