Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach
2011/08/26
研討會日期 : 2011-08-26
時間 : 14:30
主講人 : Professor Yu-Chin Chen
地點 : Conference Room B110
演講者簡介 : Yu-Chin Chen received her Ph.D. in Economics from Harvard University in 2002. She is currently serving as Assistant Professor of Economics at University of Washington. Her research field is in international finance, open economy macroeconomics, and international trade.
演講摘要 : 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.
