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://ucris.univie.ac.at/portal/en/publications/asymmetric-time-aggregation-and-its-potential-benefits-for-forecasting-annual-data(8d54b3c5-a52b-42ee-91d0-2542e3b118a8).html