When Long Memory Meets the Kalman Filter: A Comparative Study

Stefano Grassi (stefano.grassi@uniroma2.it) and Paolo Santucci de Magistris (psantucci@creates.au.dk)
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Paolo Santucci de Magistris: Aarhus University and CREATES, Postal: Bartholins Allé 10, 8000 Aarhus C, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: 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.

Keywords: ARFIMA models; Kalman Filter; Missing Observations; Measurement Error; Level Shifts. (search for similar items in EconPapers)
JEL-codes: C10 C22 C80 (search for similar items in EconPapers)
Pages: 43
Date: 2011-05-02
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: When long memory meets the Kalman filter: A comparative study (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2011-14

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