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When Long Memory Meets the Kalman Filter: A Comparative Study

  • Stefano Grassi

    ()

    (Aarhus University and CREATES)

  • Paolo Santucci de Magistris

    ()

    (Aarhus University and CREATES)

The finite sample properties of the state space methods applied to long memory time series are analyzed through Monte Carlo simulations. The state space setup allows to introduce a novel modeling approach in the long memory framework, which directly tackles measurement errors and random level shifts. Missing values and several alternative sources of misspecification are also considered. It emerges that the state space methodology provides a valuable alternative for the estimation of the long memory models, under different data generating processes, which are common in financial and economic series. Two empirical applications highlight the practical usefulness of the proposed state space methods.

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Paper provided by Department of Economics and Business Economics, Aarhus University in its series CREATES Research Papers with number 2011-14.

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Length: 43
Date of creation: 02 May 2011
Date of revision:
Handle: RePEc:aah:create:2011-14
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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