580 Book reviews

for a marketing manager with little or no ex- perience in marketing research or forecasting.

Section I: Market research, Chapters 1-8

The first section of the book, Chapters 1-8, deals with marketing research. Chapter 1 dis- cusses the relationship of marketing analysis with marketing research. It is a very broad brush approach defining such marketing concepts as customers, product mix, price, promotion and how they can be dealt with by marketing re- search. Chapter 2 gives a brief overview of the analysis of a new product potential. Chapter 3 discusses the need for market research, and Chapter 4 provides an overview of market re- search. In Chapter 5, a statistically based discus- sion of the size and representativeness of a market research sample is developed. In Chapter 6, a presentation of the market research process in an industrial setting is given, which includes the design and implementation of surveys in the industrial setting. A basic framework of con- sumer market research is given in Chapter 7. Such topics as market research for new and for existing products, as well as the design and implementation of a questionnaire, are dis- cussed. Finally, in Chapter 8, a discussion of market research is given for the export market. Given the new globalization of industry and markets, this chapter could be particularly valu- able. However, the coverage of this chapter is

severely limited.

Section II: Forecasting, Chapters 9-11

Chapter 9 presents an elementary intro- duction to forecasting techniques for demand analysis for existing products. The treatment discusses the use of simple one-variable linear models, logarithmic and Gompertz models. However, the reader would not have sufficient background to make use of this material in many

. realistic situations. Chapter 10 presents an over- view of the concepts of total or industry forecast- ing. These forecasting techniques are limited to a yearly forecasting of demand. Chapter 11 looks at market share forecasting and sales forecast monitoring, also in a restricted fashion. This

section of the book is the only one in which there is a direct and distinct relationship to forecasting business activities. However, this material, for the most part, can be found in almost any book on forecasting in a fashion which is much more suitable for actual use.

Section III: Implementation, Chapters 12-14

Chapter 12 presents a cookbook-type ap- proach to writing a marketing research report to management. Chapter 13 gives the reader a guide on whether to employ an outside agency or in-house personnel to perform marketing re- search. Finally, Chapter 14 discusses the prob- lem of whether to use in-house resources or agencies in performing research. These chapters are at best tangentially related to forecasting.

Final review

The book is severely limited in its usefulness to persons interested in forecasting. The majori- ty of the book is an overview of the process of marketing research and its actual use. The sec- tion on forecasting uses material which is quite basic and limited in nature. The greatest contri- bution of the book to forecasting is to present the very basic concepts and applications of forecasting and market research to the non-tech- nical audience who become involved in the use of marketing research and forecasting as an integral part of the development of business strategies and tactics.

Kenneth D. Lawrence New Jersey Institute of Technology

North Brunswick, NJ, USA

Svend Hylleberg, Ed., Modelling Seasonality, (Oxford University Press, New York), 476 pp., US$75.00 hard cover (ISBN 0-19-877317-X), US$35.00 paperback (ISBN O-19-8773188).

The modelling of seasonality in data is an issue that is of great importance to both researchers

Book reviews 581

and practitioners in the field of forecasting. In business and economic applications, we are often confronted with data that vary on a seasonal basis. Sales, power usage, ambient temperatures, and food prices are some obvious and important examples of seasonal data, along with many of the economic indicators published by various governments. Given the prevalence of interest- ing data that are seasonal, a pertinent question is how to account for the seasonal variation in the process of using the data for modelling and forecasting.

Researchers are not in agreement on how best to handle the seasonality in data. Essentially, the controversy is related to whether forecasts should construct models using data that have been seasonally adjusted, or include the seasonality in the specification of the model and use unadjusted data.

Seasonal adjustment represents an attempt to filter out the seasonal component of the data to permit the modeller to concentrate more fully on the other signals in the data. Particularly for government-published economic indicators, seasonally adjusted data are arguably easier for the general public to interpret. If nothing else, people are accustomed to the use of indicators that have been adjusted for seasonality. How- ever, the arguments against the use of seasonally adjusted data are strong. These include an over- simplification of the nature of the seasonal effect and the loss of information resulting from the tendency for many adjustment procedures to overadjust, removing more than just the season- al effect. The use of seasonal adjustment pro- cedures can result in a distortion of the nature of the relationships among variables, which can clearly have serious consequences for multi- variate modelling. These drawbacks lead many statisticians and econometricians to advocate the superiority of an approach based on the incorpo- ration of the seasonal component into the specifi- cation of the model used for forecasting.

Modelling Seasonality, edited by Svend Hyl- leberg, is a collection of some of the most important published work on the topic of model- ling with seasonal data. The selection of 18 works represents an interesting combination of recent research and an historical perspective on the subject.

The book is organized into five sections, each

of which consists of several papers, and is pre- faced by an introduction. Part I provides an overview of many of the fundamental issues associated with modelling seasonal data, includ- ing a general introduction and works by Hyl- leberg (1986), Wallis (1974), Sims (1974), and Bell and Hillmer (1984). In Part II, articles by Crutchfield and Zellner (1962), Ghysels (1988), Osborn (1988), and Miron and Zeldes (1988) are combined to address the development of an economic theory of seasonality. Works by Hyl- leberg (1986) and Burridge and Wallis (1984), which describe existing approaches to seasonal adjustment, constitute Part III. Part IV deals with advances in model-based procedures for dealing with univariate seasonal data, and in- cludes papers by Engle (1978), Hillmer and Tiao (1982), Harvey and Todd (1983)) Maravall and Pierce (1987), and Burridge and Wallis (1990). In Part V, issues associated with expanding upon the univariate approach, through seasonal inte- gration and cointegration, are addressed in pa- pers by Dickey et al. (1984); Hylleberg et al. (1990); and Osborn et al. (1988).

