Time series aggregation, disaggregation and long memory
Abstrak
Large-scale aggregation and its inverse, disaggregation, problems are important in many fields of studies like macroeconomics, astronomy, hydrology and sociology. It was shown in Granger (1980) that a certain aggregation of random coefficient AR(1) models can lead to long memory output. Dacunha-Castelle and Oppenheim (2001) explored the topic further, answering when and if a predefined long memory process could be obtained as the result of aggregation of a specific class of individual processes. In this paper, the disaggregation scheme of Leipus et al. (2006) is briefly discussed. Then disaggregation into AR(1) is analyzed further, resulting in a theorem that helps, under corresponding assumptions, to construct a mixture density for a given aggregated by AR(1) scheme process. Finally the theorem is illustrated by FARUMA mixture densityÆs example.
Topik & Kata Kunci
Penulis (2)
Dmitrij Celov
Remigijus Leipus
Akses Cepat
- Tahun Terbit
- 2023
- Sumber Database
- DOAJ
- DOI
- 10.15388/LMR.2006.30723
- Akses
- Open Access ✓