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Commodity prices, commodity currencies, and global economic developments

  • Groen, Jan J. J.
  • Pesenti, Paolo

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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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 7689.

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Date of creation: Feb 2010
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Handle: RePEc:cpr:ceprdp:7689
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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. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
  3. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  4. 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.
  5. 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.
  6. Elekdag, Selim & Lalonde, Rene & Laxton, Doug & Muir, Dirk & Pesenti, Paolo, 2008. "Oil Price Movements and the Global Economy: A Model-Based Assessment," CEPR Discussion Papers 6700, C.E.P.R. Discussion Papers.
  7. Aasim M. Husain & Chakriya Bowman, 2004. "Forecasting Commodity Prices; Futures Versus Judgment," IMF Working Papers 04/41, International Monetary Fund.
  8. Jan J. J. Groen & George Kapetanios, 2009. "Model selection criteria for factor-augmented regressions," Staff Reports 363, Federal Reserve Bank of New York.
  9. Akram, Q. Farooq, 2009. "Commodity prices, interest rates and the dollar," Energy Economics, Elsevier, vol. 31(6), pages 838-851, November.
  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. Reinhart, Carmen, 1988. "Real Exchange Rate and Commodity Prices in a Neoclassical Model," MPRA Paper 13188, University Library of Munich, Germany.
  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. Eduardo Borensztein & Carmen M. Reinhart, 1994. "The Macroeconomic Determinants of Commodity Prices," IMF Staff Papers, Palgrave Macmillan, vol. 41(2), pages 236-261, June.
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