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

Martyna Marczak, Tommaso Proietti and Stefano Grassi ()

Econometrics and Statistics, 2018, vol. 5, issue C, 107-123

Abstract: A robust augmented Kalman filter (AKF) is presented for the general state space model featuring non-stationary 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. The performance of the robust AKF is investigated 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)
Date: 2018
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: A Data–Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models (2016)
Working Paper: A data-cleaning augmented Kalman filter for robust estimation of state space models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:5:y:2018:i:c:p:107-123

DOI: 10.1016/j.ecosta.2017.02.002

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