584 Book reviews I International Journal of Forecasting 13 (1997) 583-589

tunately, to some degree the book retains this feel with little connection between chapters. After an introduction in Chapters i, 2 and 3 consider the long-run forces of purchasing power parity (PPP) and internal and external balance. Chapters 4 - 6 and 8-9 discuss particular models of exchange rate determination, respectively: balance of payments flow approaches, the Mundell-Fleming model, the monetary approach, the portfolio balance approach, and real interest differentials. Absent are the theories that use an inter-temporal approach to model the current account, a shortcoming that should be rem- edied. Chapters 7, 10, and I1 contain discussions of the special topics of currency substitution, fiscal policy, and central bank intervention. Chapter 12 on technical forecasting models, sorely in need of practical examples, is the last chapter in the book aside from the conclusion, Organizationally, I would have preferred a slightly different structure. Long- run forces, such as PPP, that are often the building blocks of full-blown models are rightly treated first. I would add a chapter on uncovered interest parity. and then bring forward the chapter on real interest differentials. The "model" chapters could then fol- low, with a discussion of the effects of fiscal and monetary policy in the model as part of each chapter (this is already the case in Chapters 5 ,'rod 8). This would eliminate Chapter 10. The book could con- clude with the chapters on currency substitution, central bank intervention, and technical analysis.

Several comments on particular chapters are in order. The chapter on PPP ought to include some discussion of the United Nations Income Comparison Project and the associated work of Summers and Heston (1991). The discussion on empirical verifica- tions of PPP is quite good and very up to date. In Chapter 3, 1 especially liked the treatment of Aus- tralia's terms of trade. More attention should be paid to econometric work on trade equation elasticities in Chapter 4, this could also tie-in nicely with the portions of that chapter dealing with current account sustainability. As Chapter 5 progresses, the small country assumption of the MundelI-Fleming model is increasingly ignored. More mention needs to be made of the effects of dropping this assumption. The discussion of the European Exchange Rate Mecha- nism (ERM) in Chapter 5 is very well done. Chapter Ii is marred by Exhibit !1-10 which looks to be a

classic apples and oranges comparison of some measure of Federal Reserve intervention to daily turnover in the foreign exchange, government bond. and money markets. The point that intervention is small relative to turnover can be made in much better fashion. The final chapter on technical analysis is really too broad to be of much use. Examples of the track record of several approaches over a recent. short time period would more useful.

In summary, Currency Forecasting is of little help to currency forecasters. It is more useful to those attempting to understand long-run movements in exchange rates or the theories underlying currency forecasting approaches based on economic fun- damentals. Even so, the book gives little guidance on when to focus on one of the several models that are presented. Later editions would be better served by a switch in title that more accurately reflects the book's contents.

References

Is.')rd. Peter. ~x('hange Rate Economics, Can)bridge University Press, I ty)5.

Sununers, Robert and Alan Fleston, "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988"'. Quarterly Journal of l'conomics, May 199 I, pp. 327-368.

William R. Melick hlternational Finance Div&ion

Federal Reserve Board

Pi t S0169-2070(97 ]00038-1

Nonstationary Time Series Analysis attd Cointegra- tion, C. Hargreaves ed. (Oxford University Press, New York, 1996), 308 pp. $29.95, ISBN 0198773927.

This book offers nine papers on how to incorpo- rate the character of time-series data in economic forecasting, econometric modeling, and hypothesis testing. Though I do not find all the papers equally

Book reviews I International Journal of Forecastin¢ 13 (1997) 583-589 585

useful, I would not be surprised if I were to open this book again and again.

In Towards a Theor3.' of Economic Forecasting, Michael Clements and David Hendry study how the non-stationarity of the data affects forecasting, for- malize the (old) practice of add-factors in ex-ante forecasts, and analyze the relative importance of the various sources of forecast uncertainty. The authors argue that the mean-squared forecast error (msfe) is not a useful criterion for model selection because it is not invariant to linear transformations: The msfe for the levels and for the changes of a variable might differ. They propose forecast encompassing as a test to detect features present in one model but not in other. The idea is to discard inaccurate models and to help model redesign. Models used in day-to-day forecasting embody, however, many differences and thus the forecast-encompassing test might confirm the joint inflt, ence of such differences but it is unlikely to identify their separate role except in simple cases.

in Bayes Models and Forecasts of Australian Macroecononzic Time Series, Peter Phillips studies models' Ibrccast perlk~rmance using their Baycs measure. This approach allows diversity in models while recognizing that they seek to explain several features of the data. To implement his approach. Phillips relics on the ratio of the Radon Nikodym derivatives of the Bayes measures for two classes of models: "ARMA(p,q) + trend(r)" and "AR(p) + trend(r)" where p. q, and r are the corresponding degrees. Fh,ctuations in this ratio over the forecast period could indicate reversals in model superiority; the conventional forecast-encompassing test pre- cludes such reversals. He finds that Bayes models have. for most of the Australian time series consid- ered, ~, superior forecast performance. Phillips' ap- proach is transparent but economic theory plays a distant role in model design.

