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• W2100808511 abstract "This paper concerns the autocovariance calculation and likelihood evaluation for periodic vector ARMA models (PV ARMA). Based on a state space representation of PV ARMA models, we derive an algorithm for computing the PV ARMA autocovariances. The proposed method computes the autocovariances for distinct seasons separately, thereby facilitating efficient calculation for models with a large period. As a result, the obtained autocovariance calculation procedure is exploited in a periodic Chandrasekhar-type filter to evaluate the exact likelihood for Gaussian PV ARMA series. Empirical evidence shows the superiority of the periodic Chandrasekhar algorithm for likelihood evaluation over the Kalman-based one." @default.
• W2100808511 created "2016-06-24" @default.
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• W2100808511 date "2009-03-01" @default.
• W2100808511 modified "2023-09-23" @default.
• W2100808511 title "Calculating the autocovariances and the likelihood for periodic V ARMA models" @default.
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• W2100808511 doi "https://doi.org/10.1080/00949650701692291" @default.
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