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Commodity prices, commodity currencies, and global economic developments

Author: Jan J J Groen; Paolo A Pesenti; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, ©2010.
Series: Working paper series (National Bureau of Economic Research), no. 15743.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jan J J Groen; Paolo A Pesenti; National Bureau of Economic Research.
OCLC Number: 526359772
Notes: Title from http://www.nber.org/papers/w15743 viewed Feb. 23, 2010.
"February 2010."
Description: 1 online resource (31 pages).
Series Title: Working paper series (National Bureau of Economic Research), no. 15743.
Responsibility: Jan J.J. Groen, Paolo A. Pesenti.

Abstract:

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.

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