Abstract
The aim of this article is to analyse the effect of the level shifts and temporary changes on the specification of a model with conditional heteroscedasticity, a concept very little dealt with up to now, the literature focusing more on additive outliers. To do this, we have conducted various Monte Carlo experiments in which the effect of these outliers on the principal model identification tools (descriptive statistics, graphs and heteroscedasticity tests) is analysed.
Acknowledgements
The authors gratefully acknowledge the helpful observations and suggestions of the anonymous referees. This research has been supported by the Spanish Ministry of Education and Science and FEDER under Project SEJ2006-02328 of the Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica.
Notes
†In the context of the ARIMA models, see Trívez and Nievas Citation13.
†In the article we only present the results corresponding to a sample size of 1000. The results for the sample size 200 can be requested from the authors.
‡Among other authors we can cite: Franses and Ghijsels Citation14, Carnero et al. Citation15, Verhoeven and Mc Aleer Citation16, Franses and Van Dijk Citation17, Gregory and Reeves Citation18, Franses and Van Dijk Citation19, He and Teräsvirta Citation20, Hotta and Tsay Citation21, Park Citation22, Afonso et al. Citation11, McCurdy and Morgan Citation23, Rich et al. Citation24, Bera et al., Citation25, Bera and Higgings Citation26. All these authors have used parameters similar to those employed in our research.
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