893 resultados para output pricing
Resumo:
After its privatization in 1989, the water and sewerage industry of England and Wales faced a new regulatory régime and implemented a substantial capital investment program aimed at improving water and environmental standards. A new RPI + K regulatory pricing system was designed to compensate the industry for its increased capital costs, encourage increased efficiency, and maintain fair prices for customers. This paper evaluates how successful privatization and the resulting system of economic regulation has been. Estimates of productivity growth, derived with quality adjusted output indices, suggest that despite reductions in labor usage, total factor productivity growth has not improved since privatization. Moreover, total price performance indices reveal that increases in output prices have outstripped increases in input costs, a trend which is largely responsible for the increase in economic profits that has occurred since privatization. * We would like to thank Emmanuel Thanassoulis, Joshy Easaw, Jim Love, John Sawkins, and an anonymous referee for helpful comments on earlier drafts of this paper. The usual disclaimer applies.
Resumo:
In data envelopment analysis (DEA), operating units are compared on their outputs relative to their inputs. The identification of an appropriate input-output set is of decisive significance if assessment of the relative performance of the units is not to be biased. This paper reports on a novel approach used for identifying a suitable input-output set for assessing central administrative services at universities. A computer-supported group support system was used with an advisory board to enable the analysts to extract information pertaining to the boundaries of the unit of assessment and the corresponding input-output variables. The approach provides for a more comprehensive and less inhibited discussion of input-output variables to inform the DEA model. © 2005 Operational Research Society Ltd. All rights reserved.
Resumo:
Using an event study approach, this article reports evidence that the UK Treasury bond market displayed anomalous pricing behaviour in the secondary market both immediately before and after auctions of seasoned bonds. Using a benchmark return derived from the behaviour of the underlying yield curve, the market offered statistically and economically significant excess returns, around the auctions held between 1992 and 2004. A cross-sectional analysis of the cumulative excess returns shows that the excess demand at the auctions is a key determinant of this excess return.
Resumo:
Computer models, or simulators, are widely used in a range of scientific fields to aid understanding of the processes involved and make predictions. Such simulators are often computationally demanding and are thus not amenable to statistical analysis. Emulators provide a statistical approximation, or surrogate, for the simulators accounting for the additional approximation uncertainty. This thesis develops a novel sequential screening method to reduce the set of simulator variables considered during emulation. This screening method is shown to require fewer simulator evaluations than existing approaches. Utilising the lower dimensional active variable set simplifies subsequent emulation analysis. For random output, or stochastic, simulators the output dispersion, and thus variance, is typically a function of the inputs. This work extends the emulator framework to account for such heteroscedasticity by constructing two new heteroscedastic Gaussian process representations and proposes an experimental design technique to optimally learn the model parameters. The design criterion is an extension of Fisher information to heteroscedastic variance models. Replicated observations are efficiently handled in both the design and model inference stages. Through a series of simulation experiments on both synthetic and real world simulators, the emulators inferred on optimal designs with replicated observations are shown to outperform equivalent models inferred on space-filling replicate-free designs in terms of both model parameter uncertainty and predictive variance.