Asymmetric time aggregation and its potential benefits for forecasting annual data
- Author(s)
- Robert Kunst, Philip Hans Franses
- 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 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.
- Organisation(s)
- Department of Economics
- External organisation(s)
- Erasmus University Rotterdam
- Journal
- Empirical Economics: a quarterly journal of the Institute for Advanced Studies, Vienna
- Volume
- 49
- Pages
- 363 - 387
- No. of pages
- 25
- ISSN
- 0377-7332
- DOI
- https://doi.org/10.1007/s00181-014-0864-0
- Publication date
- 08-2015
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 502025 Econometrics
- Keywords
- ASJC Scopus subject areas
- Economics and Econometrics, Mathematics (miscellaneous), Statistics and Probability, Social Sciences (miscellaneous)
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/asymmetric-time-aggregation-and-its-potential-benefits-for-forecasting-annual-data(8d54b3c5-a52b-42ee-91d0-2542e3b118a8).html