Single and multiple error state-space models for signal extraction

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Casals Carro, José and Sotoca López, Sonia and Jerez Méndez, Miguel (2013) Single and multiple error state-space models for signal extraction. Journal of Statistical Computation and Simulation, 85 (5). pp. 1053-1069. ISSN 1563-5163

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Official URL: https://doi.org/10.1080/00949655.2013.867960




Abstract

We compare the results obtained by applying the same signal extraction procedures to two observationally equivalent state-space forms. The first model has different errors affecting the states and the observations, while the second has a single perturbation term which coincides with the one-step-ahead forecast error. The signals extracted from both forms are very similar but their variances are drastically different, because the states for the single-source error representation collapse to exact values while those coming from the multiple-error model remain uncertain. The implications of this result are discussed both, with theoretical arguments and practical examples. We find that single error representations have advantages to compute the likelihood or to adjust for seasonality, while multiple error models are better suited to extract a trend indicator. Building on this analysis, it is natural to adopt a ‘best of both worlds’ approach, which applies each representation to the task in which it has comparative advantage.


Item Type:Article
Uncontrolled Keywords:State-space model; Signal extraction; Seasonal adjustment; Kalman filter; Trend
Subjects:Social sciences > Economics > Econometrics
ID Code:61904
Deposited On:25 Aug 2020 08:49
Last Modified:31 Aug 2020 10:05

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