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Asymmetric Time Aggregation and its Potential Benefits for Forecasting Annual Data

  • Kunst, Robert M.

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and Department of Economics, University of Vienna, Vienna, Austria)

  • Franses, Philip Hans

    (Erasmus School of Economics, Econometrics, Erasmus University Rotterdam, Rotterdam, The Netherlands)

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.

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File URL: http://www.ihs.ac.at/publications/eco/es-252.pdf
File Function: First version, 2010
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Paper provided by Institute for Advanced Studies in its series Economics Series with number 252.

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Length: 30 pages
Date of creation: Jul 2010
Date of revision:
Handle: RePEc:ihs:ihsesp:252
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  1. Oscar Jordà & Massimiliano Marcellino, 2004. "Time-scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
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