Control, accounting and value-for-money implications of utility regulation:a literature review

This paper reviews and evaluates the literature concerning the privatisation and regulation of the utility industries in the UK. The economic theories behind and political reasons for the programme are considered to give the reader an appreciation of the environment from which these organisations were born and the implications for their continued existence. Once this has been established the paper then considers the role that accounting has played and will continue to play in these industries. This includes consideration of the technical questions which these new organisations are asking and also the role that accounting has in the organisational structure and culture. It draws as a conclusion that these recently privatised industries provide a unique and rich source for further accounting research.

Household sector money demand for the UK

We test for the existence of a long-run money demand relationship for the UK involving household-sector Divisia and simple sum monetary indexes for the period from 1977 to 2008. We construct our Divisia index using non-break-adjusted levels and break-adjusted flows following the Bank of England. We test for cointegration between the real Divisia and simple sum indexes, their corresponding opportunity cost measures, real income and real share prices. Our results support the existence of a long-run money demand relationship for both the Divisia and simple sum indexes.

Does money matter in inflation forecasting

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.

Follow the money:the Audit Commission, Public Money and the Management of Public Services, 1983-2008, Duncan Campbell-Smith

Book review: Duncan Campbell-Smith. Allen Lane, 2008, 744 pp., £ 25 (hb), ISBN: 9781846140686

Econometrics of yield spreads in the money market:a note

The literature on bond markets and interest rates has focused largely on the term structure of interest rates, specifically, on the so-called expectations hypothesis. At the same time, little is known about the nature of the spread of the interest rates in the money market beyond the fact that such spreads are generally unstable. However, with the evolution of complex financial instruments, it has become imperative to identify the time series process that can help one accurately forecast such spreads into the future. This article explores the nature of the time series process underlying the spread between three-month and one-year US rates, and concludes that the movements in this spread over time is best captured by a GARCH(1,1) process. It also suggests the use of a relatively long term measure of interest rate volatility as an explanatory variable. This exercise has gained added importance in view of the revelation that GARCH based estimates of option prices consistently outperform the corresponding estimates based on the stylized Black-Scholes algorithm.

Breaks in the UK household sector money demand function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.

More than money:developing an integrative multi-factorial measure of entrepreneurial success

This article conceptualises and operationalizes ‘subjective entrepreneurial success’ in a manner which reflects the criteria employed by entrepreneurs, rather than those imposed by researchers. Using two studies, a first qualitative enquiry investigated success definitions using interviews with 185 German entrepreneurs; five factors emerged from their reports: firm performance, workplace relationships, personal fulfilment, community impact, and personal financial rewards. The second study developed a questionnaire, the Subjective Entrepreneurial Success–Importance Scale (SES-IS), to measure these five factors using a sample of 184 entrepreneurs. We provide evidence for the validity of the SES-IS, including establishing systematic relationships of SES-IS with objective indicators of firm success, annual income and entrepreneur satisfaction with life and financial situation. We also provide evidence for the cross-cultural invariance of SES-IS using a sample of Polish entrepreneurs. The quintessence of our studies being that subjective entrepreneurial success is a multi-factorial construct, i.e. entrepreneurs value various indicators of success with money as only one possible option.

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regressiontechniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a nave random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies. © 2010 Elsevier B.V. All rights reserved.