Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach
Yu-chin Chen () and
Wen-Jen Tsay ()
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Yu-chin Chen: University of Washington
No 11-A001, IEAS Working Paper : academic research from Institute of Economics, Academia Sinica, Taipei, Taiwan
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.
Keywords: Mixed frequency data; autoregressive distributed lag; commodity prices; forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 F31 F47 Q02 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2011-03, Revised 2011-05
New Economics Papers: this item is included in nep-agr, nep-ecm, nep-for and nep-mst
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Working Paper: Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach
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