Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/121860 
Year of Publication: 
2015
Series/Report no.: 
Hohenheim Discussion Papers in Business, Economics and Social Sciences No. 13-2015
Publisher: 
Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften, Stuttgart
Abstract: 
This article presents a robust augmented Kalman filter that extends the data-cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M-type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series.
Subjects: 
robust filtering
augmented Kalman filter
structural time series model
additive outlier
innovation outlier
JEL: 
C32
C53
C63
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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