Book reuiews 285

involving judgment and the use of leading eco- nomic indicators. Econometric models (both causal and autoregressive) are mentioned, but summarily dismissed. Chapters 17 and 18 continue the dis- cussion of short-run forecasting of GNP. The mes- sage of these two chapters is that short-run fore- casts are best obtained by applying judgment to the simple-Keynesian model of the economy. At this point, the reader may well wonder why it was necessary to read beyond chapter 8, so far as learning theory is concerned, and whether judg- ment is a technique that can be learned from reading this book, or any book for that matter.

Can the book be recommended? Yes or no. The first eleven chapters are quite good; they can be fruitfully read by anyone. After that, the book tends to lose its coherence and sophistication. Part 3 does not build on part 2, and neither does part 4. Parts 3 and 4 should appeal to those readers who have an aversion to formal economics, statis- tics, and computers. But these are the requisite foundations for economic forecasting in the real world. Econometric forecasting, tempered with judgment, is the state of the art. Purely judgmen- tal forecasts are not taken seriously anymore. Al- most everyone, today, has desktop access to both hardware and software for do-it-yourself forecast- ing. Business Cycles and Forecasting is simply behind the times. It covers the relevant economic theory, but not the relevant forecasting tech- niques.

Dwayne Wrightsman The University of New Hampshire

Jeffrey Jarrett, Business Forecasting Methods (Basil Blackwell Ltd., Oxford, U.K., 1987) pp. 346, $15.00.

Chapter one says that ' the purpose of this book is to introduce a vast array of forecasting methods' so as to 'assist management to plan requirements for marketing efforts, materials, personnel, pro- duction, services' and so on. The book does indeed cover a wide range of methods, both quantitative and qualitative, ranging from simple exponential smoothing right through to multiple time series A R I M A models.

I assume the intended readership consists prim- arily of business and management science stu-

dents. This being so, it may not appear obvious that this writer, a statistician, is the most ap- propriate choice as reviewer. However, forecasting is an interdisciplinary activity and I regard it as very important that the work of statisticians, management scientists and economists should be subject to reciprocal scrutiny. It can be very dif- ficult for writers to span the whole field from the statistical and econometric analysis of time series to qualitative and judgemental forecasting, and the management problems involved in implement- ing forecasts. The author of this book appears to be more at home in the management science aspects of forecasting, as one would expect, but a large part of the book is taken up with the details of time series methods. Thus a statistical appraisal seems desirable.

After the introduction, there are chapters on smoothing methods and classical time-series de- composition methods. This material is presented in a straight-forward, rather cook-book style which I expect students will generally like. My only quibbles here are to suggest that there are better ways of motivating exponential smoothing than that given in the footnote on page 20, and that there are more efficient ways of finding smoothing parameters than by trial-and-error.

Chapter four on regression makes an unfor- tunate start since the X-variable is missing from the basic linear regression equation (equation 4.1) and is repeated incorrectly on page 106. In the list of assumptions for the regression model, I cannot see the necessity for the second assumption which says that the same distribution of X-values will appear in any replication of the experiment. This assumption is not given in standard regression texts and is, I think, confused with the fact that inference for a given sample is conditioned on the X-values being fixed. Unfortunately, the accompa- nying discussion is obscure because the reference to fig. 3.1 should, I think, be fig. 4.1, but then the stated range of mortgage loans is incorrect. From hereon the misprint rate starts to cause real prob- lems for the reader and there is further confusion in chapter four with algebraic slips in equations (4.3), (4.9) and (4.11), while fig. 4.2 does not give a key to the symbols.

On page 99 the book starts to show some of the misconceptions about significance tests which per- meate the non-statistical literature. Null hypothe- ses are sometimes written as 'There is no signifi-

286 Book reviews

cant relationship' rather than 'There is no rela- tionship', while significant values are taken to prove the alternative hypothesis (page 99, line 7) rather than to give evidence in favour of the alternative hypothesis. On page 103, lines 5/6 , the interpretation of the t-value is ' round the wrong way', while on page 99, line 6, the word 'com- puted' should be 'critical'. These comments might be seen as quibbles about semantics, but they are ones which statisticians regard as very important.

