This paper investigates the extent to which the outcomes of the 2008 Research Assessment Exercise, determined by peer review, can be explained by a set of quantitative indicators, some of which were made available to the review panels. Three cognate units of assessment are examined in detail:...

The UK's higher education funding councils have proposed reducing the number of submitted outputs from four to three in the forthcoming Research Excellence Framework to reduce the burden on panel members. This reduction is considered to be sufficient for panels to form a robust view of the...

We present new autoregressive logit models for forecasting the probability of a time series of financial asset returns exceeding a threshold. The models can be estimated by maximizing a Bernoulli likelihood. Alternatively, to account for the extent to which an observation does or does not exceed...

The efficient management of wind farms and electricity systems benefit greatly from accurate wind power quantile forecasts. For example, when a wind power producer offers power to the market for a future period, the optimal bid is a quantile of the wind power density. An approach based on...

The recent advent of smart meters has led to large micro-level datasets. For the first time, the electricity consumption at individual sites is available on a near real-time basis. Efficient management of energy resources, electric utilities, and transmission grids, can be greatly facilitated by...

Electricity demand forecasts are needed for decisions regarding generation dispatch for lead times as short as just a few minutes. Imbalance between generation and demand causes deviation of the system frequency from its target, which in Great Britain is 50 Hz. This, in turn, causes a change in...

The recent advent of smart meters has led to large micro-level datasets. For the first time, the electricity consumption at individual sites is available on a near real-time basis. Efficient management of energy resources, electric utilities, and transmission grids, can be greatly facilitated by...

Value at Risk (VaR) forecasts can be produced from conditional autoregressive VaR models, estimated using quantile regression. Quantile modeling avoids a distributional assumption, and allows the dynamics of the quantiles to differ for each probability level. However, by focusing on a quantile,...