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Commodity prices, commodity currencies, and global economic developments

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

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://ec.europa.eu/economy_finance/publications/economic_paper/2011/ecp440_en.htm
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Paper provided by Directorate General Economic and Financial Affairs (DG ECFIN), European Commission in its series European Economy - Economic Papers with number 440.

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Length: 60 pages
Date of creation: Mar 2011
Date of revision:
Handle: RePEc:euf:ecopap:0440
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  1. 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.
  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. Aasim M. Husain & Chakriya Bowman, 2004. "Forecasting Commodity Prices; Futures Versus Judgment," IMF Working Papers 04/41, International Monetary Fund.
  4. Akram, Q. Farooq, 2009. "Commodity prices, interest rates and the dollar," Energy Economics, Elsevier, vol. 31(6), pages 838-851, November.
  5. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  6. Yu-Chin Chen & Kenneth Rogoff & Barbara Rossi, 2008. "Can Exchange Rates Forecast Commodity Prices?," NBER Working Papers 13901, National Bureau of Economic Research, Inc.
  7. 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.
  8. Reinhart, Carmen & Borensztein, Eduardo, 1994. "The Macroeconomic Determinants of Commodity Prices," MPRA Paper 6979, University Library of Munich, Germany.
  9. Selim Elekdag & René Lalonde & Douglas Laxton & Dirk Muir & Paolo Pesenti, 2007. "Oil Price Movements and the Global Economy: A Model-Based Assessment," Working Papers 07-34, Bank of Canada.
  10. Jan J. J. Groen & George Kapetanios, 2009. "Model selection criteria for factor-augmented regressions," Staff Reports 363, Federal Reserve Bank of New York.
  11. 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.
  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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