Department of Economics and Business Economics

When long memory meets the Kalman filter: A comparative study

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When long memory meets the Kalman filter : A comparative study. / Grassi, Stefano; Santucci de Magistris, Paolo.

In: Computational Statistics & Data Analysis, Vol. 76, No. 2, 08.2014, p. 301-319.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Grassi, Stefano ; Santucci de Magistris, Paolo. / When long memory meets the Kalman filter : A comparative study. In: Computational Statistics & Data Analysis. 2014 ; Vol. 76, No. 2. pp. 301-319.

Bibtex

@article{5c4aaeccd33647a599c1ee1124159f5a,
title = "When long memory meets the Kalman filter: A comparative study",
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, State space, Missing observations, Measurement error, Level shifts",
author = "Stefano Grassi and {Santucci de Magistris}, Paolo",
year = "2014",
month = aug,
doi = "10.1016/j.csda.2012.10.018",
language = "English",
volume = "76",
pages = "301--319",
journal = "Computational Statistics & Data Analysis",
issn = "0167-9473",
publisher = "Elsevier BV",
number = "2",

}

RIS

TY - JOUR

T1 - When long memory meets the Kalman filter

T2 - A comparative study

AU - Grassi, Stefano

AU - Santucci de Magistris, Paolo

PY - 2014/8

Y1 - 2014/8

N2 - 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.

AB - 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.

KW - ARFIMA models

KW - State space

KW - Missing observations

KW - Measurement error

KW - Level shifts

U2 - 10.1016/j.csda.2012.10.018

DO - 10.1016/j.csda.2012.10.018

M3 - Journal article

VL - 76

SP - 301

EP - 319

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

SN - 0167-9473

IS - 2

ER -