Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models
AbstractWe discuss computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.
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Bibliographic InfoPaper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2001-W27.
Length: 14 pages
Date of creation: 29 Nov 2001
Date of revision:
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Web page: http://www.nuff.ox.ac.uk/economics/
Long memory; Bias; Modified profile likelihood; Restricted maximum likelihood estimator; Time-series regression model likelihood;
Other versions of this item:
- Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-CMP-2001-12-19 (Computational Economics)
- NEP-ECM-2001-12-19 (Econometrics)
- NEP-ETS-2001-12-19 (Econometric Time Series)
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