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A new state–space methodology to disaggregate multivariate time series
Author(s) -
Gómez Víctor,
AparicioPérez Félix
Publication year - 2009
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2008.00602.x
Subject(s) - identifiability , observability , multivariate statistics , series (stratigraphy) , autoregressive model , mathematics , state space , state space representation , interpolation (computer graphics) , time series , autoregressive–moving average model , autoregressive integrated moving average , econometrics , statistics , algorithm , computer science , artificial intelligence , biology , motion (physics) , paleontology
. 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.