Book Reviews 321

planning, and portfolio selection is particularly thorough.

For the researcher, the listings of comparisons among alternative forecasting methods should be valuable. One can choose any major forecasting method and find references which compare the effec- tiveness of other methods. For example, some sixty comparisons are listed between ARIMA methods and one or more of the following: autoregressive, causal, causal (simultaneous systems), decomposition, distributed lag, exponential smoothing, judgmental, and other. I found that many of these comparisons were not evident from the titles or abstracts of the papers, which reflects the high-quality research that went into this bibliography.

Academic users of this bibliography will be able to spice up their lectures with some of the more exotic references listed. Some good classroom examples that I found were papers on forecasting productivity in the Israeli diamond industry (good results), the population of coloured foxes in Labrador (also good), and earthquakes (shaky results).

Although the author does not give an evaluation of any reference, some are labelled either basic or advanced according to mathematical complexity. Basic references can be used by beginners in the field. Articles labelled as advanced are considered to have little general value, because of their inaccessibility. I checked a number of these labels and found that I agreed with most. I also found that labels were assigned only in extreme cases. Numerous difficult papers on such topics as spectral analysis and statistical testing of simultaneous equations models are not regarded as advanced. I expect that the average user will find that any paper that did get an advanced label is quite incomprehensible.

Most bibliographies are seldom updated. However, Fildes intends to publish periodic updates through the Manchester Business School. He also intends a small expansion of journal coverage.

In conclusion, this is a unique reference work which should help unify the field of forecasting.

EVERETTE S . GARDNER, JR. Commander, Supply Corps, U. S. Navy Management Information Systems OfJicer U. S. Atlantic Fleet Headquarters (Code N0422) Norfolk, Virginia 23.511, U.S.A.

REFERENCES

Armstrong, J. Scott, Long-Range Forecasting: From Crystal Ball to Computer, New York: Wiley, 1978.

Chow, W. M., ‘Adaptive control of the exponential smoothipg constant’, Journal of Industrial Engineering, 16 (1965), 314317.

Gilchrist, Warren G., ‘Methods of estimation involv- ing discounting’, Journal of the Royal Statistical Society, 29 (1967), 355-369.

Harrison, P. J., and Davies, 0. L., ‘The use of cumulative sum (CUSUM) techniques for the control of routine forecasts of product demand, Operational Research Quarterly, 12 (1964), 325-333.

Trigg, D. W., ‘Monitoring a forecasting system’, Operational Research Quarterly, 15 (1 964),

Winters, Peter S., ‘Forecasting sales by exponen- tially weighted moving averages’, Management Science, 6 (1960), 324342.

2 7 1 -274.

THE ECONOMIST INTELLIGENCE UNIT SPECIAL REPORTS, London Office: Spencer House, 27 St James’s Place, London SWlA 1NT.

The Economist Intelligence Unit is the research and consultancy subsidiary of the Economist Newspaper. In addition to mainstream consultancy assignments and its well known reviews of International Economies, Energy, Retail Business and others, the EIU issues a series of special reports on a range of business and industrial topics. Since 1980 the reviews have covered subjects as diverse as personal investment, personal and corporate tax, commodity studies, economic analyses of Japan, Iran and the New Industrial Countries, and energy studies. The industrial studies have included both new technologies-micro-elec- tronics and material handling-as well as the pro- blems of old industries such as vehicles and alco- holic drinks.

In exchange for around f50 what should a cus- tomer expect? Judging from five recent studies: UK Commercial and Industrial Property, Air Cargo in the EEC, World Timber to the year 2000, the International Sugar Market and the U.K. Plastics Industry, the customer receives a competent and well organized updating on the current position of the sector in terms of market segmentation, principal participants, alternative technologies and regulatory authorities. A detailed breakdown of the report’s contents is always provided but the inclusion of bibliographies, references and indices which were helpful when they appeared, was not, unfortunately, standard practice. From a forecasting viewpoint, the reports relied minimally on sophisticated techniques, although relevant econometric studies were some- times reported. The original work in the reports tended to be based, however, on linear trend extrapolation and to use assumptions based on, for example, World Bank or OECD forecasts.

