A new state-space methodology to disaggregate multivariate time series
This article addresses the problem of disaggregating multivariate time series sampled at different frequencies using state-space models. In particular, we consider the relation between the high-frequency and low-frequency models, the possible loss of observability and identifiability in the latter with respect to the former, the estimation of the parameters of the low-frequency model by maximum likelihood, and the prediction and interpolation of high-frequency figures when only low-frequency data are available. Since vector autoregressive moving average models are a special case of state-space models, our results are also valid for those models, but they include other models as well, like structural models. We provide a rigorous theoretical development of the aforementioned issues, including a comparison with the classical model-based approaches, and we propose a practical methodology to disaggregate multivariate time series that is both efficient and easy to implement. Copyright 2009 The Authors. Journal compilation 2009 Blackwell Publishing Ltd
Volume (Year): 30 (2009)
Issue (Month): 1 (01)
|Contact details of provider:|| Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 |
|Order Information:||Web: http://www.blackwellpublishing.com/subs.asp?ref=0143-9782|
When requesting a correction, please mention this item's handle: RePEc:bla:jtsera:v:30:y:2009:i:1:p:97-124. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If references are entirely missing, you can add them using this form.