Arbeitspapier
Leave-k-out diagnostics in state space models
The paper derives an algorithm for computing leave-k-out diagnostics for the detection of patches of outliers for stationary and non-stationary state space models with regression effects. The algorithm is based on a reverse run of the Kalman filter on the smoothing errors and is both efficient and easy to implement. An illustration concerning the US index of industrial production for Textiles proves the effectiveness of multiple deletion diagnostics in unmasking clusters of outlying observations.
- Language
-
Englisch
- Bibliographic citation
-
Series: SFB 373 Discussion Paper ; No. 2000,74
- Classification
-
Wirtschaft
- Subject
-
Kalman filter and smoother
influence
outliers
structural time series models
- Event
-
Geistige Schöpfung
- (who)
-
Proietti, Tommaso
- Event
-
Veröffentlichung
- (who)
-
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
- (where)
-
Berlin
- (when)
-
2000
- Handle
- URN
-
urn:nbn:de:kobv:11-10048008
- Last update
-
10.03.2025, 11:45 AM CET
Data provider
This object is provided by:
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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
- Proietti, Tommaso
- Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Time of origin
- 2000
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