The transformation of steering and governance in Higher Education: funding and evaluation as policy instruments.

This paper focuses on policy implementation in higher education (HE) to be analysed through the evolution and transformation of the policy instruments, namely those related to the Government funding and evaluation. The research questions are: to what extent instruments can reveal the evolution of policy rationales and justifications? How instruments emerged, and become institutionalised, affecting and being affected by the characteristics of national configuration of HE systems? Whether and how they produce desired effects or evolve in unpredictable ways, generating unexpected results, playing new roles and functionalities? The evolution of the instruments seems to be dependent on some characteristics of the context and some key features of the instruments. The development has been often inspired by NPM principles, which aimed at increasing steering capacity of the policy maker on one side, and university role and autonomy on the other. The common narrative is then declined in very different ways among countries, and instruments implementation reveals the extent to which it is adapted to the existing characters (dominant paradigm) of the HE system.Higher Education, Funding, Evaluation, Policy instruments, Policy implementation

The Variance Profile

The variance profile is defined as the power mean of the spectral density function of a stationary stochastic process. It is a continuous and non-decreasing function of the power parameter, p, which returns the minimum of the spectrum (p → −∞), the interpolation error variance (harmonic mean, p = −1), the prediction error variance (geometric mean, p = 0), the unconditional variance (arithmetic mean, p = 1) and the maximum of the spectrum (p → ∞). The variance profile provides a useful characterisation of a stochastic processes; we focus in particular on the class of fractionally integrated processes. Moreover, it enables a direct and immediate derivation of the Szego-Kolmogorov formula and the interpolation error variance formula. The paper proposes a non-parametric estimator of the variance profile based on the power mean of the smoothed sample spectrum, and proves its consistency and its asymptotic normality. From the empirical standpoint, we propose and illustrate the use of the variance profile for estimating the long memory parameter in climatological and financial time series and for assessing structural change.Predictability; Interpolation; Non-parametric spectral estimation; Long memory.

Some New Approaches to Forecasting the Price of Electricity: A Study of Californian Market

In this paper we consider the forecasting performance of a range of semi- and non- parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and time- and price-dependent volatility. The typical approach in this area has been to use a range of tools that have proven popular in the financial econometrics literature, where volatility clustering is common. However, electricity time series tend to exhibit higher volatility on a daily basis, but within a mean reverting framework, albeit with occasional large ’spikes’. In this paper we compare the existing forecasting performance of some popular parametric methods, notably GARCH AR-MAX, with approaches that are new to this area of applied econometrics, in particular, Artificial Neural Networks (ANN); Linear Regression Trees, Local Regressions and Generalised Additive Models. Section 2 presents the properties and definitions of the models to be compared and Section 3 the characteristics of the data used which in this case are spot electricity prices from the Californian market 07/1999-12/2000. This period includes the ’crisis’ months of May-August 2000 where extreme volatility was observed. Section 4 presents the results and ranking of methods on the basis of forecasting performance. Section 5 concludes.Electricty Time Series; Forecasting Performance; Semi- and Non- Parametric Methods

The Empirical Properties of Some Popular Estimators of Long Memory Processes

We present the results of a simulation study into the properties of 12 different estimators of the Hurst parameter, H, or the fractional integration parameter, d, in long memory time series. We compare and contrast their performance on simulated Fractional Gaussian Noises and fractionally integrated series with lengths between 100 and 10,000 data points and H values between 0.55 and 0.90 or d values between 0.05 and 0.40. We apply all 12 estimators to the Campito Mountain data and estimate the accuracy of their estimates using the Beran goodness of t test for long memory time series.Strong dependence; global dependence; long range dependence; Hurst parameter estimators

Bayesian Extreme Value Mixture Modelling for Estimating VaR

A new extreme value mixture modelling approach for estimating Value-at-Risk (VaR) is proposed, overcoming the key issues of determining the threshold which defines the distribution tail and accounts for uncertainty due to threshold choice. A two-stage approach is adopted: volatility estimation followed by conditional extremal modelling of the independent innovations. Bayesian inference is used to account for all uncertainties and enables inclusion of expert prior information, potentially overcoming the inherent sparsity of extremal data. Simulations show the reliability and flexibility of the proposed mixture model, followed by VaR forecasting for capturing returns during the current financial crisis.Extreme values; Bayesian; Threshold estimation; Value-at-Risk

Constructing Structural VAR Models with Conditional Independence Graphs

In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which could be given a causal interpretation. A comparison between competing models is then made by considering likelihood and economic theory.Graphical models; directed acyclic graphs; term structure; causality.

Widespread nanoflare variability detected with Hinode/XRT in a solar active region

It is generally agreed that small impulsive energy bursts called nanoflares are responsible for at least some of the Sun's hot corona, but whether they are the explanation for most of the multi-million degree plasma has been a matter of ongoing debate. We here present evidence that nanoflares are widespread in an active region observed by the X-Ray Telescope on-board the Hinode mission. The distributions of intensity fluctuations have small but important asymmetries, whether taken from individual pixels, multi-pixel subregions, or the entire active region. Negative fluctuations (corresponding to reduced intensity) are greater in number but weaker in amplitude, so that the median fluctuation is negative compared to a mean of zero. Using MonteCarlo simulations, we show that only part of this asymmetry can be explained by Poisson photon statistics. The remainder is explainable with a tendency for exponentially decreasing intensity, such as would be expected from a cooling plasma produced from a nanoflare. We suggest that nanoflares are a universal heating process within active regions.Comment: 26 pages, 7 figure

A comparison of Spillover Effects before, during and after the 2008 Financial Crisis

This paper applies graphical modelling to the S&P 500, Nikkei 225 and FTSE 100 stock market indices to trace the spillover of returns and volatility between these three major world stock market indices before, during and after the 2008 financial crisis. We find that the depth of market integration changed significantly between the pre-crisis period and the crisis and post- crisis period. Graphical models of both return and volatility spillovers are presented for each period. We conclude that graphical models are a useful tool in the analysis of multivariate time series where tracing the flow of causality is important.Volatility spillover; graphical modelling; financial crisis; causality