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Advances in the Estimation of Fractionally Integrated Models

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thesis
posted on 28.05.2019 by KANCHANA NADARAJAH
Data in the economic and financial spheres often exhibit dynamic patterns characterized by a long lasting response to past shocks. The correct modelling of such long range dependence is of paramount importance, both in the production of accurate forecasts over long term horizons and in the isolation of long run equilibrium relationships. While the convention in the area has been to adopt complete parametric specifications for the dynamics in the time series, semi-parametric approaches have also featured. This thesis contributes to both of these lines of research, exploring the consequences for parametric estimation of mis-specification of the short memory dynamics and developing a bias-corrected semi-parametric estimator of the long memory parameter.

History

Principal supervisor

Gael Margaret Martin

Year of Award

2019

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Business and Economics

Exports

Exports