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DISCERNING TRENDS IN COMMODITY PRICES

Published online by Cambridge University Press:  17 July 2017

Dimitri Dimitropoulos and
Adonis Yatchew
Dimitri Dimitropoulos
Affiliation:
University of Toronto
Adonis Yatchew*
Affiliation:
University of Toronto
*
Address correspondence to: Adonis Yatchew, Economics Department, University of Toronto, Toronto M6S 3G7, Canada; e-mail: yatchew@chass.utoronto.ca.
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Abstract

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This paper emphasizes the use of data-driven techniques to discern trends in commodity prices. Whereas many previous papers rely on parametric assumptions, we take as our departure point the view that such trends are inherently nonparametric, driven by complex forces of innovation and obsolescence, competition and strategic behavior, resource depletion and discovery, and supply and demand. Our reference specification is the partial linear model y t = f(t) + z t β + ϵt where macroeconomic variables z enter parametrically, and f is nonparametric, to be discovered using cross-validation.

We analyze data on 11 commodities—3 hydrocarbons and 8 metals. For the majority of these commodities, our data-driven estimates of f bear close similarity to band pass estimates which include long term trends and super cycles. The OPEC effect is estimated to have increased oil prices by over 50% on average and coal prices by about 25%. U.S. coal mining legislation is estimated to have increased coal prices by 9% to 15%.

Keywords

Commodity PricesOil PricesSemiparametric ModelingTrendsSupercycles

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Special Issue 3: Dynamics of Oil and Commodities Prices , April 2018, pp. 683 - 701
Copyright
Copyright © Cambridge University Press 2017 

Footnotes

We are especially grateful to Mathieu Marcoux for assistance on multiple aspects of this project, and to Lutz Killian and Angelo Melino for helpful comments. The support of the Social Sciences and Humanities Research Council is gratefully acknowledged.

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