Demand uncertainty and foreign trade in the UK tobacco industry

The concern about the potential health harzards of various tobacco products in the UK has led many tobacco companies to exploit markets in underdeveloped countries. These markets are relatively safe since the health risks of tobacco are not yet fully known. The possibility, therefore, arises that domestic producers might exploit certain markets to provide a form of profit insurance, exporting surpluses when domestic demand is unexpectedly low. This study concentrates on the UK cigarette industry. However, similar analysis could be performed for any uncertain market where a certain alternative exists that can be exploited at relatively low cost.     

Cellular clouds

This paper progresses an analysis of what it means to be a cellular network operator and what form the ownership and control of future cellular networks may take. Alternative modes of ownership may allow for the creation of more flexible cellular networking environments in which competition for packages of resources, that is, the licenced spectrum, the RAN and core network, ensures that the most efficient and innovative architectures, services and applications are delivered to the consumer. This paper develops the concept of the Cellular Network Cloud and Utility Cellular Networking as a means to explore the possibilities for such revolution in the way in which cellular networks are owned and operated. Changes to current industry practices which would help to meet the objectives of more flexible and competitive network ownership and control are proposed.     

The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models vs. Simple Linear Econometric Models*

In this paper we construct output gap and inflation predictions using a variety of dynamic stochastic general equilibrium (DSGE) sticky price models. Predictive density accuracy tests related to the test discussed in Corradi and Swanson [Journal of Econometrics (2005a), forthcoming] as well as predictive accuracy tests due to Diebold and Mariano [Journal of Business and Economic Statistics (1995) , Vol. 13, pp. 253–263]; and West [Econometrica (1996) , Vol. 64, pp. 1067–1084] are used to compare the alternative models. A number of simple time‐series prediction models (such as autoregressive and vector autoregressive (VAR) models) are additionally used as strawman models. Given that DSGE model restrictions are routinely nested within VAR models, the addition of our strawman models allows us to indirectly assess the usefulness of imposing theoretical restrictions implied by DSGE models on unrestricted econometric models. With respect to predictive density evaluation, our results suggest that the standard sticky price model discussed in Calvo [Journal of Monetary Economics (1983), Vol. XII, pp. 383–398] is not outperformed by the same model augmented either with information or indexation, when used to predict the output gap. On the other hand, there are clear gains to using the more recent models when predicting inflation. Results based on mean square forecast error analysis are less clear‐cut, although the standard sticky price model fares best at our longest forecast horizon of 3 years, it performs relatively poorly at shorter horizons. When the strawman time‐series models are added to the picture, we find that the DSGE models still fare very well, often outperforming our forecast competitions, suggesting that theoretical macroeconomic restrictions yield useful additional information for forming macroeconomic forecasts.     

Monetary Policy Rules with Model and Data Uncertainty

In this paper we examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules that mimic monetary policymaking decisions. Our approach is to build real-time data sets and simulate a real-time policy-setting environment in which we assume that policy is captured by movements in the actual federal funds rate, and then to assess what sorts of policy rule models and what sorts of data best explain what the Federal Reserve actually did. This approach allows us not only to track the performance of alternative rules over time (hence facilitating a type of model selection among competing rules), but also to more generally assess the importance of the data revision process in the formation of macroeconomic time series models. From the perspective of real-time data, our results suggest that the use of data that are erroneous, in the sense that they were not available at the time decisions could have been made based on forecasts from the rules, can lead to the selection of quantitatively different models. From the perspective of finding a rule that best approximates what the Federal Reserve Board (Fed) has actually done (and hence from the perspective of finding a rule that best approximates what the Fed will do in the future), we find that (i) our version of “calibration” is better than naïve estimation, although both are dominated by an approach to rule formation based on the use of adaptive least-squares learning; (ii) rules based on data that are not seasonally adjusted are more reliable than those based on seasonally adjusted data; and (iii) rules based solely on preliminary data do not minimize mean square forecast error risk. In particular, early releases of data can be noisy, and for this reason it is useful to also use data that have been revised when making decisions using policy rules.     

Still balancing the books: the NUM and the 1984–85 strike

In the context of the NUM's decision to seek a merger, this article examines the financial impact of the 1984–85 strike. In conducting the strike, its federal structure is shown to have provided the union leadership with flexibility. But it has limited the capacity to recover afterwards and has consequences for merger prospects with other unions.