Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios

What color are commodity prices? A fractal analysis

Commodity price behavior holds much interest not only because these markets are affected by waves of speculative activity similar to security markets but more so that these commodities are linked to industries which purchase them and developing country producers which supply them. Commodity spot and future prices have thus been studied extensively. This research extends this work by employing recent fractal approaches to evaluate how the apparent random movements associated with short term behavior can also persist when examining long run behavior. We thus test for the presence of a persistent and finite variance component (i.e. long memory stationary process) as opposed to an infinite variance component (i.e. short memory nonstationary process) in a selected group of international commodity price series. Both fractal and persistent dependence hypotheses and test statistics have been employed. Estimates made of the power law exponent and of the nonintegral or fractional exponent suggest generating processes which are closer to black noise than to white, pink or brown noise.Fractal Analysis · Long-Memory and Persistence · Commodity Price Colors