Abstract
The mass and energy-capital conservation equations are employed to study the time evolution of mass and price of nonrenewable energy resources, extracted and sold to the market, in case of no-accumulation and no-depletion, that is, when the resources are extracted and sold to the market at the same mass flow rate. The Hotelling rule for nonrenewable resources, that is, an exponential increase of the price at the rate of the current interest multiplied the time, is shown to be a special case of the general energy-capital conservation equation when the mass flow rate of extracted resources is unity. The mass and energy-capital conservation equations are solved jointly to investigate the time evolution of the extracted resources.
Highlights
The price evolution of nonrenewable energy resources is a very important problem in an economy based on nonrenewable energy resources
During the 2010s, among the others, de Souza e Silva et al [94] investigated the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements, developing a forecasting methodology that consists of employing wavelet analysis to remove high frequency price movements, assumed as noise, and using the probability distribution of the price return accumulated over the days to infer future price trends
The results obtained in this study indicate that the proposed empirical mode decomposition (EMD)-slope-based method (SBM)-forward neural network (FNN) model, using the MIMO strategy, is the best in terms of prediction accuracy with accredited computational load
Summary
The price evolution of nonrenewable energy resources is a very important problem in an economy based on nonrenewable energy resources. During the 2010s, among the others, de Souza e Silva et al [94] investigated the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements, developing a forecasting methodology that consists of employing wavelet analysis to remove high frequency price movements, assumed as noise, and using the probability distribution of the price return accumulated over the days to infer future price trends He et al [95] presented two forecasting models, one based on a vector error correction mechanism and the other based on a transfer function framework with the range taken as a driver variable, for forecasting the daily highs and lows, showing that both of these models offer significant advantages over the naıve random walk and univariate ARIMA models in terms of out-of-sample forecast accuracy. The present theory has been confirmed in case of negative inflation rate [115], projecting the forecast to the following five months of 2013 [116], with a very recent verification [117]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
