532 Book reviews / International Journal of Forecasting 19 (2003) 521–541

90s book andnot a second edition Secondly, the than a guide for managers or business students. Thebibliography is out of date (pre-1990) and, finally, Preface spends much time specifying a teachingthe book is designed basically for practitioners. programme for a forecasting course and, despite

In conclusion, while I think this book deserves a being entitled ‘‘Why Forecast?’’, Chapter 1 devotesplace on anyone’s bookshelf. I did expect more from much of its space to identifying topics that shouldtwo outstanding scientists, though I do acknowledge appear in forecasting examinations or be the subjectthat research and authoring are traditional enemies. I of student projects. Phrases like ‘‘In teaching fore-am looking forward to reading a second – up to date casting we have found. . . ’’ or ‘‘In our courses we– edition including a fourth part dedicated to the require students. . . ’’ can be found throughout theaward winning reliability methodology PREDICT. text, as can criticisms of other forecasting textbooks.

Such topics are unlikely to be of interest to managerswho bought a book entitled ‘‘Practical ForecastingDr. K. Nikolopoulosfor Managers’’.Forecasting Systems Unit

Secondly, the book uses a level and style ofSchool of Electrical & Computer Engineeringmathematical notation, which is likely to deter mostNational Technical University of Athensmanagers and many business students. For example,Athensthe simple multiplicative seasonal model is expressedGreeceas:

doi:10.1016/S0169-2070(03)00022-0 y(t)| 5 trend(t)* I(t modL 1 1).

Similarly, moving averages areintroduced usingthe following formula:

Practical Forecasting for Managers, John C. Nash n

& Mary Nash (2001), London: Arnold and New MA(n,t,y)5 O y /n.S Dt112ii51York: Oxford University Press, 296 pages. ISBN 0

340 76238 1 Paperback £24.99, $40.00. In many cases, the use of subscripts andsuperscripts might have made the notation clearer,

The cover of this book claims that it is essential but sometimes there is a curious mixture of styles.reading for: i) students on business and managementFor example, the following rather long expression iscourses and ii) professional managers and adminis- used to explain the simple idea of a series that has atrators needing a practical guide to forecasting. long-term average that is constant (page 158):Regrettably, I cannot recommend the book to either

y predicted5 constant5mu Y 5m .of these groups. Indeed, I believe that the book Y] ]would convince most professional managers thatformal forecasting methods are incomprehensible Managers who make forecasts are usually expertsand irrelevant to their needs. As a result, they would on their products and markets, but there is no reasonbe likely to continue to rely on informal judgmental to assume that they will also be at ease with this typemethods, disregarding any statistical forecasts that of mathematical notation.were presented to them. I base these views on almost Thirdly, when the book uses discussion, ratherthirty years of experience of teaching business than mathematics, to develop ideas it is also likely tostudents and working with professional managers. confuse. For example, in chapter 2 (Planning the

Why does the book, which has the admirable Forecasting Task), a section headed,Example ofobjective of helping managers ‘‘do a better job of forecasting with transformed variables, suddenlyanticipating and hence a better job of managing’’, appears without explanation or warning. Its firstmiss the needs of its intended readers so badly? First,three sentences read as follows:the authors do not seem to be clear on whom theyare writing for. For example, much of the book reads ‘‘The following edited output was obtainedlike a guide to teaching a forecasting course, rather starting with some data that grows exponentially.

Book reviews / International Journal of Forecasting 19 (2003) 521–541 533

It is in the column ‘expeg’ below. By using linear Forecasting Tasks given at the start of the book, onlyregression (Chapter 12) to fit a straight-line model two are directly related to business. The otherto the natural logarithm of this data, labelled ‘ln forecasting tasks involve cases of AIDS, smoking,expeg’ below, we get fitted values in ‘logmod’. If student populations and the weather. One of the fewwe subtract ‘log mod’ from ‘linexpeg’ we get the examples in the book that involves forecasting theresidual ‘resin log’.’’ demand for a product is based on data that was

obtained from another forecasting text (BowermanIt cannot be assumed that readers encountering and O’Connell, 1979). There is also little or no

this section will know what linear regression is, what discussion of the relationship of forecasting tofitted values are, or why natural logarithms might typical business problems such as inventory control,have anything to do with forecasting. Indeed, this human resource management, cash flow manage-section sets a pattern of discussing technical terms ment, production scheduling and distribution. Inbefore they have been explained or set in context. particular, I could find no reference to Croston’sReaders are told how to interprett-ratios for regres- method which can offer a practical solution to thesion or ARIMA parameters, and what the Ljung– widely encountered problem of forecasting whenBox test is used for, 110 pages before regression is demand is intermittent.introduced and 162 pages before the section ‘‘What In its treatment of topics the books relies heavilyare ARIMA models?’’ appears. In the early chapters on the personal experience of the authors and less ontoo much material is introduced too quickly. For published research results. For example, on page 25example, in the space of a four short paragraphs the authors state that ‘‘In our experience? ? ?

readers are introduced to Pegel’s classification, [damped] trends are relatively rare’’. This is to bestationarity, multiplicative and additive seasonality, contrasted with the research-based advice given inexponential trends and the application of geometric Armstrong (2001), p262): ‘‘Damp the trend as themeans to determine ‘‘average’’ interest rates. In a forecast horizon lengthens’’. Most managers makefew sections the text degenerates into lists of use of judgment in their forecasts, but the treatmentkeywords or note form, as if it has been copied of this topic makes no reference to the growingdirectly from an overhead projector slide (e.g., page research literature on the psychological biases that19). are usually associated with judgmental forecasts and

