Abstract
In recent years, the Kalman filter has been used extensively to compute the exact likelihood of autoregressive-moving average (ARMA) and autoregressive integrated moving average (ARIMA) time series models. See, for example, Akaike (1978), Gardner, Harvey & Phillips (1980), Jones (1980) and Harvey & Pierse (1984). Using the Cholesky factorization, Ansley (1979) gave an alternative computationally efficient algorithm for computing the likelihood of an ARMA model when there are no missing observations. In particular, he showed how to obtain substantial computational savings for the seasonal moving average model by using the Cholesky factorization. By applying his Theorem 4-1, we can obtain similar savings for an algorithm based on the Kalman filter. An extension of the result to a seasonal ARMA model is also given. We consider the zero mean seasonal moving average model
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
