282 Book reviews

to forecasters. By and large, the rate of diffusion is found to be positively related to the number of firms in the industry, but negatively related to size differences. While such findings may not affect technological forecasts in the short- to medium- term for single markets, such ideas could be useful when considering regional sub-markets or diffu- sion in an industry in different countries.

A declared aim of the series is to prevent the ' Balkanization' of economics, and the authors have covered a lot of ground in providing a unified treatment of the subject matter. Those interested in technological forecasting will find some familiar names such as Mansfield interspersed through the text but, by and large, the forecasting literature and related issues are not discussed. Nevertheless, this monograph can be recommended as a state- of-the-art review of economic theory and practice relating to technological change.

Keith Ord The Pennsylvania State University

Bruce L. Bowerman and Richard T. O'Connell, Time Series Forecasting, Unified Concepts and Computer Implementation, 2nd ed. (Duxbury Press, Boston, 1987) pp. 540.

The focus of this book is on the concepts and applications of time series methods. The authors' approach to time series forecasting is illustrated using the SAS software package, providing the readers with detailed information about the use of SAS. The importance of forecasting for many functional areas (marketing, finance, production, personnel management and process control) is highlighted.

Bowerman and O'Connell introduce the book as a text for applied courses in time series fore- casting and as a reference book for practitioners. I agree that the text is suitable for applied under- graduate and graduate courses. However, I dis- agree that it is a useful tool for practitioners. The text touches only slightly on the practical imple- mentation of forecasting. In this context, other books such as those by Levenbach and Cleary (1984) and Wheelwright and Makridakis (1985) do a superior job.

In addition, practitioners seek user-friendly software that, unlike SAS, do require complex

procedures to use particular/forecasting methods, and which provide easy-to-interpret results. It is very difficult to judge the efficiency and accuracy of a forecasting method from the output of SAS, the authors' chosen software for illustrative pur- poses. For example, SAS provides the actual, fore- cast and error values in the same sequence for each time period, one period after another. This makes it difficult to draw conclusions and to track error over periods of time. Managers prefer soft- ware such as Auto-Box, 4Cast /2, AUTOCAST, ISP, STATGRAPHICS, etc. (see Mahmoud et al., 1986, Mahmoud, 1988). For Bowerman and O'Connell's text to have been appropriate for and attractive to practitioners, software considerations should have been more carefully addressed.

For all readers, a summary table of all SAS procedures with respect to time series methods would be helpful. The table could describe the functions of each procedure (SAC, SPAC, etc).

In chapter 1, the authors discuss the compo- nents of time series. Later, they explain data pat- terns. They do not address stationary data, how- ever. It is essential to explain stationarity before this is referred to in the discussion of the Box Jenkins technique in chapter 2. The authors should introduce stationarity in chapter 1, provide a graph to illustrate it and explain that it will be covered in chapter 2.

One observation is that the authors focus on only a few accuracy measures. This is in contrast to the approaches taken by other authors such as Makridakis et al. (1983) and Mahmoud (1987). It is important to expose the reader to a variety of accuracy measures and include those such as Theil's U-statistic and R 2.

The focus of the text would have been more sharp had Bowerman and O'Connell provided brief background information about both quantitative and qualitative forecasting methods, with exam- ples of each. They should then have indicated that their text addresses, within the group of quantita- tive methods, only a selection of time series meth- ods. This is important for the reader who is new to forecasting. Some exponential smoothing, meth- ods such as Holt's exponential smoothing, were omitted.

The authors introduce the Box-Jenkins tech- nique first. It would be better to begin with simple time series techniques and gradually progress to the more difficult ones. Also chapter 7, which

Book reviews 283

deals with transfer function models, should precede the explanation of the Box-Jenkins tech- nique. A thorough understanding of transfer func- tions is helpful in learning how to use Box-Jenkins.

In chapter 2, in the introduction to the Box Jenkins technique, Bowerman and O'Connell list four steps that must be followed in the use of the technique. It is a pity that these steps are merely listed without any discussion of the impor- tance of each step in defining the appropriate model. An illustrative diagram would also have been useful to the readers. A much better intro- duction is given in Box and Jenkins (1976) where the need to familiarize the readers with the steps is recognized.

Further improvement of the Box-Jenkins ap- proach could have been realized with the addition of a case study of a specific 'data set at the end of the Box-Jenkins chapters. While Bowerman and O'Connell do provide different examples of Box Jenkins models, students and practitioners alike would benefit from a step-by-step approach to the building of a Box-Jenkins technique using real case material. (Compare, for example, the approach used in Makridakis et al., 1983).

Chapter 2 deals with non-seasonal Box Jenkins models supported by SAS output. In chapter 3, the authors cover seasonal Box-Jenkins models and their tentative identification. Chapter 4 dis- cusses estimation and diagnostic checking for Box-Jenkins models.

In chapter 5, Bowerman and O'Connell explain the concepts of time series regression, determinis- tic functions of time to model trend and seasonal effects, the use of seasonal dummy variables and trigonometric terms and growth curve models. These issues are well explained and are important. The authors do not, however, indicate the dif- ferences between time series regression and regres- sion.

It is not until chapter 6 that the authors cover exponential smoothing. The introduction to the topic is very brief and does not include discussion of different exponential smoothing methods and why they are widely used. The authors discuss simple exponential smoothing in detail, showing the development of the general model. They also demonstrate the relationship between exponential smoothing, Box Jenkins, and time series regres- sion. This is well presented.

Chapter 8 cleals with classical regression in

detail. Bowerman and O'Connell provide useful information about model development, identifica- tion of model assumptions, residual analysis, in- fluential observations, and the analysis of a poten- tial model. This chapter is very well-structured.

A final observation is that the text includes a very limited number of ' fur ther references'. The authors also do not refer to other writers' work in the main body of their text. It is important that they consider doing so in the next edition, by providing a list of references and further readings at the end of each chapter.

Essam Mahmoud University of North Texas

References

Box, George E.P. and Gwilym M. Jenkins, 1976, Time series analysis: Forecasting and control, revised ed, (Holden-Day; San Francisco).

Levenbach, Hans and James P. Cleary, 1984, The modern forecaster (Van Nostrand Reinhold Company; New York).

Makridakis, S., S. Wheelwright and V. McGee, 1983, Forecast- ing." Methods and applications, 2nd ed. (John Wiley and Sons, New York).

Mahmoud, Essam, 1987, The Evaluation of Forecasts, in: Spyros Makridakis and Steven C. Wheelwright, eds., 1987, The handbook of forecasting, a manager's guide, 2nd ed. (John Wiley and Sons, New York), 504-522.

Mahmoud, Essam, 1988, "' Review of selected software for sales forecasting and decision support systems," Journal of the Academy of Marketing Science, 16, 104-109.

Mahmoud, E., G. Rice, V.E. McGee and C. Beaumont, 1986, "Mainframe specific purpose forecasting software: A survey", Journal of Forecasting, 5, 75-83.

Wheelwright, Steven C. and Spyros Makridakis, 1985, Fore- casting melq~ods for management, 4th ed. (John Wiley and Sons, New York).

Lloyd M. Valentine, Business Cycles and Forecast- ing, 7th ed. (South-Western Publishing Company, Cincinnati, Ohio, 1987) pp. 572, $36.75.

Business Cycles and Forecasting is a textbook in- tended for college students who are taking courses in economic forecasting. Its author, Professor Lloyd M. Valentine, has very clear notions about the content of this subject. The book is written from the point of view that economic forecasting is an integrated subject built upon an understand- ing of both economic theory and forecasting tech-