With the exception of the introductions that preface the sections, the material that appears in Modelling Seasonality has been published previ- ously, most of it in the leading journals in the fields of statistics and economics. Although I am certain that most researchers with strong inter- ests in forecasting and time series will have seen the majority of these papers in their original publications, I am left with the impression that this book represents a situation in which the whole is more than the sum of its parts. Essen- tially, the value-added represented by Modelling Seasonality is in the logical ordering and group- ing of the works, along with editor Hylleberg’s commentary. The book provides a coherent collection of some of the most influential re- search in the area of modelling seasonal data, along with a comprehensive set of references for researchers and students with interests in the field.

This is not a book that is intended for the average user of seasonal data, or even for relatively casual practitioners. Modelling Seasonality is definitely not a “how-to” book. In fact, the mathematical sophistication required to deal with most of the papers is quite high, as one would expect from papers that have appeared in

582 Book reviews

journals such as Econometrica, Journal of the American Statistical Association, Journal of Busi- ness and Economic Statistics, and Journal of Econometrics. However, the book could form the basis of an interesting doctoral seminar on seasonality, and will serve as a valuable refer- ence for researchers who are interested in having ready access to many of the influential writings on this topic, in one place, logically grouped.

Elizabeth Rose University of Southern California

Los Angeles, CA, USA

References

Bell, W.R. and S.C. Hillmer, 1984, “Issues involved with the

seasonal adjustment of economic time series”. Journal of

Business and Economic Statistics. 2, 291-320.

Burridpe, P. and K.F. Wallis, 1984, “Unobserved-compo-

nents models for seasonal adjustment filters”, Journal of

Business and Economic Statistics. 2. 350-359.

Burridge. P. and K.F. Wallis, 1990. “Seasonal adjustment

and Kalman filtering: Extension to periodic variances”,

Journal of Forecasting, 9, 109-118.

Crutchfield. J. and A. Zellner, 1962, “Analysis of port

pricing of halibut: Theoretical considerations and empiri-

cal results”, in: Economic Aspects of Halibut Fishery,

Washington, DC, US Department of the Interior.

Dickey, D.A., D.P. Hasza and W.A. Fuller, 1984, “Testing

for unit roots in seasonal time series”, Journal of the

American Statistical Association. 79, 355-367.

Engle, R.F., 1978, “Estimating structural models of

seasonality”, in: A. Zellner, Ed., Seasonal Analysis of

Economic Time Series, Proceedings of the Conference on

the Seasonal Analysis of Economic Time Series, Washing-

ton, DC, 9-10 September 1976, Washington, DC. US

Department of Commerce. Bureau of the Census, 281-

295.

Ghysels, E., 1988, “A study toward a dynamic theory of

seasonality for economic time series”, Journal of the

American Statistical Association. 83, 168-172.

Harvey, A.C. and P.H.J. Todd, 1983, “Forecasting economic

time series with structural and Box-Jenkins models: A

case study”. Journal of Business and Economic Statistics,

1, 2999307. Hillmer, S.C. and G.C. Tiao. 1982, “An ARIMA-model-

based approach to seasonal adjustment”, Journal of the

American Statistical Association. 77, 63-70. Hylleberg, S.. 1986, “The historical perspective” and “The

X-11 method”, in: S. Hylleberg, Ed., Seasonality in

Regression (Academic Press, Orlando), 7-14 and 89-93. Hylleberg, S.. R.F. Engle, C.W.J. Granger and B.S. Yoo,

1990, “Seasonal integration and cointegration”, Journal

of Econometrics, 44, 215-238. Maravall, A. and D.A. Pierce, 1987, “A prototypical season-

al adjustment model”, Journal of Time Series Analysis, 8, 177-193.

Miron, J.A. and S.P. Zeldes, 1988, “Seasonality, cost shocks,

and the production smoothing model of inventories”,

Econometrica, 56, 877-908.

Osborn, D.R., 1988, “Seasonality and habit persistence in a

life cycle model of consumption”, Journal of Applied

Econometrics, 3, 255-266.

Osborn, D.R.. A.P.L. Chui, J.P. Smith and C.R. Bir-

chenhall, 1988, “Seasonality and the order of integration

for consumption”. Oxford Bulletin of Economics and

Statistics, 50, 361-377.

Sims, C.A., 1974, “Seasonality in regression”, Journal of the American Statistical Association. 69, 618-627.

Wallis, K.F.. 1974, “Seasonal adjustment and relations

between variables”, Journal of the American Statistical

Association. 69, 18-31.

Andrew C. Harvey, 1993, Time Series Models, Second Edition (Harvester-Wheatsheaf, New York) xviii + 308 pp., 514.99 (paperback), ISBN 0-7450-1200-O.

It is hard to believe that more than 10 years have passed by since the first edition of this book was published. As the author remarks in his new preface, there has been a plethora of research in time series during this period, and the moment was surely ripe for an updated version to accom- modate these advances.

There are eight chapters in all, including and essentially repeating from the first edition, the Introduction, chapters on stationary stochastic processes, estimation and testing of ARMA

models, state space modelling, and frequency domain aspects of time series. There are some changes in the contents of these reflecting recent developments, which are introduced appro-

priately. Multivariate interests previously scattered are

now collected into one chapter, which seems the logical step to take and has in addition a discus- sion on co-integration. A new chapter on time series models is highly commended concentrating on the ARIMA and structural approaches, together with long memory models, and how to deal with explanatory variables, and interven- tions.

The recent research on non-linear models is subject to an interesting appraisal in Chapter 8,