Asymmetric time aggregation and its potential benefits for forecasting annual data
Robert Kunst () and
Philip Hans Franses
Empirical Economics, 2015, vol. 49, issue 1, 363-387
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 timescale instead of calendar time. Such a scheme may strike a balance between annual modeling (which processes little information) and modeling at the finest available frequency (which may lead to an excessive parameter dimension), and it may also outperform modeling 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 two exemplary weekly time series. Copyright Springer-Verlag Berlin Heidelberg 2015
Keywords: Seasonality; Forecasting; Time deformation; Time series (search for similar items in EconPapers)
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Working Paper: Asymmetric Time Aggregation and its Potential Benefits for Forecasting Annual Data (2010)
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Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund
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