Major developments in the analysis of non-stationary time series and co-integration are shown in this book. Papers include David Hendry's work on forecasting, Peter Phillip's work on Bayesian models, Svend Hylleberg's work on seasonality, and Adrian Pagan's work on real business cycle models.

Other topics covered include an overview of the different estimators of cointegrating relationships, and a new test of cointegration. Applications are shown finding roots in macroeconomic series, testing the Fisher Hypothesis, testing money demand functions, and testing for inflation bubbles.

The book provides good coverage of the depth of this literature showing the importance of an understanding of non-stationarity and co-integration.

The other contributors are: F. Canova, Mike P. Clements, Francis X. Diebold, Steven N. Durlauf, Neil R. Ericsson, M. Finn, Colin Hargreaves, David Harris, Mark A. Hooker, Brett Inder, Joon-Haeng Lee, Hong-Anh Tran, Gretchen C. Weinbach

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Non-stationary Time Series Analysis and Cointegration The econometric analysis of the long run has developed dramatically over the last 12 years. This volume describes and evaluates new methods, provides useful overviews, and shows detailed implementations helpful to practitioners.

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