Due to the increasing importance of natural gas to modern economic activity, and gas’s non-renewable nature, it is extremely important to try to estimate possible trajectories of future natural gas production while accounting for uncertainties in resource estimates, demand growth, production...

In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of...

Statistical models usually rely on the assumption that the shape of the distribution is fixed and that it is only the mean and volatility that varies. Although the fitting of heavy tail distributions has become easier due to computational advances, the fitting of the appropriate heavy tail...

In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of...

Statistical models usually rely on the assumption that the shape of the distribution is fixed and that it is only the mean and volatility that varies. Although the fitting of heavy tail distributions has become easier due to computational advances, the fitting of the appropriate heavy tail...

This paper utilises the GAMLSS framework for the statistical modelling of movie box-office revenues. The dominant modelling paradigm of the film industry, traditionally exemplified by the nobody knows principle is based upon the infinite variance of the Pareto distribution. Using GAMLSS we have...

Information on observable economic and financial variables is sometimes limited to summary form. Therefore, in many practical situations, it is desirable to restrict the flexibility of nonparametric density estimators to accommodate information about the summary data as well as any prior...

In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of...

This paper reports on concepts and methods to incorporate the Markov-Switching Multifractal model for stochastic volatility introduced by Calvet and Fisher (2004) within the GAMLSS model introduced by Rigby and Stasinopoulos (2005), allowing generalization to a non-normal distribution. The...

The ACEGES (Agent-based Computational Economics of the Global Energy System) 1.0 model is an agent-based model of conventional oil production for 93 countries. The model accounts for four key uncertainties, namely Estimated Ultimate Recovery (EUR), estimated growth in oil demand, estimated...