The purpose of my research is to contribute to the field of forecasting from a
methodological perspective as well as to the field of crude oil as an application area to
test the performance of my methodological contributions and assess their merits. In sum,
two main methodological contributions are presented.
The first contribution consists of proposing a mathematical programming based
approach, commonly referred to as Data Envelopment Analysis (DEA), as a
multidimensional framework for relative performance evaluation of competing
forecasting models or methods. As opposed to other performance measurement and
evaluation frameworks, DEA allows one to identify the weaknesses of each model, as
compared to the best one(s), and suggests ways to improve their overall performance.
DEA is a generic framework and as such its implementation for a specific relative
performance evaluation exercise requires a number of decisions to be made such as the
choice of the units to be assessed, the choice of the relevant inputs and outputs to be
used, and the choice of the appropriate models. In order to present and discuss how one
might adapt this framework to measure and evaluate the relative performance of
competing forecasting models, we first survey and classify the literature on performance
criteria and their measures – including statistical tests – commonly used in evaluating
and selecting forecasting models or methods. In sum, our classification will serve as a
basis for the operationalisation of DEA. Finally, we test DEA performance in evaluating
and selecting models to forecast crude oil prices. The second contribution consists of
proposing a Multi-Criteria Decision Analysis (MCDA) based approach as a
multidimensional framework for relative performance evaluation of the competing
forecasting models or methods. In order to present and discuss how one might adapt
such framework, we first revisit MCDA methodology, propose a revised methodological
framework that consists of a sequential decision making process with feedback
adjustment mechanisms, and provide guidelines as to how to operationalise it. Finally,
we adapt such a methodological framework to address the problem of performance evaluation of competing forecasting models. For illustration purposes, we have chosen
the forecasting of crude oil prices as an application area
