Arbeitspapier
A data-cleaning augmented Kalman filter for robust estimation of state space models
This article presents a robust augmented Kalman filter that extends the data-cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M-type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series.
- Language
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Englisch
- Bibliographic citation
-
Series: Hohenheim Discussion Papers in Business, Economics and Social Sciences ; No. 13-2015
- Classification
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Forecasting Models; Simulation Methods
Computational Techniques; Simulation Modeling
- Subject
-
robust filtering
augmented Kalman filter
structural time series model
additive outlier
innovation outlier
- Event
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Geistige Schöpfung
- (who)
-
Marczak, Martyna
Proietti, Tommaso
Grassi, Stefano
- Event
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Veröffentlichung
- (who)
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Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
- (where)
-
Stuttgart
- (when)
-
2015
- Handle
- URN
-
urn:nbn:de:bsz:100-opus-11563
- Last update
-
14.03.0003, 8:01 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Marczak, Martyna
- Proietti, Tommaso
- Grassi, Stefano
- Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
Time of origin
- 2015
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