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Commodity Price Uncertainty in Developing Countries

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  • Jan Dehn
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    Abstract

    Commodity export price uncertainty is typically measured as the standard deviation of the terms of trade, but this approach encounters at least three objections. First, terms of trade indices are unsuitable as proxies for commodity price movements per se. Secondly, the shortness of terms of trade time series makes them inappropriate as a basis for constructing time varying uncertainty measures. Thirdly, simple standard deviation measures ignore the distinction between predictable and unpredictable elements in the price process, and therefore risk overstating uncertainty. The paper examines the features of commodity price uncertainty in developing countries using a new data set of unique quarterly aggregate commodity price indices for 113 developing countries over the period 1957Q1-1997Q4. A total of six different uncertainty measures are constructed, which confirm the importance of distinguishing between predictable and unpredictable components in the price process when measuring uncertainty. A a positive and highly significant relationship between commodity export concentration and commodity price uncertainty is found for all the measures. No obvious link is found between a country’s regional affiliation and its exposure to uncertainty. Similarly, there is no apparent relationship between a country’s experience of uncertainty and the type of commodities which dominates its exports. The exception is oil producers, which face greater uncertainty. The greater uncertainty faced by these countries can, however, be attributed almost exclusively to discrete and well publicised discrete oil shocks. A GARCH based measure of uncertainty indicates considerable time variation in uncertainty. Uncertainty is sometimes characterised by discrete spikes, while uncertainty in countries exhibits a secular increase in uncertainty over time. The majority of countries have seen uncertainty which exhibits considerable persistence. It is not clear what lies behind the time variation in uncertainty, which cannot be explained with reference to relatively time invarying export concentration.

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    Bibliographic Info

    Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number WPS/2000-12.

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    Date of creation: 01 May 2000
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    Handle: RePEc:oxf:wpaper:wps/2000-12

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    1. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, April.
    2. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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    Cited by:
    1. Collier, Paul & Goderis, Benedikt, 2009. "Structural policies for shock-prone developing countries," MPRA Paper 17311, University Library of Munich, Germany.
    2. Paul Collier & Benedikt Goderis, 2007. "Does aid mitigate external shocks?," CSAE Working Paper Series 2007-18, Centre for the Study of African Economies, University of Oxford.
    3. Collier, Paul & Goderis, Benedikt, 2008. "Commodity Prices, Growth, and the Natural Resource Curse: Reconciling a Conundrum," MPRA Paper 17315, University Library of Munich, Germany.
    4. Burke, Paul J. & Leigh, Andrew, 2010. "Do Output Contractions Trigger Democratic Change?," IZA Discussion Papers 4808, Institute for the Study of Labor (IZA).
    5. Joël CARIOLLE, 2012. "Measuring macroeconomic volatility - Applications to export revenue data, 1970-2005," Working Papers I14, FERDI.
    6. Joël CARIOLLE, 2012. "Mesurer l’instabilité macroéconomique - Applications aux données de recettes d’exportation, 1970-2005," Working Papers I14, FERDI.

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