This dissertation investigates various issues of interest regarding the workings and uses of commodity futures markets. Chapter II evaluates the relative performances of various estimators of bid-ask spreads in futures markets using commonly available transaction data. Results indicate a wide divergence in the performance of the competing estimators. This chapter also examines the effect of automating trading on spreads in commodity futures markets. Results indicate that spreads generally widened after trading was automated on the markets considered, and the tendency for spreads to widen during periods of high volatility increased. These results are in contrast to those found in higher volume financial futures markets. Chapter III investigates various unresolved issues regarding futures markets, using formal methods appropriate for inferring causal relationships from observational data when some relevant quantities are hidden. I find no evidence supporting the generalized version of Keynes's theory of normal backwardation. I find no evidence supporting theories that predict that the level of activity of speculators or uninformed traders affects the level of price volatility, either positively or negatively. My evidence strongly supports the mixture of distribution hypothesis (MDH) that trading volume and price volatility have one or more latent common causes, resulting in their positive correlation. Chapter IV examines partial equilibrium and statistical approaches to hedging. Different types of hedgers have traditionally used each of two approaches: derivatives dealers and market makers have typically used the former approach to hedge their portfolios, while commodity producers and consumers more commonly use the latter. This research provides the first known comparison of the out-of-sample hedging performance of the two approaches. Results indicate that for a simple derivative with a linear payoff function (a futures contract), the statistical models significantly outperform the partial equilibrium models considered here.

Causality analysis is an approach to time series analysis that is being used increasingly to investigate neuroimaging data. The reason for its popularity is the useful perspective it provides in describing the ordered operations of various brain regions using indirectly and passively measured neurophysiological signals. Although there are numerous frameworks with which causality analysis can be performed, one concept in particular – termed Granger causality (GC) – is receiving much of the attention because of its ease of implementation and interpretability. GC makes use of the fact that a predictive relationship between the history of one signal and the future of another signal provides evidence for there being a causal relationship between the two signals, and as a result, the physical events underlying those signals. If such a relationship can be established across neural time series, causal dependencies between neural pathways can be inferred and their contribution to brain function can be studied. Several analysis frameworks exist for applying GC to neurophysiological questions but many of these frameworks have deficiencies that impede their application to large and highly multivariate neuroimaging datasets. To address some of these concerns, this study develops the theory and methods for a novel neural time series classification procedure – referred to as GC classification – based on concepts in GC analysis. Validation of this method in neuroimaging research is provided by showing that it can be applied to heterogeneous datasets, that it makes use of many parallel sources of information about causal relationships, and that it can be adapted to different types of preprocessing steps to uncover causal relationships in multivariate neural time series data. Application of this analysis method to human behavioural MEG data revealed that, during a cued button-pressing task, distinct causal relationships exist between sensory cortices and their downstream targets preceding the initiation of actions that differ by whether or not they were the result of a decision being made. These results provide evidence that the GC classification procedure is a useful and robust technique for studying causal relationships in neurophysiological time series.

Causality analysis is an approach to time series analysis that is being used increasingly to investigate neuroimaging data. The reason for its popularity is the useful perspective it provides in describing the ordered operations of various brain regions using indirectly and passively measured neurophysiological signals. Although there are numerous frameworks with which causality analysis can be performed, one concept in particular – termed Granger causality (GC) – is receiving much of the attention because of its ease of implementation and interpretability. GC makes use of the fact that a predictive relationship between the history of one signal and the future of another signal provides evidence for there being a causal relationship between the two signals, and as a result, the physical events underlying those signals. If such a relationship can be established across neural time series, causal dependencies between neural pathways can be inferred and their contribution to brain function can be studied. Several analysis frameworks exist for applying GC to neurophysiological questions but many of these frameworks have deficiencies that impede their application to large and highly multivariate neuroimaging datasets. To address some of these concerns, this study develops the theory and methods for a novel neural time series classification procedure – referred to as GC classification – based on concepts in GC analysis. Validation of this method in neuroimaging research is provided by showing that it can be applied to heterogeneous datasets, that it makes use of many parallel sources of information about causal relationships, and that it can be adapted to different types of preprocessing steps to uncover causal relationships in multivariate neural time series data. Application of this analysis method to human behavioural MEG data revealed that, during a cued button-pressing task, distinct causal relationships exist between sensory cortices and their downstream targets preceding the initiation of actions that differ by whether or not they were the result of a decision being made. These results provide evidence that the GC classification procedure is a useful and robust technique for studying causal relationships in neurophysiological time series.

