Robust estimation of linear state space models

Ruben Crevits and Christophe Croux

No 588734, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: The model parameters of linear state space models are typically estimated with maximum likelihood estimation, where the likelihood is computed analytically with the Kalman filter. Outliers can deteriorate the estimation. Therefore we propose an alternative estimation method. The Kalman filter is replaced by a robust version and the maximum likelihood estimator is robustified as well. The performance of the robust estimator is investigated in a simulation study. Robust estimation of time varying parameter regression models is considered as a special case. Finally, the methodology is applied to real data.

Keywords: Kalman Filter; Forecasting; Outliers; Time varying parameters (search for similar items in EconPapers)
Date: 2017-08
New Economics Papers: this item is included in nep-ecm and nep-ets
References: Add references at CitEc
Citations:

Published in FEB Research Report KBI_1713

Downloads: (external link)
https://lirias.kuleuven.be/retrieve/462644 Robust estimation of linear state space models (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ete:kbiper:588734

Access Statistics for this paper

More papers in Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven
Bibliographic data for series maintained by library EBIB ().