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

Synoptic climatology of winter intense precipitation events along the Mediterranean coasts

Abstract. The link between winter (December-January-February) precipitation events at 15 Mediterranean coastal locations and synoptic features (cyclones and Northern Hemisphere teleconnection patterns) is analyzed. A list of precipitation events has been produced; q percentile thresholds (Thq) and corresponding frequency Nq (for q equal to 25, 50, 90 and 98) have been considered. A negative trend has been detected in total precipitation and N50 at many locations, while no significant trend in N25, N90 and N98 has been found. The negative phase of the North Atlantic Oscillation (NAO) and the East Atlantic/West Russia pattern (EAWR) compete for exerting the largest influence on the frequency of the 25th, 50th and 90th percentiles, with EAWR and NAO exerting their largest influence in the central and western Mediterranean areas, respectively. All percentiles show a similar behavior except for the 98th percentile, which shows no convincing link to any teleconnection pattern. The cyclone tracks that are associated with precipitation events have been selected using the ERA-40 reanalysis data, and a strong link between intense precipitation and cyclones is shown for all stations. In general, the probability of detecting a cyclone within a distance of 20° from each station increases with the intensity of the precipitation event and decreases with the duration of a dry period. The origin and track of cyclones producing intense precipitation differ among different areas. When precipitation occurs in the northwestern Mediterranean, cyclones are generally either of Atlantic origin or secondary cyclones associated with the passage of major cyclones north of the Mediterranean Basin, while they are mostly generated inside the region itself for events at the eastern Mediterranean coast. An important fraction of intense events in the southern areas is produced by cyclones that are generated over northern Africa. The analysis of sea level pressure and geopotential height at 500 hPa highlights the important role of cyclone depth, circulation strength, surrounding synoptic condition, and of slow speed of the cyclone center for producing intense precipitation events

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

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.

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

Extreme Value GARCH modelling with Bayesian Inference

Extreme value theory is widely used financial applications such as risk analysis, forecasting and pricing models. One of the major difficulties in the applications to finance and economics is that the assumption of independence of time series observations is generally not satisfied, so that the dependent extremes may not necessarily be in the domain of attraction of the classical generalised extreme value distribution. This study examines a conditional extreme value distribution with the added specification that the extreme values (maxima or minima) follows a conditional autoregressive heteroscedasticity process. The dependence has been modelled by allowing the location and scale parameters of the extreme distribution to vary with time. The resulting combined model, GEV-GARCH, is developed by implementing the GARCH volatility mechanism in these extreme value model parameters. Bayesian inference is used for the estimation of parameters and posterior inference is available through the Markov Chain Monte Carlo (MCMC) method. The model is firstly applied to relevant simulated data to verify model stability and reliability of the parameter estimation method. Then real stock returns are used to consider evidence for the appropriate application of the model. A comparison is made between the GEV-GARCH and traditional GARCH models. Both the GEV-GARCH and GARCH show similarity in the resulting conditional volatility estimates, however the GEV-GARCH model differs from GARCH in that it can capture and explain extreme quantiles better than the GARCH model because of more reliable extrapolation of the tail behaviour.Extreme value distribution, dependency, Bayesian, MCMC, Return quantile