When Long Memory Meets the Kalman Filter: A Comparative Study
AbstractThe 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 InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-14.
Date of creation: 02 May 2011
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Web page: http://www.econ.au.dk/afn/
ARFIMA models; Kalman Filter; Missing Observations; Measurement Error; Level Shifts.;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-24 (All new papers)
- NEP-ECM-2011-05-24 (Econometrics)
- NEP-ETS-2011-05-24 (Econometric Time Series)
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