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This book provides a self-contained account of periodic models for seasonally observed economic time series with stochastic trends. Two key concepts are periodic integration and periodic cointegration. Periodic integration implies that a seasonally varying differencing filter is required to remove a stochastic trend. Period cointegration amounts to allowing cointegration part-term adjustment parameters to vary with the season. The emphasis is on econometric models that explicitly describe seasonal variation and can reasonably be interpreted in terms of economic behaviour. The analysis considers econometric theory, Monte Carlo simulation, and forecasting, and it is illustrated with numerous empirical time series. A key feature of the proposed models
is that changing seasonal fluctuations depend on the trend and business cycle fluctuations. In the case of such dependence, it is shown that seasonal adjustment leads to inappropriate results.
Readership: Second year graduate students taking econometric courses, especially those on time series.
Philip Hans Franses, Research Fellow, Royal Netherlands Academy of Arts and Sciences
"The book can be recommended to those who want a comprehensive introduction to modern analysis of seasonality or who want to give a post-graduate course on the subject." - Marten Lof, International Journal of Forecasting, 15, (1999).
"Franses' book takes the reader the whole way from fundamentals of time series analysis to the latest achievements, where the young author's own contribution is impressive ... The book gives many practical state of the art tricks and hints that an applied researcher will appreciate." - Marten Lof, International Journal of Forecasting, 15, (1999).