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The informational role of commodity prices in formulating monetary policy: a reexamination under the frequency domain

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  • Yanfeng Wei

Abstract

This paper employs the frequency domain causality test proposed by Breitung and Candelon (J Econom 132:363–378, 2006 ) to investigate whether commodity prices are useful in formulating monetary policy. Based on the monthly U.S. data from January 1957 to December 2011, we find the frequency domain causal relationship between non-oil commodity prices and economic activities (e.g., consumer prices and industrial production) has changed dramatically over time. Our results indicate that the non-oil commodity prices are useful in setting monetary policy in the 1970s and the beginning of 1980s, but the usefulness has disappeared completely since the early 1980s. In contrast, the oil price or the commodity price index including oil price can still be used as an informational variable for managing monetary policy after the early 1980s. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Yanfeng Wei, 2015. "The informational role of commodity prices in formulating monetary policy: a reexamination under the frequency domain," Empirical Economics, Springer, vol. 49(2), pages 537-549, September.
    • Handle: RePEc:spr:empeco:v:49:y:2015:i:2:p:537-549
    DOI: 10.1007/s00181-014-0870-2
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    1. Assenmacher-Wesche, Katrin & Gerlach, Stefan & Sekine, Toshitaka, 2008. "Monetary factors and inflation in Japan," Journal of the Japanese and International Economies, Elsevier, vol. 22(3), pages 343-363, September.
    2. Marc Gronwald, 2009. "Reconsidering the macroeconomics of the oil price in Germany: testing for causality in the frequency domain," Empirical Economics, Springer, vol. 36(2), pages 441-453, May.
    3. Bodart, Vincent & Candelon, Bertrand, 2009. "Evidence of interdependence and contagion using a frequency domain framework," Emerging Markets Review, Elsevier, vol. 10(2), pages 140-150, June.
    4. Garner, C Alan, 1989. "Commodity Prices: Policy Target or Information Variable? A Note," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 21(4), pages 508-514, November.
    5. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    6. Bertrand Candelon & Gianluca Cubadda, 2006. "Testing for Parameter Stability in Dynamic Models across Frequencies," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 741-760, December.
    7. George Mavrotas & Roger Kelly, 2001. "Old Wine in New Bottles: Testing Causality between Savings and Growth," Manchester School, University of Manchester, vol. 69(s1), pages 97-105.
    8. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters, in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230, National Bureau of Economic Research, Inc.
    9. repec:bla:manchs:v:69:y:2001:i:0:p:97-105 is not listed on IDEAS
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    11. Cody, Brian J & Mills, Leonard O, 1991. "The Role of Commodity Prices in Formulating Monetary Policy," The Review of Economics and Statistics, MIT Press, vol. 73(2), pages 358-365, May.
    12. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
    13. Awokuse, Titus O. & Yang, Jian, 2003. "The informational role of commodity prices in formulating monetary policy: a reexamination," Economics Letters, Elsevier, vol. 79(2), pages 219-224, May.
    14. Verheyen, Florian, 2010. "Monetary Policy, Commodity Prices and Infl ation – Empirical Evidence from the US," Ruhr Economic Papers 216, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    15. Margaret M. McConnell & Gabriel Perez-Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    16. repec:zbw:rwirep:0216 is not listed on IDEAS
    17. Martin Bodenstein & Luca Guerrieri & Lutz Kilian, 2012. "Monetary Policy Responses to Oil Price Fluctuations," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(4), pages 470-504, December.
    18. Frederick T. Furlong & Robert Ingenito, 1996. "Commodity prices and inflation," Economic Review, Federal Reserve Bank of San Francisco, pages 27-47.
    19. Bhar, Ramaprasad & Hamori, Shigeyuki, 2008. "Information content of commodity futures prices for monetary policy," Economic Modelling, Elsevier, vol. 25(2), pages 274-283, March.
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    Cited by:

    1. Breitung, Jörg & Schreiber, Sven, 2018. "Assessing causality and delay within a frequency band," Econometrics and Statistics, Elsevier, vol. 6(C), pages 57-73.
    2. Ritabrata Bose & Ashima Goyal, 2020. "Disaggregated Indian industrial cycles: A Spectral analysis," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2020-033, Indira Gandhi Institute of Development Research, Mumbai, India.
    3. Wei, Yanfeng & Guo, Xiaoying, 2016. "An empirical analysis of the relationship between oil prices and the Chinese macro-economy," Energy Economics, Elsevier, vol. 56(C), pages 88-100.
    4. Matteo Farnè & Angela Montanari, 2022. "A Bootstrap Method to Test Granger-Causality in the Frequency Domain," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 935-966, March.
    5. Afees A. Salisu & Raymond Swaray & Hadiza Sa'id, 2021. "Improving forecasting accuracy of the Phillips curve in OECD countries: The role of commodity prices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2946-2975, April.
    6. Wei, Yanfeng & Guo, Xiaoying, 2017. "Oil price shocks and China's stock market," Energy, Elsevier, vol. 140(P1), pages 185-197.
    7. Alfredo Villca & Alejandro Torres-García, 2023. "Commodity price shocks and the business cycles in emerging economies: the role of banking system balance sheets," Empirical Economics, Springer, vol. 65(5), pages 2039-2063, November.
    8. Wei, Yanfeng & Zhang, Liguo & Guo, Xiaoying & Yang, Ting, 2021. "A theoretical and simulation analysis on the power of the frequency domain causality test," Statistics & Probability Letters, Elsevier, vol. 170(C).

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    More about this item

    Keywords

    Commodity prices; Predictive power; Frequency domain causality; C32; E31;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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