Book Reviews

Forecasting Economic Time Series. C.W. J. Granger and Paul Newbold. New York: Academic Press, 1977; pp. xii + 333. $24.25.

This book attempts to bridge two gaps. The first is the gap between the theory and application of time series forecasting. The second gap is that which exists between the approach of the time series analyst and the traditional econometric approach to economic forecasting. In the opinion of this reviewer, the authors have been at least partially successful on both counts, although the reader remains acutely aware of the need for more bridge building, particularly regarding the second gap.

The first four chapters of the book present the theory necessary for modelling and forecasting univariate time series. Included is a brief sur- vey of spectral analysis (Chapter 2), which is not crucial to the rest of the book. Chapter 5 discusses the practical application of three classes of univariate time series forecasting techniques. These include the Box- Jenkins techniques, several exponential smoothing procedures, and the method of stepwise autoregression. Also in Chapter 5, Granger and New- bold report the results of recent studies comparing the forecast accuracy of these several techniques.

Chapter 7 discusses the modelling and forecasting of multiple time series with practical emphasis on bivariate series. Here the Box-Jenkins techniques are extended with particular attention paid to “causal’ rela- tionships. The analysis of bivariate time series is investigated in the con- text of series which exhibit one-way causality as well as those which are characterized by feedback. The final chapter, Chapter 9, briefly considers the relaxation of the assumptions of stationarity, linearity, and normality.

These seven chapters constitute the exposition of time series analysis proper. It is the remaining two chapters, however, which will make this book of particular interest to macroeconomists. In these chap- ters the problems of forecasting with traditional econometric models is discussed. Chapter 6 presents the theory and practice of forecasting from both single and simultaneous equation models which are based on eco- nomic theory. Chapter 8 is clearly the most stimulating chapter of the book. Here the authors discuss methods of combining forecasts of the same economic variable as well as the important problem of how to evaluate forecasts, Several recent studies which examine the forecasting performance of certain large econometric models in comparison with the forecasting performance of “naive” univariate time series models are also reviewed.

Throughout these chapters Granger and Newbold are critical of current econometric practice for a number of reasons. Several of these are important enough to enumerate here:

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Book Reviews

(1) While economic theory generally suggests which variables are related in a particular application, only in rare cases can we rely on theory to specify the time series structure of the error terms. Since such specifi- cation is crucial for consistent and efficent estimation and forecasts, we must therefore extract the appropriate information from the data.

(2) The problem of spurious regressions may arise if the time series prop- erties of the error term are ignored. The authors are particularly critical (and rightly so) of the use of estimated regressions for which the value of the Durbin-Watson statistic is less than the value of R2.

(3) Even when the errors are not assumed to be white noise, the only alternative specification frequently considered is that of a first order autoregressive process. Such an assumption is seldom justified on either theoretical or empirical grounds.

Granger and Newbold claim that the rather poor forecasting performance of large econometric models, when compared with naive forecasts based on univariate time series models, is a consequence of this general lack of careful analysis of the error structures by econometricians.

In order to improve this unsatisfactory state of affairs, the authors suggest two useful strategies. The first of these is to combine the forecasts given by alternative models. The use of such combinations in order to improve forecasts is discussed in Chapter 8. The second and more far- reaching strategy suggested is to employ time series techniques in the analysis of the residuals from estimation of econometric models. Such analysis would be pursued not only for the purpose of improving forecasts but also to improve the specification of the model itself. It is unfortunate that the authors only mention the possibility of such a strategy. A practical demonstration of the time series analysis of residuals would have greatly enhanced the usefulness of the book to macroeconomists.

It seems apparent that macroeconomists interested in forecasting economic time series can benefit from the methods of time series analysis. Although controversy will remain, this book can provide a fine beginning.

DAVID SPENCER Illinois State University

Economic Uncertainty and Financial Structure. A.T.K. Grant. London: Macmillan and Co., and New York: Holmes and Meier, 1977; pp. x + 231. $23.50.

Mr. Grant’s theme is that undersupply and mal-allocation of funds for capital investment are key to the underperformance of the postwar British economy. Changes in financial structure have exacerbated these

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