Random matrix theory and the failure of macro-economic forecasts

By scientific standards, the accuracy of short-term economic forecasts has been poor, and shows no sign of improving over time. We form a delay matrix of time-series data on the overall rate of growth of the economy, with lags spanning the period over which any regularity of behaviour is postulated by economists to exist. We use methods of random matrix theory to analyse the correlation matrix of the delay matrix. This is done for annual data from 1871 to 1994 for 17 economies, and for post-war quarterly data for the US and the UK. The properties of the eigenvalues and eigenvectors of these correlation matrices are similar, though not identical, to those implied by random matrix theory. This suggests that the genuine information content in economic growth data is low, and so forecasting failure arises from inherent properties of the data.     

Eigen analysis of the stability and degree of information content in correlation matrices constructed from property time series data

Property is an asset which forms part of the portfolios of many investors, particularly institutional ones, along with equities and bonds. Techniques from physics, particularly that of random matrix theory, have provided powerful insights into the behaviour of financial assets. A large database providing time series data for over 10,000 individual properties is available for the UK. Some of the data is available at an annual and some at a monthly frequency. However, even at the monthly frequency, only a relatively small number of observations is available, certainly in comparison with that available with financial assets. A key issue in translating methods of analysis in financial markets to property data is whether they are applicable given the small number of data points available. This paper addresses this issue. Using the tools of random matrix theory, we find that a great deal of information is contained within property data. The correlations between different types and geographical locations of property tend to have far more true information and be more stable over time than is the case with financial data, despite the large number of observations available with the latter. Received 31 December 2001     

A model for the packing of irregularly shaped grains

In this paper we will attempt to address the problem of the packing properties of granular materials composed of irregularly shaped grains (using configurational statistical mechanics). In particular, we will develop a model for a system of irregular grains based upon perturbing a packing of mono- or poly-disperse spheres. In the mono-disperse case we will show that the system packs less densely than a packing of perfect spheres, except when local correlations between configurations of grains are taken into account. The opposite is found to be true for a perturbation expansion based upon poly-disperse spheres. Finally we will show that for a bi-disperse packing of spheres phase segregation occurs for any size ratio and discuss whether this is to be expected.     

The statistical mechanics of granular systems composed of elongated grains

In this paper we will suggest a model for the packing properties and phase behaviour of a granular material whose constituents are elongated in nature, using the concepts of configurational statistical mechanics. We will show that, depending upon the shape of the grains, the systems need not necessarily undergo a discontinuous first-order phase transition (even at minimum close packing). We will also briefly discuss the relationship between this model and more conventional models, such as Onsager's hard rod model.     

The statistical mechanics of granular systems composed of spheres and elongated grains

In this paper we will apply a statistical mechanical theory of granular materials to a system composed of a mixture of elongated rods and spheres (both mono- and polydisperse). We will show for a system of rods and spheres that the system's constituents do not completely phase segregate even at minimum close packing. The generalisation of this to packings of rods and bi-disperse spheres produces a similar result except that the two species of sphere do phase segregate at some particular packing.     

Creatine kinase B deficient neurons exhibit an increased fraction of motile mitochondria

Background  

Neurons require an elaborate system of intracellular transport to distribute cargo throughout axonal and dendritic projections. Active anterograde and retrograde transport of mitochondria serves in local energy distribution, but at the same time also requires input of ATP. Here we studied whether brain-type creatine kinase (CK-B), a key enzyme for high-energy phosphoryl transfer between ATP and CrP in brain, has an intermediary role in the reciprocal coordination between mitochondrial motility and energy distribution. Therefore, we analysed the impact of brain-type creatine kinase (CK-B) deficiency on transport activity and velocity of mitochondria in primary murine neurons and made a comparison to the fate of amyloid precursor protein (APP) cargo in these cells, using live cell imaging.