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Carta al editor. / Revisión por pares

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<p>The concept of matter is discussed by Aristotle in the context of investigations dealing with the issues of causality, substance, and change. The following inquiry focusses (sic) on the discussion of matter in the context of change by analysing the two accounts of change that Aristotle gives in the first book of the Physics and the ninth book of the Metaphysics, respectively. The two schemas of change are outlined and the development of the concept of matter is followed from the hypokeimenon of accidental change, to the primary matter of elemental change, to the matter that underlies substantial change and finally to the potential of the second model of change. </p> / Thesis / Master of Arts (MA)

Yes / This paper evaluates the importance of trade in goods when modelling demand for tourism. It is argued that the limited literature testing causality between trade in goods and tourism does not consider the appropriate variables. This study utilises bilateral data for 16 UK tourist destinations in order to test Granger causality between trade in goods and tourism expenditure. UK imports, exports and total trade are tested separately, whilst controlling for real GDP and real bilateral exchange rates. The novelty of this paper is the variable specification, as well as testing the causal relationship for the case of UK outgoing tourists. Our findings suggest a causal relationship between the tourism expenditure of UK residents and trade in goods. These results support the inclusion of a trade in goods variable when estimating tourism demand, as well as adopting appropriate methodologies to account for this causal relationship. Furthermore, there is strong evidence that the trade-tourism link is important for both the UK and host countries.

A program for advancing a new philosophical account of physical laws is presented. The program is non-reductive in that it maintains that any correct account of physical laws must recognize law sentences as irreducible--that is, as not admitting of an analysis which does not invoke any unanalyzed nomic facts (i.e. causal statements, law statements, subjunctive conditionals, etc.). The program has the unusual attraction of being consistent with Nominalism and epistemically in the spirit of Empiricism. Initially motivating my program is a two-stage attack in chapters two and three on all reductive accounts. The first stage of the attack is on traditional reductive accounts. Traditional reductive accounts are those accounts which do not invoke abstract entities in addressing nomic modality, i.e. in distinguishing universal laws from accidentally true generalizations or in explaining the relationship between statements of probability and statements of relative frequency. These accounts include those of Brian Skyrms, David Lewis, and Bas van Fraassen. The second stage of the attack is on all non-traditional reductive accounts. These accounts include David Armstrong's Nomic Realism. The two-stage attack exhausts the ontological ground for a reduction of laws. It is concluded that no reductive accounts of physical laws is possible. Chapter four spells out the details of my positive program. The program calls for (i) statement of the basic philosophical truths about nomic modality, (ii) the specification of axiomatic principles governing physical laws, and (iii) the analysis of nomic facts in terms of other nomic facts. The basic truths about nomic modality are stated in full. Foremost among these is the Irreducibility Thesis which states that law sentences are irreducible. Some examples of axiomatic principles governing physical laws are specified and one example of an analysis of nomic facts in terms of other nomic facts is given. The analysis is of general causal statements. Time is also spent in chapter four critically reviewing other accounts in the history of philosophy which have recognized law sentences as irreducible. The final chapter addresses the most common and the most significant objection to my positive program. That objection is the epistemological challenge of Empiricism. I argue that my program and, in particular, the Irreducibility Thesis are epistemologically innocuous.

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