Abstract
In this paper, we address the classical problem of testing for stationarity in the prices of energy-related commodities. A panel of fourteen time series of monthly prices is analyzed for the 1980–2020 period. Nine of the series are classical nonrenewable, GHG-emissions-intensive resources (coal, crude oil, natural gas), whereas the remaining, low-emission group includes both uranium and four commodities employed in biofuels (rapeseed, palm, and soybean oils, and ethanol). A nonparametric, bootstrap-based stationarity testing framework is employed. The main advantage of this procedure is its asymptotically model-free nature, being less sensitive than parametric tests to the risks of misspecification and detection of spurious unit roots, although it has the potential limitation of typically requiring larger samples than mainstream tools. Results suggest that most of the series analyzed may be trend stationary. The only exception would be crude oil, where different conclusions are obtained depending on whether a seasonal correction is applied or not.
Highlights
IntroductionIntroduction in the Prices of EnergyCommodities.Stationarity/unit-root analysis is a relevant topic in a great many fields, including climate change studies and commodities modeling
Introduction in the Prices of EnergyCommodities.Stationarity/unit-root analysis is a relevant topic in a great many fields, including climate change studies and commodities modeling
With a view to controlling for potentially adverse effects of the various kinds of filtering applied to the data, we report the results in three different settings, namely for (i) the nominal price series, (ii) the series deflated with the US Consumer Price Index (US CPI), and (iii) the series previously de-seasonalized
Summary
Introduction in the Prices of EnergyCommodities.Stationarity/unit-root analysis is a relevant topic in a great many fields, including climate change studies and commodities modeling. That distinction has practical relevance in many areas including assessment of the validity of theories, policy design and analysis (as integrated processes typically require more proactive policies to correct for the effect of shocks, these being less necessary in trend-stationary processes, where their natural inertia leads them to mean-revert), and econometric estimation and subsequent application of models to forecasting (where the optimal predictors clearly differ in stationary and integrated cases) and decision making. From the standpoint of the application of stationarity for the assessment of theories validity, a widely discussed framework is Hotelling’s model, which predicts that in a world of certainty, non-renewable resource prices would be trend stationary. The classical monograph by Slade [3] was one of the first attempts to evaluate Hotelling’s model through the analysis of the time series properties of natural resource prices. Subsequent papers (e.g., Presno et al [4], among many others) extended the analysis
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