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A new state-space methodology to disaggregate multivariate time series

Listed author(s):
  • Víctor Gómez
  • Félix Aparicio-Pérez
Registered author(s):

    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

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    Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

    Volume (Year): 30 (2009)
    Issue (Month): 1 (January)
    Pages: 97-124

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    Handle: RePEc:bla:jtsera:v:30:y:2009:i:1:p:97-124
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