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When Long Memory Meets the Kalman Filter: A Comparative Study

Grassi, Stefano, de Magistris, Paolo Santucci (2014) When Long Memory Meets the Kalman Filter: A Comparative Study. Computational Statistics and Data Analysis, 76 . pp. 301-319. ISSN 0167-9473. (doi:10.1016/j.csda.2012.10.018) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/j.csda.2012.10.018

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.

Item Type: Article
DOI/Identification number: 10.1016/j.csda.2012.10.018
Additional information: number of additional authors: 1;
Uncontrolled keywords: ARFIMA models; State space; Missing observations; Measurement error; Level shifts
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculties > Social Sciences > School of Economics
Depositing User: Stewart Brownrigg
Date Deposited: 07 Mar 2014 00:05 UTC
Last Modified: 29 May 2019 12:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/40244 (The current URI for this page, for reference purposes)
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