Smoothed estimates for 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:
2004

Citing 2
Cited 0

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

Mathematical and Computer Modelling: An International Journal
Estimation for regression with infinite variance errors

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 (see [1] and references therein). For example, ARIMA time-series models with infinite variance innovations are widely used in financial modelling. However, a little attention has been paid to incorporate infinite variance innovations for time-series models with random coefficients introduced by [2]. This paper considers the problem of nonparametric estimation for some time-series models using the smoothed least absolute deviation (SLAD) estimating function approach. We introduce a class of kernels in order to smooth the LAD estimators. It is also shown that this new SLAD estimators are superior than some existing ones.