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Abstract
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This article applies the EM-based (ECM and ECME) algorithms to find the maximum
likelihood estimates of model parameters in general AR models with independent scaled
t-distributed innovations whenever the degrees of freedom are unknown. The ECME,
sharing advantages with both EM and Newton–Raphson algorithms, is an extension
of ECM, which itself is an extension of the EM algorithm. The ECM and ECME algorithms, which are analytically quite simple to use, are then compared based on the
computational running time and the accuracy of estimation via a simulation study. The
results demonstrate that the ECME is efficient and usable in practice. We also show how
our method can be applied to the Wolfer’s sunspot data.
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