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Explaining commodity prices by a cointegrated time series-cross section model

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  • Hildegart Ahumada
  • Magdalena Cornejo

Abstract

This paper analyzes idiosyncratic and common determinants of commodity prices by adopting a time series-cross section approach. Different from the previous empirical literature, we allow for long-run and short-run effects, testing for cointegration and accounting for cross-dependence among different commodities. An automatic model selection algorithm is used to obtain a dominant congruent econometric model, selecting among many potential explanatory variables. As the effects of commodity prices’ determinants may vary over time and across commodities and we are estimating single conditional models, poolability and exogeneity issues are also evaluated. Our results indicate that commodity price formation in the long run is determined by supply and demand factors, apart from the US exchange rate. We find significant effects of individual commodity production and the demand-pull of China’s economy. The economic growth of both emerging and developed countries as well as the dollar’s depreciation, inventory changes, and easier monetary policies also have significant short-run effects on real commodity prices. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Hildegart Ahumada & Magdalena Cornejo, 2015. "Explaining commodity prices by a cointegrated time series-cross section model," Empirical Economics, Springer, vol. 48(4), pages 1667-1690, June.
    • Handle: RePEc:spr:empeco:v:48:y:2015:i:4:p:1667-1690
    DOI: 10.1007/s00181-014-0827-5
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    Cited by:

    1. Mathias Schneid Tessmann & Carlos Enrique Carrasco-Gutierrez & Alexandre Vasconcelos Lima, 2023. "Determinants of Corn and Soybean Futures Prices Traded on the Brazilian Stock Exchange: An ARDL Approach," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 15(1), pages 1-65, January.
    2. 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.
    3. Sima Siami‐Namini, 2021. "U.S. Monetary Policy and Commodity Prices: A SVECM Approach," Economic Papers, The Economic Society of Australia, vol. 40(4), pages 288-312, December.
    4. Fabian Lutzenberger & Benedikt Gleich & Herbert G. Mayer & Christian Stepanek & Andreas W. Rathgeber, 2017. "Metals: resources or financial assets? A multivariate cross-sectional analysis," Empirical Economics, Springer, vol. 53(3), pages 927-958, November.
    5. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
    6. Crespo-Cuaresma, Jesus & Fortin, Ines & Hlouskova, Jaroslava & Obersteiner, Michael, 2021. "Regime-dependent commodity price dynamics: A predictive analysis," IHS Working Paper Series 28, Institute for Advanced Studies.
    7. Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner, 2021. "Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1245-1273, November.

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    More about this item

    Keywords

    Commodity prices; Time series-cross section; Cointegration; Automatic selection; Poolability; Exogeneity; C33; Q11;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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