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Leave-k-out diagnostics in state space models

Tommaso Proietti

No 2000,74, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

Abstract: 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.

Keywords: Kalman filter and smoother; influence; outliers; structural time series models (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://www.econstor.eu/bitstream/10419/62248/1/723854424.pdf (application/pdf)

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Journal Article: LEAVE‐K‐OUT DIAGNOSTICS IN STATE‐SPACE MODELS (2003) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb373:200074

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