Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models

We discuss computational aspects of likelihood-based estimation of unvariate 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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Year of publication:	2001-11-01
Authors:	Doornik, Jurgen ; Ooms, Marius
Institutions:	Department of Economics, Oxford University
Subject:	Long memory \| Bias \| Modified profile likelihood \| Restricted maximum likelihood estimator \| Time-series regression model likelihood

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Extent:	application/pdf
Series:	Economics Series Working Papers.
Type of publication:	Book / Working Paper
Notes:	Number 2001-W27
Classification:	C22 - Time-Series Models ; C63 - Computational Techniques
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010605220