Periodicity and Stochastic Trends in Economic Time Series: Periodicity & Stochastic Trend by Philip Hans FransesPeriodicity and Stochastic Trends in Economic Time Series: Periodicity & Stochastic Trend by Philip Hans Franses

Periodicity and Stochastic Trends in Economic Time Series: Periodicity & Stochastic Trend

byPhilip Hans Franses

Paperback | April 30, 1999

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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 toremove 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. Theanalysis 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 suchdependence, it is shown that seasonal adjustment leads to inappropriate results.
Philip Hans Franses is at Royal Netherlands Academy of Arts and Sciences.
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Title:Periodicity and Stochastic Trends in Economic Time Series: Periodicity & Stochastic TrendFormat:PaperbackDimensions:242 pages, 9.21 × 6.14 × 0.55 inPublished:April 30, 1999Publisher:Oxford University Press

The following ISBNs are associated with this title:

ISBN - 10:0198774540

ISBN - 13:9780198774549

Reviews

Editorial Reviews

`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).