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.
|Date of creation:||2000|
|Date of revision:|
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- Daniel E. Sichel, 1989.
"Business cycle asymmetry: a deeper look,"
Working Paper Series / Economic Activity Section
93, Board of Governors of the Federal Reserve System (U.S.).
- Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-89, October.
- Proietti Tommaso, 1998. "Characterizing Asymmetries in Business Cycles Using Smooth-Transition Structural Time-Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(3), pages 1-18, October.
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