When Long Memory Meets the Kalman Filter: A Comparative Study

42 Pages Posted: 22 Apr 2011 Last revised: 19 May 2011

See all articles by Stefano Grassi

Stefano Grassi

Aarhus University - CREATES

Paolo Santucci de Magistris

Luiss University of Rome

Date Written: May 17, 2011

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

JEL Classification: C10, C22, C80

Suggested Citation

Grassi, Stefano and Santucci de Magistris, Paolo, When Long Memory Meets the Kalman Filter: A Comparative Study (May 17, 2011). Available at SSRN: https://ssrn.com/abstract=1815065 or http://dx.doi.org/10.2139/ssrn.1815065

Stefano Grassi

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

Paolo Santucci de Magistris (Contact Author)

Luiss University of Rome ( email )

Viale Romania 32
Rome, 00197
Italy

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