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Time series models, second edition
582
Book
journals such as Econometrica, Journal of the American Statistical Association, Journal of Business 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 reference 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
reviews 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. Birchenhall, 1988, “Seasonality and the order of integration Oxford Bulletin of Economics and for consumption”. Statistics, 50, 361-377. Sims, C.A., 1974, “Seasonality in regression”, Journal of the American Statistical Association. 69, 618-627. adjustment and relations Wallis, K.F.. 1974, “Seasonal between variables”, Journal of the American Statistical Association. 69, 18-31.
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-components 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 empirical 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, Washington, DC, 9-10 September 1976, Washington, DC. US Department of Commerce. Bureau of the Census, 281295. 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-modelbased approach to seasonal adjustment”, Journal of the American Statistical Association. 77, 63-70. Hylleberg, S.. 1986, “The historical perspective” and “The Ed., Seasonality in X-11 method”, in: S. Hylleberg, 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-
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 accommodate these advances. There are eight chapters in all, including and essentially repeating from the first edition, the Introduction, chapters on stationary stochastic estimation and testing of ARMA processes, models, state space modelling, and frequency domain aspects of time series. There are some changes in the contents of these reflecting recent which are introduced approdevelopments, priately. Multivariate interests previously scattered are now collected into one chapter, which seems the logical step to take and has in addition a discussion 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 interventions. The recent research on non-linear models is subject to an interesting appraisal in Chapter 8,
Book
alluding to conditional Gaussian models, ARCH models, stochastic variance models, and switching regimes. The latter two topics appear to this reviewer to be important areas for research, occurring as they do in many applications, particularly so perhaps in the financial and economic fields. There is no mention here of neural networks and the equations giving rise to chaos, however there is enough material and stimulus contained within these pages to whet any interested reader’s appetite. This is a welcome updated version of a valuable student textbook. W.D. Ray Birkbeck College University of London, UK
Kenneth Planning 270 pp.,
N. Myers, 1993, Total Contingency for Disasters (Wiley, New York) xvi + 2545.50, ISBN O-471-574418-X.
reviews
583
The author has written a book on contingency planning deliberately setting out a personal recipe which he maintains is practical and cost effective. He includes examples of specimen problem solutions, answers to popular questions, policies/strategies to gain senior management co-operation, and a trusted methodology for lowering solution costs. A key concept is the ‘disaster life cycle’, each stage of which is identified and discussed. Business dependency on computer systems makes it important to have plans in the event of catastrophe. The author advises close examination of existing plans and gives his own criteria upon which to judge them. Senior management may not swallow all the ideas presented here, but they may well benefit by a browse through its pages. W.D. Ray Birkbeck College University of London, UK
Book
journals such as Econometrica, Journal of the American Statistical Association, Journal of Business 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 reference 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
reviews 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. Birchenhall, 1988, “Seasonality and the order of integration Oxford Bulletin of Economics and for consumption”. Statistics, 50, 361-377. Sims, C.A., 1974, “Seasonality in regression”, Journal of the American Statistical Association. 69, 618-627. adjustment and relations Wallis, K.F.. 1974, “Seasonal between variables”, Journal of the American Statistical Association. 69, 18-31.
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-components 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 empirical 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, Washington, DC, 9-10 September 1976, Washington, DC. US Department of Commerce. Bureau of the Census, 281295. 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-modelbased approach to seasonal adjustment”, Journal of the American Statistical Association. 77, 63-70. Hylleberg, S.. 1986, “The historical perspective” and “The Ed., Seasonality in X-11 method”, in: S. Hylleberg, 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-
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 accommodate these advances. There are eight chapters in all, including and essentially repeating from the first edition, the Introduction, chapters on stationary stochastic estimation and testing of ARMA processes, models, state space modelling, and frequency domain aspects of time series. There are some changes in the contents of these reflecting recent which are introduced approdevelopments, priately. Multivariate interests previously scattered are now collected into one chapter, which seems the logical step to take and has in addition a discussion 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 interventions. The recent research on non-linear models is subject to an interesting appraisal in Chapter 8,
Book
alluding to conditional Gaussian models, ARCH models, stochastic variance models, and switching regimes. The latter two topics appear to this reviewer to be important areas for research, occurring as they do in many applications, particularly so perhaps in the financial and economic fields. There is no mention here of neural networks and the equations giving rise to chaos, however there is enough material and stimulus contained within these pages to whet any interested reader’s appetite. This is a welcome updated version of a valuable student textbook. W.D. Ray Birkbeck College University of London, UK
Kenneth Planning 270 pp.,
N. Myers, 1993, Total Contingency for Disasters (Wiley, New York) xvi + 2545.50, ISBN O-471-574418-X.
reviews
583
The author has written a book on contingency planning deliberately setting out a personal recipe which he maintains is practical and cost effective. He includes examples of specimen problem solutions, answers to popular questions, policies/strategies to gain senior management co-operation, and a trusted methodology for lowering solution costs. A key concept is the ‘disaster life cycle’, each stage of which is identified and discussed. Business dependency on computer systems makes it important to have plans in the event of catastrophe. The author advises close examination of existing plans and gives his own criteria upon which to judge them. Senior management may not swallow all the ideas presented here, but they may well benefit by a browse through its pages. W.D. Ray Birkbeck College University of London, UK