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Commodity Price Booms and Breaks: Detection, Magnitude and Implications for Developing Countries

Author

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  • Rodrigo Mariscal
  • Andrew Powell

    ()

Abstract

There has been much interest of late regarding the current commodity “super cycle”. However, even sizing the current boom implies knowledge of long-run trends that are notoriously difficult to estimate. This paper uses new techniques to identify breaks in commodity prices and estimate trends and cointegrating relationships and argues that the weight of evidence is against a stable declining commodity terms of trade. The results are used to characterize the current boom and, assuming no new break, how commodity prices would be expected to return to the estimated “equilibrium”. The paper also discusses implications for commodity-dependent developing countries.

Suggested Citation

  • Rodrigo Mariscal & Andrew Powell, 2014. "Commodity Price Booms and Breaks: Detection, Magnitude and Implications for Developing Countries," Research Department Publications IDB-WP-444, Inter-American Development Bank, Research Department.
  • Handle: RePEc:idb:wpaper:idb-wp-444
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    Citations

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    Cited by:

    1. Winkelried, Diego, 2018. "Unit roots, flexible trends, and the Prebisch-Singer hypothesis," Journal of Development Economics, Elsevier, vol. 132(C), pages 1-17.
    2. Hildegart Ahumada y Magdalena Cornejo, 2015. "Long-run effects of commodity prices on the real exchange rate: evidence from Argentina," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 61, pages 3-33, January-D.
    3. Fernández, Andrés & González, Andrés & Rodríguez, Diego, 2018. "Sharing a ride on the commodities roller coaster: Common factors in business cycles of emerging economies," Journal of International Economics, Elsevier, vol. 111(C), pages 99-121.
    4. Di Iorio, Francesca & Fachin, Stefano, 2018. "The Prebish–Singer hypothesis in the post-colonial era: Evidence from panel cointegration," Economics Letters, Elsevier, vol. 166(C), pages 86-89.
    5. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    6. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-25, April.
    7. Bacha, C. & Carvalho, L., 2018. "The Evolution of Brazilian Production and Exportation of Agro-based Products from 1990 thru 2013," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277740, International Association of Agricultural Economists.

    More about this item

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q33 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Resource Booms (Dutch Disease)

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