Self-weighted least absolute deviation estimation for infinite variance autoregressive models
How to undertake statistical inference for infinite variance autoregressive models has been a long-standing open problem. To solve this problem, we propose a self-weighted least absolute deviation estimator and show that this estimator is asymptotically normal if the density of errors and its derivative are uniformly bounded. Furthermore, a Wald test statistic is developed for the linear restriction on the parameters, and it is shown to have non-trivial local power. Simulation experiments are carried out to assess the performance of the theory and method in finite samples and a real data example is given. The results are entirely different from other published results and should provide new insights for future research on heavy-tailed time series. Copyright 2005 Royal Statistical Society.
Year of publication: |
2005
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Authors: | Ling, Shiqing |
Published in: |
Journal of the Royal Statistical Society Series B. - Royal Statistical Society - RSS, ISSN 1369-7412. - Vol. 67.2005, 3, p. 381-393
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Publisher: |
Royal Statistical Society - RSS |
Saved in:
freely available
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