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

35 Pages Posted: 10 Mar 2011 Last revised: 1 May 2011

Yu-Chin Chen

University of Washington - Department of Economics

Wen-Jen Tsay

Academia Sinica - Institute of Economics

Date Written: April 29, 2011

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 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.

Keywords: Mixed Frequency Data, Autoregressive Distributed Lag, Commodity Prices, Forecasting

JEL Classification: C22, C53, F31, F47, Q02

Suggested Citation

Chen, Yu-Chin and Tsay, Wen-Jen, Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach (April 29, 2011). Available at SSRN: https://ssrn.com/abstract=1782214 or http://dx.doi.org/10.2139/ssrn.1782214

Yu-Chin Chen (Contact Author)

University of Washington - Department of Economics ( email )

Box 353330
Seattle, WA 98195-3330
United States
206-543-6197 (Phone)

HOME PAGE: http://faculty.washington.edu/yuchin

Wen-Jen Tsay

Academia Sinica - Institute of Economics ( email )

128 Academia Road, Section 2
Nankang
Taipei, 11529
Taiwan

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