Book reviews 569

state-space models, VARMA models and some

non-linear models. The chapter on forecasting discusses both uni-

variate and multivariate procedures. The compara- tive review of forecasting procedures is certainly useful from a practical point of view. The book also considers the analysis of real data and at several places the author shares his experiences with the reader in dealing with some practical issues that usually arise in time series analysis.

The list of references given in the end is fairly comprehensive and includes some recent literature on the subject. Some references such as Mann and Wald (1943) Holt (1958) Winters (1960) Yule, and Wold are cited in the text but are missing from the reference list.

The book provides more depth than some other introductory level books such as Cryer (1986) or Bowerman and O’Connell (1987). The coverage is not as wide as Shumway (1988) but the mathe- matical level needed is approximately the same.

Overall, I find the material to be well organized and well presented. The error rate appears to be low. The author is successful in providing an introductory understanding of various topics. His treatment of various topics may not be very rigor- ous or deep but appropriate references giving more details are provided to the reader.

Prem P. Talwar University of Alberta

Canada

References

Bowerman, B.L. and R.T. O’Connell, 1987, Time Series Fore-

custing (Duxbury Press, Boston, MA). Cryer, J.D., 1986, Time Series Analysis (Duxbury Press, Bos-

ton, MA). Shumway, R.H., 1988, Applied Statistical Time Series Analysis

(Prentice-Hall, Englewood Cliffs, NJ).

J. Holton Wilson and Barry Keating, Business Forecasting (Richard D. Irwin, Homewood, IL), 1990, pp. 386, $45.95.

Business Forecasting by J. Holton Wilson and Barry Keating is a fine introduction to the essen-

tials of forecasting. In just 386 pages, the reader learns how to forecast using moving averages and exponential smoothing, simple and multiple re- gression, time-series decomposition, ARIMA models and simulation. The book is capped with suggestions about how to combine forecasts to achieve even better results. In direct and clear prose, the authors present a balanced and useful guide to standard forecasting methods that will be useful in either graduate or undergraduate busi- ness courses at a wide variety of schools. I heartily recommend this book. Now, some details.

Chapter 1 gets the text off to a running start with an overview of the major types of forecasting methods that are available today, from subjective methods like customer surveys to data based methods like simple naive models. The authors apply the naive models to retail car sales, im- mediately setting the practical tone for the rest of the book. In each of the remaining chapters, as new models are introduced, they are again applied to forecasting retail car sales. This is good teach- ing, for it generates suspense, interest and a feel for the strengths and weaknesses of each approach when applied practically.

Chapter 1 introduces SORITEC, a software package that has been adapted for the text. Al- though the software was not available at the time of my review, every chapter of the text contains specific instructions about how to use SORITEC to implement the forecasting methods. Printout

from SORITEC appears throughout the book too. My impression is that the package is easy to use. The text ably integrates model presentation with a virtual manual on how to use SORITEC to apply the models. The amateur forecaster should be able to make rapid progress toward sophistication by reading the text and using SORITEC.

Chapter 2 reviews basic statistical concepts such as descriptive statistics, the normal and t-distribu- tions, sampling distributions, hypothesis testing and correlation, The text presents specific and detailed commands from SORITEC so that the student can generate histograms, scatter plots and basic statistics. The chapter ends, as do the re- maining chapters, with a problem set that provides ample opportunity to practice. The problem sets look well thought out and useful.

Chapter 3 introduces moving averages and three forms of exponential smoothing: simple, Holt and Winters. The presentations are smooth, clear and

570 Book reviews

complete. We are shown all the data and graphs of the data. I especially like the coverage of trend and seasonality.

Chapters 4 and 5 introduce regression models and apply them to several economic time series including real personal disposable income, motor vehicle and parts sales, private housing starts and retail car sales. Problems such as autocorrelation, multicollinearity, seasonality and diagnostics such as the Durbin-Watson statistic are discussed in enough depth to give them real meaning. The problem of forecasting the economic variables that explain other economic variables is not shirked. For example, motor vehicle sales are explained by contempor~eous personal disposable income so that forecasts of personal income are needed in the regression model to produce forecasts of mo- tor vehicle sales. The authors use Halt’s exponen- tial smoothing forecasts of personal income in the regression. These chapters also skillfully introduce dummy variables to account for seasonality, lagged regressors and nonlinear variables. Finally, com- mon sense wisdom on how to construct sensible causal economic models is sprinkled throughout the exposition.

The next topic, time-series decomposition, ex- amines quarterly private housing starts in detail, leading the reader step by step through methods of deseasonalizing data, finding the long-term trend and measuring the cyclical component. Ultimately, a multiplicative time-series decom- position forecast is constructed for this series and for retail car sales. Those of us who have con- centrated on ARIMA models to the exclusion of these more traditional models, will find this treat- ment instructive and useful.

Box-Jenkins ARIMA models appear in Chapter 7. I wondered how such a complex topic could possibly be treated in a single chapter cover- ing 55 pages. The answer: stick to the essentials, avoid unnecessary notation and fine points, and employ the clever teaching device of starting with a white noise time series and generating observed time series from it by imposing moving average and autoregressive models upon the white noise terms.

The treatment of Monte Carlo simulation in Chapter 8 is the briefest in the book. SORITEC is not used for this application. Rather, the authors suggest using spreadsheets that have random num- ber generator functions. The example of a service

time frequency distribution is well done, but would be even more useful is specific instructions were included for using the random number generator function.

Chapter 9 is a brief introduction to the combin- ing of forecasts. The authors present three weight- ing schemes, but do not compare them or apply them in a thorough way to the forecasts of retail car sales. They choose two forecasts - multiple regression and Winters’ exponential smoothing - and compute combinations with varying weights, showing the benefit of combining forecasts. This topic is important enough to deserve a fuller dis- cussion both of the pros and cons of the alterna- tive weighting schemes and of the practical prob- lems involved in combining forecasts.

The last chapter of the book steps back from specific forecasting methods to present an over- view of the entire forecast process and a discus- sion of expert systems in forecasting. This chapter introduces some of the practical considerations concerning time, cost, data availability, nature of user, etc. that condition the optimal choice of a forecasting method.

I repeat my positive recommendation of this book: this is a fine starting point to learn how to apply the essential forecasting methods that are now in wide use.

Michael S. Rozeff SUNY - Buffalo, New York, NY, USA

Malcolm Abrams and Harriet Bernstein, Future Stuff (Penguin Books, New York, 1989), pp. 300, $8.95.

I first heard of this book when I was asked to debate its authors on the radio. They refused and so did I. I ran across it again when an executive MBA student gave me a copy figuring I would want to keep up with the latest literature. The student was surprised to learn of my negative first impressions. After reading the book, I have not changed my opinion.

Future Stuff predicts more than 250 new prod-