In A Review of Methods of Estimating Cointegrat- ing Relationships, Colin Hargreaves evaluates the empirical distributions of six estimators of long-run coeflicients: OLS. "Hcndry-like", Engle-Yoo three- steps, Fully-Modilied, Box-Tiao, and Johansen. Har- greaves derives the formula for each estimator, reviews its theoretical properties, and computes its empirical distribution with Monte Carlo simulations. For each distribution, Hargreaves computes the mean

bias, the median bias, the standard deviation, skew- ness, and kurtosis; the paper also reports the sen- sitivity of these measures to the degree of serial correlation in the disturbances of the Data Genera- tion Process (DGP). The results indicate that the Johansen estimator comes closest to the parameters of the DGP if the residuals are uncorrelated and the model is well specified; the Fully-Modified estimator gives the best results when the goal is to estimate one cointegrating vector with unknown dimensionali- ty.

In A Test of the Null Hypothesis of Cointegration, David Harris and Brett lnder test the null hypothesis of cointegration instead of the null of no cointegra- tion. This switch, they argue, incorporates a re- searcher's prior beliefs in the design of the test statistic. Current practice tests the null of no coin- tegration and rejects it if there is overwhelming evidence regardless of the researcher's prior beliefs. Etarris and Brett derive the distribution of the test statistic for the hypothesis of cointegration and provide the associated critical values. The paper's empirical implementation is, however, too brief. ! would have found useful a comparison of test results for an unresolved issue such as the validity of purchasing power parity.

In Modelling Seasonal Variation, Svend Hylleberg notes that seasonality need not be predictable enough to have its effects on coefficient estimates be re- moved through the inclusion of 'dummy' variables in the regression. This observation gains further impor- tance when seasonal fluctuations account for most of the variation of the variable of interest and when the seasonal/non-seasonal factors are interrelated. Hy- Ileberg's presentation, however, is needlessly terse given the space availability that an edited volume aftbrds.

In Cointegration, Seasonality, Encompassing. and the Demand fi~r Money in The UK, Neil Ericsson. David Hendry, and Hong-AnhTran examine the implications of seasonal adjustments for modeling UK money demand. They apply encompassing tests to models based on seasonal and unseasonal data. Their empirical models meet the criteria that propo- nents of the general-to-specific methodology advo- cate and the paper's reporting of the results is outstanding. Ericsson et al. find that the cointegration vector for seasonal and nonseasonal data for a given

586 Book reviews / International Journal of Forecasting 13 (1997) 583-589

variable is (1, - 1 ) and that disequilibrium values for models based on seasonal and nonseasonal data are virtually identical. Though these findings might suggest that seasonal adjustments do not matter, Ericsson et al. report persuasive evidence to the contrary. Specifically, they find that the model based on nonseasonal data encompasses the one with the seasonal data and not the other way around. In addition, the treatment of seasonality affects the character of dynamic adjustments as well as the exogeneity status of the explanatory variables: Seasonal adjustments matter.

In Evaluating a Real Business Cycle Model, Favio Canova, Mary Finn, and Adrian Pagan test the time- series implications of a real business cycle model using an unrestricted VAR model as an alternative. Canova et al. use the following criteria: the variables of interest must be I(1), the residuals of the implied cointegrating relations must be I(0), the implications of the postulated model for dynamic adjustments and cointegration vectors cannot contradict the parameter estimates from an unrestricted VAR. Their testing methodology is clearly superior to previous alter- natives but I do not find useful their model-redesign strategy. Specifically, after finding that the data reject the postulated model, the authors follow the specific- to-general methodology for model redesign which is at odds with the general-to-specific methodology embodied in their cointegration tests.

In Mi.tspecification Versus Bubbles in the Cagan Hyperinflation Model, Steven Durlauf and Mark Hooker design and implement a methodology for testing bubbles in the Cagan model controlling for model misspecification. Given this model, Durlauf and Hooker group the factors affecting the properties of the autocorrelation function into model misspecifi- cation and bubbles and apply their test to Cagan's model of the German hyperinflation. They find that the predictive failure of this model is due to mis- specification and not to price bubbles. Though their decomposition can be applied to other models, one needs to recognize the limitations imposed by the ex-post character of their test.

In Regime Switching with Time-varyb~g Transition Probabilities, Francis Diebold, Joo-Haeng Lee and Gretchen Weinbach extend Hamilton's regime switching model by modeling the transition prob- abilities as a linear function of fundamentals. They

evaluate their extension in a simulation study that estimates transition probabilities with both models. The results indicate a larger gap between actual and estimated probability for Hamilton's model than for the extended model. The usefulness of these findings for forecasting remains to be seen, however. Though Diebold et al. endogenize the transition probabilities. they treat the fundamentals as exogenous variables. A more persuasive analysis would have allowed interactions between transitions probabilities and fundamentals.

This paper represents the views of the author and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or other members of its staff.

Jaime Marquez Federal Reserve Board

PII S0169-2070(97)00037-X

Wladyslaw Welfe (ed.) Economies in Transition and the Worhl Economy: Models, Forecasts and Scenarios (Peter Lang, Frankfurt am Main, 1997) 528 pp., £52.00, ISBN 3-631-49335-5.

One of the basic assumptions in econometric modelling and forecasting is that the structure of the economy is fixed so that behaviour in the past can provide guidance for the future. This is obviously not the case with Eastern Europe where there have been dramatic changes in recent years. This volume presents the results of a European Union funded project on the problems of macro-econometric modelling in the transition, which started in 1990, from centrally planned to market economies in the cases of the Polish, Czech and Slovak economies. The approach adopted is to build models for each country for the period of transition and, for forecast- ing and policy simulations, to link them with world models.

The book is in six parts. In part I, a quarterly model for Poland incorporating financial sector flows, rational expectations and error-correction be- haviour, is described, with its simulation properties reported in part 1I. Two different annual models for