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

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Author Info
Lii, Keh-Shin
Rosenblatt, Murray
Abstract

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.

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File URL: http://www.sciencedirect.com/science/article/B6WK9-4CRM929-50/2/2d7d49f0f4edb4e5a9f8bee27746117c
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Publisher Info
Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 43 (1992)
Issue (Month): 2 (November)
Pages: 272-299
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Handle: RePEc:eee:jmvana:v:43:y:1992:i:2:p:272-299

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Related research
Keywords: approximate maximum likelihood estimates asymptotic normality moving average nonminimum phase noninvertible non-Gaussian;

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Jaap Geluk & Liang Peng & Casper G. de Vries, 1999. "Convolutions of Heavy Tailed Random Variables and Applications to Portfolio Diversification and MA(1) Time Series," Tinbergen Institute Discussion Papers 99-088/2, Tinbergen Institute. [Downloadable!]
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