Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/81.1.133
Title: Fast likelihood evaluation and prediction for nonstationary state space models
Authors: Jong, P.D.
Chu-chun-lin, S. 
Keywords: ARIMA model
Basic structural model
Diffuse
Kalman filter
Likelihood
Nonstationarity
Prediction
State space
Issue Date: 1994
Source: Jong, P.D.,Chu-chun-lin, S. (1994). Fast likelihood evaluation and prediction for nonstationary state space models. Biometrika 81 (1) : 133-142. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/81.1.133
Abstract: SUMMARY: A recursive procedure for initializing the Kalman filter is displayed. The recursion is for nonstationary state space models. The procedure imposes small computational and programming burden over and above the Kalman filter. The procedure is superior to other suggested approaches in both computational speed and general applicability. General properties of the method are investigated. Details of the initialization for the ARIMA (p,d,q) and basic structural models are considered. © 1994 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/45035
ISSN: 00063444
DOI: 10.1093/biomet/81.1.133
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