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A Data–Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models

Martyna Marczak, Tommaso Proietti and Stefano Grassi ()

No 374, CEIS Research Paper from Tor Vergata University, CEIS

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)
Pages: 30 pages
Date: 2016-03-31, Revised 2016-03-31
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
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Journal Article: A data-cleaning augmented Kalman filter for robust estimation of state space models (2018) Downloads
Working Paper: A data-cleaning augmented Kalman filter for robust estimation of state space models (2015) Downloads
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