Journal
MATHEMATICAL AND COMPUTER MODELLING
Volume 39, Issue 4-5, Pages 363-372Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0895-7177(04)90512-2
Keywords
stable distributions; least absolute deviation; smoothed estimates; heavy tails; random coefficients; infinite variance; autoregressive; dispersion; estimation
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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 them LAD estimators. It is also shown that this new SLAD estimators, are superior than some existing ones. (C) 2004 Elsevier Ltd. All rights reserved.
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