Inference for some time series models with random coefficients and infinite variance innovations

Authors:
A. Thavaneswaran;S. Peiris
Affiliations:
School of Mathematics and Statistics The University of Sydney, N.S.W. 2006, Australia;School of Mathematics and Statistics The University of Sydney, N.S.W. 2006, Australia
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2001

Citing 1
Cited 1

Estimation for regression with infinite variance errors

Mathematical and Computer Modelling: An International Journal

Smoothed estimates for models with random coefficients and infinite variance innovations

Mathematical and Computer Modelling: An International Journal

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Abstract

Infinite variance processes have attracted growing interest in recent years due to its applications in many areas of statistics. For example, ARIMA time series models with infinite variance innovations are widely used in financial modelling. However, little attention has been paid to incorporate infinite variance innovations for time series models with random coefficients introduced by Nicholls and Quinn [1]. Estimation of model parameters for some special cases are discussed using the least absolute deviation (LAD) estimating function approach when the closed form density is available. It is also shown that these new LAD estimates are superior to some of the existing ones.