Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/62248
Authors: 
Proietti, Tommaso
Year of Publication: 
2000
Series/Report no.: 
Discussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 2000,74
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.
Subjects: 
Kalman filter and smoother
influence
outliers
structural time series models
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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