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

Forecasting prices of dairy commodities – a comparison of linear and nonlinear models

peer reviewedDairy commodity prices have become more volatile over the last 10–11 yr. The aim of this paper was to produce reliable price forecasts for the most frequently traded dairy commodities. Altogether five linear and nonlinear time series models were applied. The analysis reveals that prices of dairy commodities reached a structural breakpoint in 2006/2007. The results also show that a combination of linear and nonlinear models is useful in forecasting commodity prices. In this study, the price of cheese is the most difficult to forecast, but a simple autoregressive (AR) model performs reasonably well after 12 mo. Similarly, for butter the AR model performs the best, while for skimmed milk powder (Smp), whole milk powder (Wmp) and whey powder (Whp) the nonlinear methods are the most accurate. However, few of the differences between models are significant according to the Diebold–Mariano (DM) test. The findings could be of interest to the whole dairy industry

Modelling soybean prices in a changing policy environment

Accurate forecasts of commodity prices are an important ingredient in the policy formation process. A commodity price forecasting procedure used routinely by the US Department of Agriculture in their policy and market analysis activities is a simple, linear, reduced-form regression model that predicts season-average farm prices (SAFP) using policy variables and the ratio of total ending stocks to use. This approach is extended to the soybean SAFP to estimate a benchmark model using annual data. Also several specification issues related to this estimation framework are addressed. Evaluation suggests that the standard forecasting procedure may be affected by the fact that the ratio of stocks to use is endogenous to prices. In addition, important structural changes are revealed in these relationships over time. A model is then considered that allows parameters to shift gradually. Improvements in the accuracy of model forecasts allowed by this parameter switching technique are identified and discussed. In addition, the exact nature of the structural shifts is evaluated using dynamic impulse response functions.