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The exact likelihood for a multivariate ARMA model
Authors:Victor Solo
Institution:Harvard University USA
Abstract:A number of algorithms are presented for calculating the exact likelihood of a multivariate ARMA model. There are two aspects to the algorithms. Firstly, the parameterization is in terms of AR parameters and autocovariances. This obviates difficulties with initial MA estimates. Secondly, the algorithms explicitly account for specification of the lag structure of the multivariate time series. Additionally, an algorithm is presented to deal with missing data. The algorithms are, of themselves, not new but they have not been applied to likelihood construction in the manner discussed here.
Keywords:62M99  93B30  Multivariate  time series  Kalman filter  Kronecker indices  Markov model  ARMA model  likelihood
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