Optimal spectral bandwidth for long memory

For long range dependent time series with a spectral singularity at frequency zero, a theory for optimal bandwidth choice in non-parametric analysis ofthe singularity was developed by Robinson (1991b). The optimal bandwidths are described and compared with those in case of analysis of a smooth spectrum. They are also analysed in case of fractional ARIMA models and calculated as a function of the self similarity parameter in some special cases. Feasible data dependent approximations to the optimal bandwidth are discussed

New methods for the analysis of long memory time series: application to Spanish inflation

Models for long-memory time series are considered, in which the autocovariance sequence is only parameterized at very long lags, or the spectral density is only parametized at very low frequencies. Various recently proposed methods for estimating the differencing parameters are reviewed, and applied to an economic time series of prices in Spain

Comparison of Molecular Dynamics with Hybrid Continuum-Molecular Dynamics for a Single Tethered Polymer in a Solvent

We compare a newly developed hybrid simulation method which combines classical molecular dynamics (MD) and computational fluid dynamics (CFD) to a simulation consisting only of molecular dynamics. The hybrid code is composed of three regions: a classical MD region, a continuum domain where the dynamical equations are solved by standard CFD methods, and an overlap domain where transport information from the other two domains is exchanged. The exchange of information in the overlap region ensures that momentum, energy and mass are conserved. The validity of the hybrid code is demonstrated by studying a single polymer tethered to a hard wall immersed in explicit solvent and undergoing shear flow. In classical molecular dynamics simulation a great deal of computational time is devoted to simulating solvent molecules, although the solvent itself is of no direct interest. By contrast, the hybrid code simulates the polymer and surrounding solvent explicitly, whereas the solvent farther away from the polymer is modeled using a continuum description. In the hybrid simulations the MD domain is an open system whose number of particles is controlled to filter the perturbative density waves produced by the polymer motion.We compare conformational properties of the polymer in both simulations for various shear rates. In all cases polymer properties compare extremely well between the two simulation scenarios, thereby demonstrating that this hybrid method is a useful way to model a system with polymers and under nonzero flow conditions. There is also good agreement between the MD and hybrid schemes and experimental data on tethered DNA in flow. The computational cost of the hybrid protocol can be reduced to less than 6% of the cost of updating the MD forces, confirming the practical value of the method.Comment: 13 pages, 8 figure

Tractable nonlinear memory functions as a tool to capture and explain dynamical behaviours

Mathematical approaches from dynamical systems theory are used in a range of fields. This includes biology where they are used to describe processes such as protein-protein interaction and gene regulatory networks. As such networks increase in size and complexity, detailed dynamical models become cumbersome, making them difficult to explore and decipher. This necessitates the application of simplifying and coarse graining techniques in order to derive explanatory insight. Here we demonstrate that Zwanzig-Mori projection methods can be used to arbitrarily reduce the dimensionality of dynamical networks while retaining their dynamical properties. We show that a systematic expansion around the quasi-steady state approximation allows an explicit solution for memory functions without prior knowledge of the dynamics. The approach not only preserves the same steady states but also replicates the transients of the original system. The method also correctly predicts the dynamics of multistable systems as well as networks producing sustained and damped oscillations. Applying the approach to a gene regulatory network from the vertebrate neural tube, a well characterised developmental transcriptional network, identifies features of the regulatory network responsible dfor its characteristic transient behaviour. Taken together, our analysis shows that this method is broadly applicable to multistable dynamical systems and offers a powerful and efficient approach for understanding their behaviour.Comment: (8 pages, 8 figures

Quadratic Divergences in Kaluza-Klein Theories

We investigate the so-called ``Kaluza-Klein regularisation'' procedure in supersymmetric extensions of the standard model with additional compact dimensions and Scherk-Schwarz mechanism for supersymmetry breaking. This procedure uses a specific mathematical manipulation to obtain a finite result for the scalar potential. By performing the full calculation, we show that the finiteness of this result is not only a consequence of the underlying supersymmetry, but also the result of an implicit fine-tuning of the coefficients of the terms that control the ultraviolet behaviour. The finiteness of the Higgs mass at one-loop level seems therefore to be an artefact of the regularisation scheme, and quadratic divergences are expected to reappear in higher orders of perturbation theory.Comment: 10 pages, LaTe

Non-nested testing of spatial correlation

We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, or when parametric modelling arises in part from a common factor or other structure. In the former case, observations may be regularly-spaced across one or more dimensions, as is typical with much spatio-temporal data, or irregularly-spaced across all dimensions; both isotropic models and non-isotropic models can be considered, and a wide variety of correlation structures. In the second case, models involving spatial weight matrices are covered, such as “spatial autoregressive models”. The setting is sufficiently general to potentially cover other parametric structures such as certain factor models, and vector-valued observations, and here our preliminary asymptotic theory for parameter estimates is of some independent value. The test statistic is based on a Gaussian pseudo-likelihood ratio, and is shown to have an asymptotic standard normal distribution under the null hypothesis that one of the two models is correct; this limit theory rests strongly on a central limit theorem for the Gaussian pseudo-maximum likelihood parameter estimates. A small Monte Carlo study of finite-sample performance is included

New Methods for the Analysis of Long-Memory Time Series

Some recent developments in the analysis of time series are applied to real economic data. It is assumed that any stochastic or nonstochastic trends have been removed from the raw observed time series. Models for long-memory time series are considered in which the autocovariance sequence is parameterized only at very long lags or the spectral density is parameterized only at very low frequencies. The recently proposed methods for estimating the differencing parameters are applied to an economic time series of prices in Spain. The results consistently indicate that the inflation series suffers from long memory. The corelogram and periodogram for the resulting filtered series are plotted. The autocorrelation estimates are very close to zero. However, the seasonal peaks are still presentPublicad