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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. We revisit how well changes in commodity currencies perform as potential 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 the more novel 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, of all the approaches, the exchange-rate-based model and the PLS factor-augmented model are more likely to outperform the naive statistical benchmarks, although PLS factor-augmented models usually have a slight edge over the exchange-rate-based approach. 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 Federal Reserve Bank of New York in its series Staff Reports with number 387.

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Date of creation: 2009
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Handle: RePEc:fip:fednsr:387
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  1. Eduardo Borensztein & Carmen Reinhart, 1994. "The Macroeconomic Determinants of Commodity Prices," IMF Working Papers 94/9, International Monetary Fund.
  2. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
  3. Yu-Chin Chen & Kenneth Rogoff & Barbara Rossi, 2008. "Can Exchange Rates Forecast Commodity Prices?," NBER Working Papers 13901, National Bureau of Economic Research, Inc.
  4. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
  5. Akram, Q. Farooq, 2009. "Commodity prices, interest rates and the dollar," Energy Economics, Elsevier, vol. 31(6), pages 838-851, November.
  6. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  7. Jan J. J. Groen & George Kapetanios, 2009. "Model selection criteria for factor-augmented regressions," Staff Reports 363, Federal Reserve Bank of New York.
  8. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-69, June.
  9. Todd E. Clark & Kenneth D. West, 2004. "Using out-of-sample mean squared prediction errors to test the Martingale difference hypothesis," Research Working Paper RWP 04-03, Federal Reserve Bank of Kansas City.
  10. 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.
  11. Aasim M. Husain & Chakriya Bowman, 2004. "Forecasting Commodity Prices; Futures Versus Judgment," IMF Working Papers 04/41, International Monetary Fund.
  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. Reinhart, Carmen, 1988. "Real Exchange Rate and Commodity Prices in a Neoclassical Model," MPRA Paper 13188, University Library of Munich, Germany.
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