Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/72726
Authors: 
Kunst, Robert M.
Franses, Philip Hans
Year of Publication: 
2010
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 252
Abstract: 
For many economic time-series variables that are observed regularly and frequently, for example weekly, the underlying activity is not distributed uniformly across the year. For the aim of predicting annual data, one may consider temporal aggregation into larger subannual units based on an activity time scale instead of calendar time. Such a scheme may strike a balance between annual modelling (which processes little information) and modelling at the finest available frequency (which may lead to an excessive parameter dimension), and it may also outperform modelling calendar time units (with some months or quarters containing more information than others). We suggest an algorithm that performs an approximate inversion of the inherent seasonal time deformation. We illustrate the procedure using weekly data for temporary staffing services.
Subjects: 
seasonality
time deformation
prediction
time series
JEL: 
C22
C53
Document Type: 
Working Paper

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