176 Book reviews / Journal of Forecasting 12 (1996) 175-182

innovations outliers (IO) and additive outliers (AO) and then describe procedures for detecting each, in the context of ARIMA models. Robust estimation procedures are considered for ARIMA models and in the frequency domain, the emphasis being on AO, which are much more troublesome. The discussion of state space models is much more limited and, surprisingly, there is no mention of the Harrison-Stevens multi-state model, even though that model was designed to accommodate various types of out- lier. The chapter ends with an excellent visual warning. A simulated series is plotted with what appears to be three clear outliers. In reality, the underlying model is non-linear and the series CONTAINS NO OUTLIERS (authors's capi- tals). Clearly, much has been done in the world of outlier detection and accommodation, but we still have a way to go.

The authors write well and have succeeded in keeping the material accessible to a reader with an intermediate background in statistics. The text is spiced with a variety of interesting exam- ples and full references are provided on each topic. The third edition will continue the tradi- tion of its predecessors as the standard reference on outliers. I would strongly recommend the book to anyone worried about outliers in their data; if you are not worried, perhaps you have even greater need of it!

Keith Ord Department of Management Science and IS,

The Pennsylvania State University University Park, PA,

USA

S S D I 0169-2070(95)00625-7

M. Metcalf, 1995, Forecasting Profit, (Kluwer Academic Publishers, Boston), US$110, ISBN 0-7923-9482-8.

By the author's admission, this book; "is not an advanced mathematical methods book, so computation details regarding the finer points of regression, ARIMA, neural networks, or chaos

theory should be sought elsewhere (xv)." This book is, however, many other things. Hard as it is to imagine of a book on this subject, it is enjoyable reading. It contains many pithy ex- pressions, and it deals with a manifold of issues pertinent to forecasting which are much broader than forecasting profit. Indeed, there is nary a mention of the word profit for approximately the first 100 pages of the book. On the down side, the book is fraught with typographical, gram- matical and occasionally logical errors: it some- times references materials that are not in the reference list or the mis-referenced (e.g., by the wrong year); and its analysis is sometimes shal- low.

The book is essentially in two parts. The first part is very enjoyable reading, perhaps because it has little to do with forecasting profit per se, or perhaps because it is of a non-technical nature. Nevertheless, the issues raised in the first part of the book are very pertinent to the issue of forecasting profit, and many of them are often ignored by 'experts' on the subject. The first six chapters of the book respectively deal with the issues of reporting a forecast (e.g., convincing strategies; the report as feedback), types of evidence (e.g., multiple methods; judgmental vs. quantitative), an historic classification of forecast methods, groups forecasts, question instruments, and interviews. Both practitioners and academics will learn a lot from this material.

The second part is more technical, but for the technician, not technical enough. The reader who is well versed in methods will find this part of the book to come up a bit short. However, in the author's defense, the book is geared to those with a modicum of technical knowledge regard- ing the subject matter. These readers will learn a lot more than readers who have seen much of this material in a more technical form elsewhere. Nine chapters constitute the second part of the book, namely extrapolation, causal models, bootstrapping and expert systems, deterministic models, eclectic methods, bankruptcy forecasts, profit forecasts, disclosure, and evaluating fore- casts.

The book has much to offer. As one example, the author emphasizes that being accurate is not the only reason to make a forecast, and he offers

Book reviews / Journal of Forecasting 12 (1996) 175-182 177

good reasons to back up his view. I agree with the author that many people focus too much on forecast accuracy, and ignore its potential im- pact. However, I disagree with his conclusion that: "The profit-forecasting accounting litera- ture has concentrated on accuracy rather than information conten t . . . " (p. 267). There are a plethora of studies on this subject, as discussed in my survey paper which was published in this Journal in November 1993. To provide a taste for this literature, assume that: (1) the decision context is to maximize the value of one's invest- ment portfolio; (2) stock prices reflect the con- sensus analyst earnings expectation prior to an earnings announcement; (3) stock prices reflect the reported earnings number after the announcement. In this scenario, one wants to be able to predict earnings better than the consen- sus estimate so that s/he can trade in advance of the earnings announcement and profit. In this scenario accuracy is of prime importance because earnings surprises (reported earnings minus the consensus analyst expectation) does have infor- mation content.

As another example of what the book has to offer, the author does not argue that any one forecast method is best. This is an important point because many individuals (often with ves- ted interests) wish to convince others that their method is best. Again the advice throughout this book that one should not put all of one's faith in any one method is wise.

The flavor of the book can best be seen by the author's summarization (xxii): "If you want to know what this book says rather than why. . . "

(1) Get into a group of about five and do your own forecasts.

(2) Use a wide range of "evidences;" do not become obsessed with building a sophisti- cated mathematical model.

(3) Measure success by the impact of the forecasts, not only its accuracy.

(4) Most important: buy this book for those who do not do the above!

I highly recommend the book for both prac- titioners and academics. I read it from cover to cover, and reread some materials in the first few chapters, if for no other reason than that it was enjoyable reading.

Lawrence D. Brown Samuel P. Capen Professor of Accounting

State University of New York at Buffalo Buffalo

N Y USA

SSDI 0169-2070(95)00630-3

Mario Baldassarri and Paolo Annunziato, eds., 1994, Is The Economic Cycle Still Alive? (St. Martin, New York), US$79.95, ISBN 0-312- 10380-8.

This book is a collection of ten essays, nine of which are written by Italian economists. The question posed by the title will strike many readers of the International Journal of Forecast- ing as rhetorical at best. Actually only Innocenzo Cipoletta in the last essay even deals with the question in more than a passing manner. He suggests that the oil crisis of the 1970s "belied the death of the business cycle" and that R.E. Lucas in his New Classical macroeconomics "rehabilitated" the cycle. This will be news to the many economists who have been concerned continuously for many years to develop cyclical explanations and improved measuring and fore- casting techniques- from J.R. Hicks' theories of the trade cycle in the early 1950s to the intensive work on indicators, which has never really slowed down, but took on renewed life in the early 1960s when the Department of Commerce began publishing the monthly updates of the National Bureau's indicator system. Further, the econometric work of the past quarter century, reported in many of the other essays in the book, speaks of the relatively unflagging interest in explaining business cycles.

It is true that the long expansion of the 1960s led to a conference in London in 1967 entitled "Is the Business Cycle Obsolete?", but none of the attendees really believed the answer was in the affirmative, and the relatively severe reces- sion which gripped many industrial market