INDUSTRIAL POLICY IN GREAT BRITAIN AND ITS EFFECT ON TOTAL FACTOR PRODUCTIVITY IN MANUFACTURING PLANTS, 1990–1998

Industrial policy in any economy has a number of varying and occasionally conflicting objectives, but the overarching intention of the various grants, subsidies and support schemes, arguably, must be to improve the economic performance of the plants they assist directly. However, in the absence of counterfactual evidence, whether or not assistance does improve performance is hard to establish. In this paper, we consider the impact of two UK government industrial support schemes (Regional Selective Assistance and the Small Firm Merit Awards for Research and Technology) on UK manufacturing plant level total factor productivity in an attempt to answer the question, ‘did assistance make a difference?’     

Predicting UK Business Cycle Regimes

This paper uses logistic regression to construct a one‐quarter ahead prediction model for classical business cycle regimes in the UK. The binary dependent variable is obtained by applying simple mechanical rules to date turning points in quarterly real GDP data from 1963 to 1999. Using a range of real and financial leading indicators, several parsimonious one‐quarter‐ahead models are developed for the GDP regimes, with model selection based on the SIC criterion. A real M4 variable is consistently found to have predictive content. One model that performs well combines this with nominal UK and German short‐term interest rates. The role of the latter emphasises the open nature of the UK economy.     

Managing New Product Design and Development: an Anglo-German Study

This article reports the results of a study of innovation and product development at 245 manufacturing sites in the UK and Germany. It examines the relationship between design and performance and the competitiveness of the UK and Germany in design and manufacturing. Overall, few sites reached "world-class" standards - 9% in Germany and 3% in the UK, although many sites were not far below these standards.     

The Bank of England quarterly model of the UK economy

The Bank of England first acquired a macroeconomic model of the UK economy in early 1973, and used it for forecasting in June and July of that year. The initial model was obtained from the London Business School (LBS), but the last 14 years or so have, on the part of both the Bank and the LBS, led to developments which now make the models no closer to each other than to other large scale models of the UK. This article describes the structure and central properties of the current version of the Bank model, which has some 663 variables, 134 of which are modelled by behavioural equations, 153 by technical equations, 212 identities and 164 exogenous variables. In order to provide a transitional step between the kind of models with which most macroeconomists are familiar and the full scale version of the Bank model, one part of the article presents a very simple stylized version of the full model. This is a stepping stone to the full model which is described and analysed on a sectoral basis in Part 3, with a complete listing of equations in Part 4. Some of the simulation properties, and hence full model dynamic responses, are considered in Part 5.     

A modified logit model for time series with an application to the pricing behaviour of manufacturing firms in Australia

The modified logit model (Amemiya and Nold, 1975) is generalised to the case where the error term is autocorrelated. The asymptotic distribution (as n →∞ and T →∞) of a feasible GLS estimator of β is derived. Tests of linear restrictions on β and the significance of ρ are presented. The results of the applied work suggest that the factors which explain the pricing behaviour of manufacturing firms, as reported in the tendency survey conducted by the Australian Chamber of Commerce and Industry and the Westpac Banking Corporation, include historical inflation rates of up to 7 quarters and capacity utilisation. First version received: March 2001/Final version received: July 2002 RID="*" ID="*"  The first draft of this paper was written while the author was on study leave at the Department of Econometrics, University of Sydney, Australia.     

A review and validation of overall survival extrapolation in health technology assessments of cancer immunotherapy by the National Institute for Health and Care Excellence: how did the initial best estimate compare to trial data subsequently made available?

Background: Validation of overall survival (OS) extrapolations of immune-checkpoint inhibitors (ICIs) during the National Institute for Health and Care Excellence (NICE) Single Technology Assessment (STA) process is limited due to data still maturing at the time of submission. Inaccurate extrapolation may lead to inappropriate decision-making. The availability of more mature trial data facilitates a retrospective analysis of the plausibility and validity of initial extrapolations. This study compares these extrapolations to subsequently available longer-term data.

Methods: A systematic search of completed NICE appraisals of ICIs from March 2000 to December 2017 was performed. A targeted search was also undertaken to procure published OS data from the pivotal clinical trials for each identified STA made available post-submission to NICE. Initial Kaplan-Meier curves and associated extrapolations from NICE documentation were extracted to compare the accuracy of OS projections versus the most mature data.

Results: The review identified 11 STAs, of which 10 provided OS data upon submission to NICE. The extrapolations undertaken considered parametric or piecewise survival models. Additional data cut-offs provided a mean of 18 months of OS beyond the end of the original data. Initial extrapolations typically under-estimated OS from the most mature data cut-off by 0.4–2.7%, depending on the choice of assessment method and use of the manufacturer- or ERG-preferred extrapolation.

Conclusion: Long-term extrapolation of OS is required for NICE STAs based on initial immature OS data. The results of this study demonstrate that the initial OS extrapolations employed by manufacturers and ERGs generally predicted OS reasonably well when compared to more mature data (when available), although on average they appeared to underestimate OS. This review and validation shows that, while the choice of OS extrapolation is uncertain, the methods adopted are generally aligned with later-published follow-up data and appear appropriate for informing HTA decisions.