Oddly, the discussion frequently meanders into how to overcome them. Instead, it confines itself to aside issues which are then discussed in unnecessary brief treatment of techniques like Delphi, cross-im-detail. For example, when introducing the simple pact analysis and scenario writing.concept of ‘‘ruler’’ forecasts, where ‘‘ judgment and All of this is a pity since, despite the aboveeyesight’’ are used to obtain a rough estimate of the omissions, the book’s coverage is broad. In additiontrend and an ‘‘envelope’’ for the time series pattern, to the topics referred to earlier, it discusses thea long detailed paragraph suddenly appears on the application of spreadsheets and other software (Mi-problems of transferring graphical output between crosoft Excel and Minitab are the main packagespackages like Minitab, Corel Draw, Wordperfect and used), how to search the internet for data and how toAdobe Illustrator. write a report about a forecasting application –

One would expect that a text that is aimed at though this deals mainly with how to mark studentbusiness managers and students would be packed assignments. Techniques covered include exponen-with business data and examples. Of course, one can tial smoothing methods, ARIMA models, econo-argue that most variables will be at least indirectly of metric models (albeit briefly), combining forecasts,interest to business, but ‘‘Quarterly traffic fatalities growth curves and neural nets (though the chapter onin Canada 1975–1997’’ (a series which is used to this is only 4-pages long). There are also nuggets ofexplain almost all of the forecasting techniques in the good advice and common sense waiting to be foundbook), weather data from Australia and the USA, by the reader who has the determination to stay with‘‘weeds per square metre’’ and the ‘‘population of the text.Calgary 1976–1997’’ are hardly ‘‘mainstream’’ busi- Given the difficulties that I believe that mostness variables. Indeed, in the nineExamples of managers would have with this book, I am surprised

534 Book reviews / International Journal of Forecasting 19 (2003) 521–541

that it was published in its current form and with its The book is organised into four chapters, coveringmanagement orientated title. I wonder how many ‘‘Statistics in the Life and Medical Sciences’’,practising managers or management lecturers were ‘‘Statistics in Business and Social Science’’, ‘‘Statis-asked to read through the manuscript and to give tics in the Physical Sciences and Engineering’’ andtheir opinions before a decision was made to publish. ‘‘Theory and Methods of Statistics’’. Each chapterIf heeded, such opinions would, I am sure, have led consists of a collection of the vignettes on a largeto major changes and improvements in the book’s variety of themes. The vignettes originally appearedstyle and structure. Sadly, in the absence of these in the Journal of the American Statistical Associationchanges, the book falls a long way short of its in 2000. This explains why the authors are pre-well-intentioned objective. dominantly American academics, though I do not

believe that this has given rise to a slanted view ofthe discipline. Rather, the view is a multifaceted

R eferences presentation of the state of statistics. This is both astrength and a weakness: each of the vignettes is

Armstrong, J. S. (2001).Principles of Forecasting, Boston: around four pages in length, which means there is noKluwer Academic Publishers. chance to develop a theme in depth, but there a lot of

Bowerman, B. L., & O’Connell, R. T. (1979).Time Series and different views presented about the state of statistics.Forecasting. North Scituate, Mass: Duxbury Press.

This is not, therefore, a book that will teach you a lotof statistics. However, given the breadth of the views

Paul Goodwinpresented and the lists of references (each vignette

The Management Schoolhas around 2 to 3 pages of references) it is a book

University of Bathwhich could be used as abasis for learning alot of

Bathstatistics.

UKThe picture presented is one of enormous pro-

gress, from a subject where ‘‘development wasdoi:10.1016/S0169-2070(03)00021-9

largely driven by applications in a small number ofareas (astronomy, official statistics, agriculture)’’ toone which has become central to many disciplineswhich involve numerical (and non-numerical) data.

Statistics in the 21st Century, Edited by Adrian E. The optimistic viewpoint even extends to a belief inRafferty, Martin A. Tanner and Martin T. Wells, the emergence of a unified foundation for statistics,Monographs on Statistics and Applied Probability, beyond the frequentist /Bayesian divide!Chapman and Hall /CRC (2002), Paperback, £24.99, There is much interesting material in the book on$39.95, ISBN 1-58488-272-7. the development of computer intensive approaches to

statistics, including Markov chain Monte CarloI was persuaded by the Book Review Editor to (MCMC), the bootstrap, and the EM algorithm.

review this book on the grounds that it would be MCMC gets 27 pages referenced in the index alone,very easy to do, as there were only two chapters on without counting references to the Gibbs sampler ortime series. This did not prove to be the whole story the Metropolis–Hastings algorithm. I particularlyas I found out when I looked at the index for time liked, in reference to computer-intensive techniques,series topics. The book’s title is not really accurate; Bradley Efron’s observation that statistical meth-it is more a series of snapshots of the state of odology always expands to strain the current limitsstatistics around the turn of the century. These of computation!vignettes both look back at the achievements of the There is a wide range of areas of application oflast century and also look forward to where the time series techniques given in the book. Theseauthors see the subjects developing. This makes for include environmental statistics, political science,fascinating reading to anyone with an interest in seismology, internet traffic data, decoding of digitalstatistics. phone messages and statistical process control.