Journal
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
Volume 285, Issue -, Pages 116-124Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cam.2015.02.020
Keywords
Asymptotic normality; Conditional least squares estimator; Domain of attraction of the normal law; Random coefficient AR(1); Self-normalization
Categories
Funding
- National Natural Science Foundation of China [11301481, 11371321]
- Zhejiang Provincial Natural Science Foundation of China [LY13A01003]
- Zhejiang Provincial Key Research Base for Humanities and Social Science Research (Statistics)
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Consider a random coefficient AR(1) model, X-t = (rho(n) + phi(n))Xt-i + u(t), where {rho(n,) n >= 1} is a sequence of real numbers, {phi(n), n >= 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 rho(n) is achieved, and shown to be asymptotically normal by allowing the second moment of the innovation to be possibly (C) 2015 Elsevier B.V. All rights reserved.
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