International Journal of Forecasting IO (1994) 381-386

Book reviews

Reviews that appear in IJF describe and evalu- ate books about new developments in research on forecasting. They cover theory, practical applications, and methodology.

New books that deal with any of the social and behavioral sciences are reviewed if they contrib- ute to the advancement of forecasting.

Suggestions of books for review are welcomed: please send them to one of the editors listed below.

North and South America: Peg Young Office of Inspector General Dept. of Veterans Affairs 810 Vermont Ave., N.W. Washington, D. C. 20420 USA

Rest of the world: Nigel Meade Imperial College of Science and Technology Department of Management Science Exhibition Road London SW7 2BX UK

Gregory C. Reinsel, 1993, Elements of Multi- variate Time Series Analysis (Springer-Verlag, New York), xiv + 263 pp., DM 88 hardback, ISBN O-387-94063-4.

This book presents the theory underlying the analysis of multivariate time series. It presup- poses a knowledge of univariate time series. The first 150 pages cover all aspects of vector ARMA models including their properties, modelling,

1994 Elsevier Science B.V. All rights reserved

SSDI 0169-2070(94)00522-E

estimation and forecasting, while the last two chapters consider ‘reduced-rank and non-station- ary co-integrated models’ and ‘state-space

models, Kalman filtering and related topics’. The writing style is generally clear though rather concise and not for the mathematically faint- hearted. There are only a few examples using real data and there is little practical guidance in the text. though there is a potentially useful appendix listing seven multivariate data sets. There is also a set of exercises and problems at the back of the book. Overall, this is a useful contribution to the literature for the specialist time-series analyst, though it is clear much remains to be done to make this sort of approach viable for practical time-series analysis. In par- ticular, the author notes (p. 191) that “more research is needed in the application of seasonal time series modeling, especially multiplicative

seasonal models, to vector time series”. It is natural to compare it with Liitkepohl

(1991), which is the only other recent book I know of covering this range of material. The latter covers much the same range of material (there are some differences, of course) in a book of about twice the length (545 pp.) and is arguably more reader-friendly except for those seeking a theoretical reference source.

Anyone who has ever tried to fit a VARMA model will know how fiendishly difficult it is. Some useful advice is to get a good computer package which does as much of the analysis as possible. Alternatively many analysts will choose to simplify matters by restricting attention to single-equation transfer-function models (which effectively assume no feedback from output to the inputs) or to omit MA terms and consider

the simpler VAR models (which get much more space in Lutkepohl, including a brief discussion of Bayesian VAR models wherein priors are used to favour low-order models and prevent over- fitting), or to use an alternative class of models such as state-space models, or to stick to uni- variate analysis! Empirical evidence on the fore- casting ability of multivariate methods, as op- posed to univariate methods, is mixed but is not seriously discussed in either Reinsel or Lutkepohl. Both books are to be regarded as texts on the theory of vector time-series models rather than as guides for the practitioner. and the latter needs clearer guidelines as to when multivariate time-series methods are really worth using.

Chris Chatfield University of Bath, UK

Reference

Liitkepohl, H.. IYYl, Introduction to Multiple Time Series

Analysis (Springer-Verlag. Berlin).

Review of Lahiri, K, and G. Moore (eds.), 1991, Leading Economic Indicators: New Approaches

and Forecasting Record, (Cambridge University Press, Cambridge).

Since the seminal contribution of Mitchell and Burns (1938), the leading indicators approach (LIA) to forecasting business cycles has with- stood the test of time. The simple idea that some business and economic time series lead others has been profitably employed over the last five and a half decades by scores of economists and statisticians. If one is to choose among various methods of business cycles forecasting based on the two criteria of popularity and longevity, the LIA will rise above all others without any serious contenders. The popularity and longevity of the LIA is undoubtedly due to its unmatched fore- casting performance. Even though the leading

indicators’ forecasting track has not been per- fect. the LIA’s forecasting record remains superior to those of alternative methods.

Lahiri and Moore’s Leuding Economic In-

dicators has as its central goal to “systematically explain and evaluate the old and the new emerg- ing techniques dealing with leading economic indicators.” To accomplish this goal, the editors divide up their volume into an introduction and three parts. The first part consists of six chapters that examine new concepts and methods related to leading indicators. The second part of the book focuses on the forecasting record of leading indicators and ways of performance evaluation. The third and the final part explores new econ- omic indicators.

Since Koopmans’ (1947) influential attack on the atheoretical nature of leading indicators approach to business cycle forecasting, most economists have not looked kindly at the leading indicators methodology and its professed method of “measurement without theory”. The first chapter in the ‘new concepts and methods” part is a courageous attempt by de Leeuw to provide a theoretical framework for the use of leading indicators based on Holt et al.‘s (1960) dynamic production theory model. Despite this and simi- lar efforts, it is highly unlikely that the leading indicators methodology will ever find a firm and satisfactory ground in economic theory. In the chapter that follows de Leeuw’s, Salih Neftci offers a statistical rationale for the use of leading indicators. In particular, by assuming that a finite state Markov process adequately represents the state of the economy. Neftci shows that in some situations the information obtained from the index of leading indicators can be interpreted as filtered cycle-generated data. In Chapter 4, Stock and Watson add to the growing literature of the probabilistic approach to leading in- dicators and business cycles forecasting. Stock and Watson offer a single index dynamic factor model that utilizes the assumption that there exists an unobservable variable that captures common elements of comovements of first differ- ences of coincident variables. Stock and Watson show that this single index probability model compares favorably to the Department of Com-