Advertisement

Empirical Economics

, Volume 43, Issue 2, pp 651–670 | Cite as

Global commodity cycles and linkages: a FAVAR approach

  • Marco J. LombardiEmail author
  • Chiara Osbat
  • Bernd Schnatz
Article

Abstract

In this article, we examine linkages across non-energy commodity price developments by means of a factor-augmented VAR model (FAVAR). From a set of non-energy commodity price series, we extract two factors, which we identify as common trends in metals and food prices. These factors are included in a FAVAR model together with selected macroeconomic variables, which have been associated with developments in commodity prices. Impulse response functions confirm that exchange rates and economic activity affect individual non-energy commodity prices, but we fail to find strong spillovers from oil to non-oil commodity prices or an impact of the interest rate. In addition, we find that individual commodity prices are affected by common trends captured by the food and metals factors.

Keywords

Oil price Commodity prices Exchange rates Globalisation FAVAR 

JEL Classification

E3 F3 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akram QF (2009) Commodity prices, interest rates and the dollar. Energy Econ 31: 838–851CrossRefGoogle Scholar
  2. Anzuini A., Pagano P, Pisani M (2007) Oil supply news in a VAR: information from financial markets. Banca d’Italia Temi di Discussione no. 632Google Scholar
  3. Anzuini A, Lombardi MJ, Pagano P (2010) The impact of monetary policy shocks on commodity prices. ECB working paper no. 1232Google Scholar
  4. Baffes J (2007) Oil spills on other commodities. Resour Policy 32: 126–134CrossRefGoogle Scholar
  5. Bai J, Ng S (2006) Confidence intervals for diffusion index forecasts with a large number of predictors and inference for factor-augmented regressions. Econometrica 74: 1133–1150CrossRefGoogle Scholar
  6. Banbura M, Giannone D, Reichlin L (2010) Large Bayesian vector autoregressions. J Appl Econom 25: 71–79CrossRefGoogle Scholar
  7. Banerjee A, Marcellino M (2008) Factor-augmented error correction models. CEPR discussion paper 6707Google Scholar
  8. Bernanke BS, Boivin J, Eliasz P (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. Q J Econ 120: 387–422Google Scholar
  9. Boschi M, Pieroni L (2009) Aluminium market and the macroeconomy. J Policy Model 31: 189–207CrossRefGoogle Scholar
  10. Breitenfellner A, Crespo Cuaresma J (2008) Crude oil prices and the euro-dollar exchange rate: a forecasting exercise. University of Innsbruck working papers in economy and statistics 2008-08Google Scholar
  11. Caballero R, Fahri E, Gourinchas P-O (2008) Financial crash, commodity prices and global imbalances. Brook Pap Econ Act 2: 1–55Google Scholar
  12. Carriero A, Kapetanios G, Marcellino M (2011) Forecasting large datasets with Bayesian reduced rank multivariate models. J Appl Econ 26: 735–761CrossRefGoogle Scholar
  13. Chaudhuri K (2001) Long-run prices of primary commodities and oil prices. Appl Econ 33: 531–538CrossRefGoogle Scholar
  14. De Mol C, Giannone D, Reichlin L (2008) Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?. J Econom 146: 318–328CrossRefGoogle Scholar
  15. Frankel JA (2008) The effect of monetary policy on real commodity prices. In: Campbell JY Asset prices and monetary policy. NBER Books, Cambridge, pp 291–333Google Scholar
  16. International Monetary Fund (2006) The boom in nonfuel commodity prices—can it last? In: World economic outlook, September 2006. IMF, Washington DC, pp 139–169Google Scholar
  17. Johansen S (2000) A Bartlett correction factor for tests on the cointegration relations. Econom Theory 16: 740–778CrossRefGoogle Scholar
  18. Kilian L (1998) Small-sample confidence intervals for impulse response functions. Rev Econ Stat 80: 218–230CrossRefGoogle Scholar
  19. Kilian L (2008) Exogenous oil supply shocks: how big are they and how much do they matter for the U.S. economy?. Rev Econ Stat 90: 216–240CrossRefGoogle Scholar
  20. Kilian L (2009) Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am Econ Rev 99: 1053–1069CrossRefGoogle Scholar
  21. Kose A, Otrok C, Whiteman CH (2004) International business cycles: world, region, and country-specific factors. Am Econ Rev 93: 1216–1239CrossRefGoogle Scholar
  22. Labys WC (2006) Modeling and forecasting primary commodity prices. Ashgate Publishing, AldershotGoogle Scholar
  23. Masters MW (2008) Testimony before the committee on homeland security and governmental affairs. United States Senate, May 20Google Scholar
  24. Mitchell D (2008) A note on rising food prices. World Bank Policy Research working paper no. 4682Google Scholar
  25. Pindyck RS, Rotemberg JJ (1990) The excess co-movement of commodity prices. Econ J 100: 1173–1189CrossRefGoogle Scholar
  26. Redrado M, Carrera J, Bastourre D, Ibarlucia J (2009) Financialization of commodity markets: non-linear consequences form heterogeneous agents behavior. Banco Central de la Republica Argentina working paper no. 2009|44Google Scholar
  27. Reitz S, Slopek U (2009) Non-linear oil price dynamics—a tale of heterogeneous speculators?. Ger Econ Rev 10: 270–283CrossRefGoogle Scholar
  28. Stock J, Watson M (2002) Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat 20: 147–162CrossRefGoogle Scholar
  29. Vansteenkiste I (2008) How important are common factors in driving non-fuel commodity prices? A dynamic factor analysis. ECB working paper no. 1072Google Scholar
  30. World Bank (2008) Global economic prospects, January 2008. World Bank, Washington DCCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Marco J. Lombardi
    • 1
    Email author
  • Chiara Osbat
    • 1
  • Bernd Schnatz
    • 1
  1. 1.European Central BankFrankfurt am MainGermany

Personalised recommendations