Output Expectations and Forecasting of UK Manufacturing

This paper analyses both quarterly data from the Confederation of British Industry (CBI) Survey on respondents’ expectations of recent and forthcoming manufacturing output and monthly Office of National Statistics (ONS) figures on actual manufacturing output within the UK. Quarterly output expectations of the CBI manufacturers are explained from the monthly ONS observations using a bounded rationality approach. The logistic formulation models the diffusion process across respondents. There is a backward-looking CBI Survey perspective, explained by past ONS observations, and a forward-looking perspective, explained from future ONS statistics. Also, the forecasting of monthly manufacturing output from earlier values, along with the quarterly CBI Survey information, is examined and tested against the alternative Pesaran/Thomas method. The study provides econometric evidence for the validity of the logistic model and shows that bounded rationality can explain the formation of predictions among business managers in the UK manufacturing sector. The emerging consensus from the literature, supported by this paper, is that the logistic format is a superior approximation to the true data generating process compared with the earlier standard Anderson/Pesaran/Thomas approach. An adjustment to the Survey is used, which achieves perfect symmetry with up and down versions of the data. The benefits of this adjustment are tested in the forecasting section.

WELFARE ECONOMICS AND THE MEASUREMENT OF NNP*

Some relationships between NNP and economic welfare are explored in the confines of a simple, static welfare maximization model. Various assumptions concerning both the measurement of NNP and the economic system underlying this model are dropped seriatem and the implications for the correspondence between NNP and economic welfare are examined. The following conclusions emerge. There are several classes of resource reorganization in which NNP and welfare move in the same direction, so that NNP can serve as an ordinal proxy for welfare. These include changes in taxes or competitive imperfections which result in product substitution and movements along the transformation function. With a general qualification, NNP-welfare correspondence is preserved for allocative changes which affect the real costs and prices of goods included in NNP or of non-included goods in inelastic demand; changes in involuntary unemployment; and changes in technological externalities affecting producers. There are other cases where changes in NNP and welfare are not positively correlated. Included here are changes in real costs of non-included goods for which demand is elastic and changes in technological externalities imposed on consumers.     

Exploring optimization through hierarchies and ratio scales

This paper explores the concept of optimization by solely using the AHP and compares outcomes with those obtained in traditional optimization theory without and with constraints. The difference is essentially in the absence of the traditional black box involving complex manipulations in algebra or the calculus on an assumed linear or nonlinear mathematical structure. This paper is an exploratory attempt to use an individual's understanding together with a way to convert his judgments to ratios to deal with optimization. At first sight it may go contrary to the reader's (learned) intuition, but in the end he must face the question of whether the magic of traditional manipulations gives rise to better answers than one's actual and complete understanding in which one does not abdicate judgmental control of the solution, and why. This idea is ripe for deeper and more detailed exploration.     

OR vice: Forecasting and classification techniques for narcotics interdiction

Counternarcotics interdiction efforts have traditionally relied on historically determined sorting criteria or “best guess” to find and classify suspected smuggling traffic. We present a more quantitative approach which incorporates customized database applications, graphics software and statistical modeling techniques to develop forecasting and classification models. Preliminary results show that statistical methodology can improve interdiction rates and reduce forecast error. The idea of predictive modeling is thus gaining support in the counterdrug community. The problem is divided into sea, air and land forecasting, only part of which will be addressed here. The maritime problem is solved using multiple regression in lieu of multivariate time series. This model predicts illegal boat counts by behavior and geographic region. We developed support software to present the forecasts and to automate the process of performing periodic model updates. During the period, the model was in use at. Coast Guard Headquarters. Because of deterrence provided by improved intervention, the vessel seizure rate declined from 1 every 36 hours to 1 every 6 months. Due in part to the success of the sea model, the maritime movement of marijuana has ceased to be a major threat. The air problem is more complex, and required us to locally design data collection and display software. Intelligence analysts are using a customized relational database application with a map overlay to perform visual pattern recognition of smuggling routes. We are solving the modeling portion of the air problem using multiple regression for regional forecasts of traffic density, and discriminant analysis to develop tactical models that classify “good guys” and “bad guys”. The air models are still under development, but we discuss some modeling considerations and preliminary results. The land problem is even more difficult, and data collection is still in progress.