Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/108605
Authors: 
Meitz, Mika
Saikkonen, Pentti
Year of Publication: 
2012
Series/Report no.: 
Koç University-TÜSİAD Economic Research Forum Working Paper Series 1226
Abstract: 
We consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to so-called all-pass models in that it allows for autocorrelation and for more fl exible forms of conditional heteroskedasticity. These features may be attractive especially in economic and financial applications. Unlike in previous literature on maximum likelihood estimation of noncausal and/or noninvertible ARMA models and all-pass models, our estimation theory does allow for Gaussian innovations. We give conditions under which a strongly consistent and asymptotically normally distributed solution to the likelihood equations exists, and we also provide a consistent estimator of the limiting covariance matrix.
Subjects: 
maximum likelihood estimation
autoregressive moving average
ARMA
autoregressive conditional heteroskedasticity
ARCH
noninvertible
noncausal
all-pass
nonminimum phase
JEL: 
C22
C51
Document Type: 
Working Paper

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