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Commodity prices, commodity currencies, and global economic developments

  • Jan J. J. Groen
  • Paolo A. Pesenti

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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File URL: http://www.nber.org/papers/w15743.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15743.

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Date of creation: Feb 2010
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Publication status: published as Commodity Prices, Commodity Currencies, and Global Economic Developments , Jan J. J. Groen, Paolo A. Pesenti. in Commodity Prices and Markets, East Asia Seminar on Economics, Volume 20 , Ito and Rose. 2011
Handle: RePEc:nbr:nberwo:15743
Note: IFM
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  1. Yu-chin Chen & Kenneth Rogoff & Barbara Rossi, 2008. "Can Exchange Rates Forecast Commodity Prices?," Working Papers UWEC-2008-11-FC, University of Washington, Department of Economics, revised Oct 2009.
  2. Reinhart, Carmen, 1988. "Real Exchange Rate and Commodity Prices in a Neoclassical Model," MPRA Paper 13188, University Library of Munich, Germany.
  3. Aasim M. Husain & Chakriya Bowman, 2004. "Forecasting Commodity Prices; Futures Versus Judgment," IMF Working Papers 04/41, International Monetary Fund.
  4. Selim Elekdag & Rene Lalonde & Douglas Laxton & Dirk Muir & Paolo Pesenti, 2008. "Oil Price Movements and the Global Economy: A Model-Based Assessment," NBER Working Papers 13792, National Bureau of Economic Research, Inc.
  5. Eduardo Borensztein & Carmen Reinhart, 1994. "The Macroeconomic Determinants of Commodity Prices," IMF Working Papers 94/9, International Monetary Fund.
  6. Q. Farooq Akram, 2008. "Commodity prices, interest rates and the dollar," Working Paper 2008/12, Norges Bank.
  7. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
  8. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  9. Kilian, Lutz, 2006. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," CEPR Discussion Papers 5994, C.E.P.R. Discussion Papers.
  10. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
  11. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
  12. Margaret E. Slade & Henry Thille, 2006. "Commodity Spot Prices: An Exploratory Assessment of Market Structure and Forward-Trading Effects," Economica, London School of Economics and Political Science, vol. 73(290), pages 229-256, 05.
  13. Jan J. J. Groen & George Kapetanios, 2009. "Model selection criteria for factor-augmented regressions," Staff Reports 363, Federal Reserve Bank of New York.
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