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When long memory meets the Kalman filter: A comparative study

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  • Grassi, Stefano
  • Santucci de Magistris, Paolo

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.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 76 (2014)
Issue (Month): C ()
Pages: 301-319

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Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:301-319

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Web page: http://www.elsevier.com/locate/csda

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Keywords: ARFIMA models; State space; Missing observations; Measurement error; Level shifts;

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Cited by:
  1. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
  2. Andersson, Fredrik N.G. & Li, Yushu, 2013. "How Flexible are the Inflation Targets? A Bayesian MCMC Estimator of the Long Memory Parameter in a State Space Model," Working Papers 2013:38, Lund University, Department of Economics.

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