A new state-space methodology to disaggregate multivariate time series
AbstractThis 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
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Journal of Time Series Analysis.
Volume (Year): 30 (2009)
Issue (Month): 1 (01)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782
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- Helmut Lütkepohl, 2010.
"Forecasting Aggregated Time Series Variables: A Survey,"
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OECD Publishing,CIRET, vol. 2010(2), pages 1-26.
- Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
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