
Abstract
We investigate a stationary random cofficient autoregressive process. Using renewal type arguments tailor-made for such processes we show that the stationary distribution has a power-law tail. When the model is normal, we show that the model is in distribution equivalent to an autoregressive process with ARCH errors. Hence we obtain the tail behaviour of any such model of arbitrary order.
Item Type: | Paper |
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Keywords: | ARCH model, autoregressive model, geometric ergodicity, heteroscedastic model, random coeËcient autoregressive process, random recurrence equation, regular variation, renewal theorem for Markov chains, strong mixing |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386 Special Research Fields > Special Research Field 386 |
Subjects: | 500 Science > 510 Mathematics |
URN: | urn:nbn:de:bvb:19-epub-1648-3 |
Language: | English |
Item ID: | 1648 |
Date Deposited: | 05. Apr 2007 |
Last Modified: | 04. Nov 2020, 12:45 |