Trends in real commodity prices: How real is real?
Abstract
Testing for the existence of downward trends in real commodity prices has been the focus of several studies since the Prebisch–Singer hypothesis was formulated back in 1950. In this article, we focus on annual and monthly series of various commodity categories and consider alternative price deflators. Based on the methodology of Harvey et al. (2010), which is robust to the order of integration of the time series, we conclude that the time frequency and the price deflators play a key role when testing for the Prebisch–Singer hypothesis. For instance, at an annual frequency (1900–2003, 1900–2008), it becomes considerably more likely to support it when deflating by the unadjusted US CPI-all items than when deflating by the Manufactures Unit Value (MUV) Index or the Historical Price Index of Manufactures (HPIM). This finding is in agreement with the Svedberg and Tilton (2006) discussion on the CPI's overestimation of inflation and the measurement of the real price of copper. When dealing with monthly data (January 1957–December 2010), our results show that real prices tend not to reject the null hypothesis of a trendless series, except when deflating by the PPI-Crude Materials and, to a lesser extent, by the HPIM.Download Info
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Bibliographic Info
Article provided by Elsevier in its journal Resources Policy.
Volume (Year): 37 (2012)
Issue (Month): 1 ()
Pages: 30-47
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Web page: http://www.elsevier.com/locate/inca/30467
Related research
Keywords: Prebisch–Singer hypothesis; Real commodity prices;Find related papers by JEL classification:
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
- O13 - Economic Development, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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