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Commodity Prices Revisited

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  • Tomek, William G.

Abstract

Empirical models of commodity prices are potentially important aids to decision-makers, especially as the economy has grown more complex. A typical time series of commodity prices exhibits positive autocorrelation, occasional spikes, and random variability, and conceptual models have been developed to explain this behavior. But, the leap from theory to empirical applications is large because of model specification and data quality problems. When modeling price expectations, for example, should a price series be deflated and if so, by what deflator? The choice can have a large effect on empirical results. Nonetheless, it is possible in some applications to obtain relatively stable estimates of structural parameters that are useful for addressing specific problems. This may not happen often, however, because the incentives in academia do not encourage rigorous, in-depth appraisals of empirical results.

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  • Tomek, William G., 2000. "Commodity Prices Revisited," Agricultural and Resource Economics Review, Cambridge University Press, vol. 29(2), pages 125-137, October.
    • Handle: RePEc:cup:agrerw:v:29:y:2000:i:02:p:125-137_00
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    Cited by:

    1. Power, Gabriel J. & Eaves, James & Turvey, Calum & Vedenov, Dmitry, 2017. "Catching the curl: Wavelet thresholding improves forward curve modelling," Economic Modelling, Elsevier, vol. 64(C), pages 312-321.
    2. Tauer, Loren W., 2006. "When to Get In and Out of Dairy Farming: A Real Option Analysis," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 35(2), pages 1-9, October.
    3. Sophie Mitra & Jean‐Marc Boussard, 2012. "A simple model of endogenous agricultural commodity price fluctuations with storage," Agricultural Economics, International Association of Agricultural Economists, vol. 43(1), pages 1-15, January.
    4. Manuel Landajo & Mar'ia Jos'e Presno, 2024. "The prices of renewable commodities: A robust stationarity analysis," Papers 2402.01005, arXiv.org.
    5. David Ubilava, 2018. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(1), pages 239-263.
    6. Chaudhry, Muhammad Imran & Katchova, Ani & Miranda, Mario Javier, 2016. "Examining pricing mechanics in the poultry value chain - empirical evidence from Pakistan," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235953, Agricultural and Applied Economics Association.
    7. Eivind Hestvik Brækkan & Øystein Myrland, 2021. "“All along the curves”: Bridging the gap between comparative statics and simultaneous econometric models," Empirical Economics, Springer, vol. 60(3), pages 1559-1573, March.
    8. Nazlioglu, Saban & Karul, Cagin, 2017. "A panel stationarity test with gradual structural shifts: Re-investigate the international commodity price shocks," Economic Modelling, Elsevier, vol. 61(C), pages 181-192.
    9. Choudhry, Taufiq, 2009. "Short-run deviations and time-varying hedge ratios: Evidence from agricultural futures markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 58-65, March.
    10. Elham Rahmani & Mohammad Khatami & Emma Stephens, 2024. "Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada," Sustainability, MDPI, vol. 16(5), pages 1-19, February.

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