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

Keh-Shin Lii and Murray Rosenblatt

Journal of Multivariate Analysis, 1992, vol. 43, issue 2, 272-299

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.

Keywords: approximate; maximum; likelihood; estimates; asymptotic; normality; moving; average; nonminimum; phase; noninvertible; non-Gaussian (search for similar items in EconPapers)
Date: 1992
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Citations: View citations in EconPapers (15)

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