In the chapter on multiple regression, I note that equations (5.5), (5.6), (5.8) and (5.16) contain misprints and that the second and sixth assump- tions are respectively unnecessary or incorrect. I also note that many statisticians prefer the term 'explanatory' or 'predictor ' variable to ' indepen- dent variable' since the X-variables are likely to be correlated, rather than independent, as the book itself notes.

Chapter 6 on econometric models is outside my expertise, but part IV of the book is on 'Advanced Topics in Time Series Analysis'. Here the misprint a n d / o r error rate becomes rather alarming, and I fear this section could create much confusion in its present form. I can only give a few examples. On page 187 we read that "The ARIMA (0, 0, 0) series has a mean that varies over time. The (0, 1, 0) series is one that is stationary in the mean". These two statements are the wrong way around. Equa- tion (7.16) is erroneously repeated as (7.17) and the following sentence is a non sequitur. Other misprints occur in equations (7.18), (7.27-29), (7.26) on page 211, (7.30), (7.39), (7.41), (8.11), (8.25), (9.18), (9.22), (9.23), (9.31), (9.33), (9.34), (11.1), (11.16), (11.17), (11.20), and (11.22). As an author myself, I know how difficult it is to remove all errors, but this level seems unacceptable. There are more misprints in the text. For example, 'ha ts ' appear or disappear over mathematical quantities at random and subscripts also appear and disap- pear or change. Figures 9.2 to 9.4 are poorly labelled, appear to be in the wrong order, and some of the graphs appear to have wrong signs. The cross-referencing is imperfect. Thus on page 195, example 7.2 should be 2.7, table 7.2 should be 2.9, and equation (7.15) should be (7.18). On page 197, the author says that the "Brown model eliminated the trend completely and produced a residual for which no statistically significant pat- tern in the residual was present", even though all the residuals are positive. On page 239, we read

that for an AR(0) process, the parameter ~ will have a value that is not significantly different from zero. And so on. In a book at this level, it is reasonable to write down many formulae without derivation, but this means that some equations, like equations (11.8) to (11.11), are difficult to check, particularly when some quantities are unde- fined. For example, I do not know what S F is in equation (11.10).

It is difficult to know how to conclude. This reviewer may be accused of statistical pedantry, but if nothing is said then I fear that Statistics will take the blame when readers find themselves con- fused. I cannot recommend the book at least until the misprint rate is reduced.

Chris Chatfield University o f Bath, U.K.

Berndt Brehmer, Helmut Jungermann, Peter Laurens and Guje Sevon, eds., New Directions in Research on Decision M a k i n g (Elsevier Science Publishers, Amsterdam, 1986) pp. 443, $105.

In principal, research in judgment and decision making should be valuable to forecasters. Such research could help improve the quality of judg- mental inputs needed for quantitative forecasting methods. Research findings on judgment should have direct application to judgmental forecasts. Improved understanding of decisional processes should lead to improved modeling of decisions that are the subject of forecasts, e.g., buying decisions in a market forecast, labor force par- ticipation decisions in a human resource forecast. Better understanding of decision making processes can enhance the chances that the forecast will be used, not ignored or misused by a decision maker.

In practice, only some of the papers in New Directions in Research on Decision Mak ing are apt to be useful to the forecaster. This book includes revised papers originally presented at the tenth research conference on Subjective Probability, Utility, and Decision Making, held in Helsinki, Finland in 1985. The goal of the conference's organizing commit tee was " t o make more determined efforts to consider the larger picture, and to promote the development of general the- ory" (p. 1), rather than to "help people make better decisions" (p. 1), a concern of much of the