Fast likelihood evaluation and prediction for nonstationary state space models

Piet De Jong*, Singfat Chu-chun-lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)133-142
Number of pages10
Issue number1
Publication statusPublished - Mar 1994
Externally publishedYes


  • ARIMA model
  • Basic structural model
  • Diffuse
  • Kalman filter
  • Likelihood
  • Nonstationarity
  • Prediction
  • State space


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