Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4638478 | Journal of Computational and Applied Mathematics | 2015 | 9 Pages |
Abstract
Consider a random coefficient AR(1) model, Xt=(Ïn+Ïn)Xtâ1+ut, where {Ïn,nâ¥1} is a sequence of real numbers, {Ïn,nâ¥1} is a sequence of random variables, and the innovations of the model form a sequence of i.i.d. random variables belonging to the domain of attraction of the normal law. By imposing some weaker conditions, the conditional least squares estimator of the autoregressive coefficient Ïn is achieved, and shown to be asymptotically normal by allowing the second moment of the innovation to be possibly infinite.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Ke-Ang Fu, Xiaoyong Fu,