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An approximate maximum likelihood estimation for non-Gaussian non-minimum phase moving average processes

An approximate maximum likelihood procedure is proposed for the estimation of parameters in possibly nonminimum phase (noninvertible) moving average processes driven by independent and identically distributed non-Gaussian noise. Under appropriate conditions, parameter estimates that are solutions of likelihood-like equations are consistent and are asymptotically normal. A simulation study for MA(2) processes illustrates the estimation procedure.
Year of publication:
1992
Authors: Lii, Keh-Shin ; Rosenblatt, Murray
Published in:
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 43.1992, 2, p. 272-299
Publisher: Elsevier
Keywords: approximate maximum likelihood estimates asymptotic normality moving average nonminimum phase noninvertible non-Gaussian
Type of publication: Article
Source:
Persistent link: https://www.econbiz.de/10005160398
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