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.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 373 Discussion Paper ; No. 2000,74
- Klassifikation
-
Wirtschaft
- Thema
-
Kalman filter and smoother
influence
outliers
structural time series models
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Proietti, Tommaso
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
- (wo)
-
Berlin
- (wann)
-
2000
- Handle
- URN
-
urn:nbn:de:kobv:11-10048008
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Proietti, Tommaso
- Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Entstanden
- 2000
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