For some purposes no doubt a descriptive and non-causal approach would be perfectly adequate

322 Journal of Forecasting Vol. 1, Iss. No. 3

but the main value of these reports to a professional forecaster lies in saving the many days that would be required to get up to date with the technology sources and special characteristics of a given market.

No doubt, in the case of sensitive forecasts, these reports provide only some of the answers and the forecaster will need to consider the likely reliability of some of the data coyly attributed to ‘industry estimates’. Also, in most cases, the infrequency of the studies means that in practice considerable updating is required and this is difficult where sources are not fully attributed.

On balance, though, the acceptability of this series is based on its ability to provide timely and time saving reviews of particular market situations, and there is no doubt that for most people with a ‘need to know’ these reports are cost effective.

DOUGLAS WOOD Professor of Business Economics Manchester Business School

AN INTRODUCTION T O ECONOMETRIC FORECASTING AND FORECASTING MODELS, Klein, Lawrence R., and Young, Richard M., Lexington, Mass: D. C. Heath and Company, 1980.

This book (or perhaps monograph would be a better description) presents a non-technical introduction to the Wharton quarterly forecasting model. Although it relates exclusively to the (extensive) experience of the Wharton forecasting project, it should be of interest to students and others who desire an intro- duction to the development of econometric fore- casting models. The authors present a good analysis of the distinction between the time series and econo- metric model approach to forecasting. In their view. the econometric model approach brings together economic theory, mathematics, statistics and in- tuition to develop a forecast.

The book consists of five relatively short chapters. The first chapter presents the time series and econ- ometric model approach to forecasting, giving em- phasis to the strength of the latter.

The structure of the Wharton quarterly model is summarized in flow chart form in chapter two. The chapter also presents several regression equations and parameter estimates.

Chapter three is the most technical of the five chapters and attempts to deal with the problems of model specification, estimation and validation. Un- fortunately, the authors do not discuss the biases that can be introduced through misspecification or the use of an inappropriate estimation technique. Although these prob1c.s are acknowledged by the

authors, they are dismissed without the discussion they deserve.

The methodology employed by Wharton to de- velop forecasts from estimated equations is set forth in chapter 4. The authors emphasize that forecasting is an art as well as a science. The science involves theoretical modelling and statistical estimation. The art involves blending of the statistical results with intuition to develop a forecast.

The final chapter presents a summary on how well the Wharton model has done in prediction over the years since its development in 1965. The authors present evidence that the Wharton model’s track record for the 1970s compares favourably with predictions based on other models.

The book provides a very good non-technical introduction to econometric forecasting using the Wharton model as the vehicle for presentation. It should prove valuable for introductory courses and to those desiring an overview of how a major forecasting model operates. It would be of little value to those desiring to develop an understanding of how to critically examine the formulation or estimation of forecasting models.

ROBERT J. ROHR, 111 Professor of Economics, Brown U . , Providence, Rhode Island, U .S .A .

THE BEG INNING FORECASTER, Leven bach, Hans and Cleary, James P., Belmont California: Lifetime Learning, 1981, No. of pages: 372. Price: $30.

Levenbach and Cleary have provided a needed contribution to the practice of forecasting with The Beginning Forecaster (TBF). It deals with the appli- cation of statistical forecasting methods in the context of business organizations. It places strong emphasis upon (1) theprocess by which forecasts are developed, maintained, and updated by forecasters, (2) the management of the forecasting function within the organization, and (3) the use of modern data analysis methods to bring to light patterns in historical data and ensure sound application of the methods chosen. I believe that, in emphasizing these areas, Levenbach and Cleary have spotlighted op- portunities for improvement in organizational fore- casting. Sound forecasting process is far from being universally understood by practising forecasters, who tend to create their own ad hoc approaches. And data analysis is critical, not only in selecting an appropriate forecasting method, but also because (I believe) a thorough understanding of the past is the fundamental building block to developing plausible forecasts.