A data-cleaning augmented Kalman filter for robust estimation of state space models

Martyna Marczak, Tommaso Proietti and Stefano Grassi ()

No 13-2015, Hohenheim Discussion Papers in Business, Economics and Social Sciences from University of Hohenheim, Faculty of Business, Economics and Social Sciences

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.

Keywords: robust filtering; augmented Kalman filter; structural time series model; additive outlier; innovation outlier (search for similar items in EconPapers)
JEL-codes: C32 C53 C63 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Related works:
Journal Article: A data-cleaning augmented Kalman filter for robust estimation of state space models (2018)
Working Paper: A Data–Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hohdps:132015

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