TY  - JOUR
AU  - Groen, Jan J. J
AU  - Pesenti, Paolo A
TI  - Commodity prices, commodity currencies, and global economic developments
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 15743
PY  - 2010
Y2  - February 2010
DO  - 10.3386/w15743
UR  - http://www.nber.org/papers/w15743
L1  - http://www.nber.org/papers/w15743.pdf
N1  - Author contact info:
Jan Groen
Federal Reserve Bank of New York
E-Mail: jan.groen@ny.frb.org

Paolo A. Pesenti
Federal Reserve Bank of New York
33 Liberty Street
New York, NY 10045
Tel: 212/720-5493
Fax: 212/720-6831
E-Mail: paolo.pesenti@ny.frb.org
M1  - published as Jan J. J. Groen, Paolo A. Pesenti. "Commodity Prices, Commodity Currencies, and Global Economic Developments," in Takatoshi Ito and Andrew K. Rose, editors, "Commodity Prices and Markets" University of Chicago Press (2011)

M3  - presented at "East Asian Seminar on Economics", June 26-27, 2009
AB  - In this paper we seek to produce forecasts of commodity price movements that can systematically improve on naive statistical benchmarks, and revisit the forecasting performance of changes in commodity currencies as efficient predictors of commodity prices, a view emphasized in the recent literature. In addition, we consider different types of factor-augmented models that use information from a large data set containing a variety of indicators of supply and demand conditions across major developed and developing countries. These factor-augmented models use either standard principal components or partial least squares (PLS) regression to extract dynamic factors from the data set. Our forecasting analysis considers ten alternative indices and sub-indices of spot prices for three different commodity classes across different periods. We find that the exchange rate-based model and especially the PLS factor-augmented model are more prone to outperform the naive statistical benchmarks. However, across our range of commodity price indices we are not able to generate out-of-sample forecasts that, on average, are systematically more accurate than predictions based on a random walk or autoregressive specifications.
ER  -