Stable algorithms for the state space model

Piet De Jong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Abstract. Numerically stable algorithms are developed for filtering, likelihood evaluation, generalized least squares computation and smoothing where data are generated by a state space model. The algorithms handle diffuse initial states in a numerically safe way. Singular innovation covariance matrices, such as those which arise in series with missing values, are dealt with. The algorithms generalize stable algorithms for ordinary least‐squares computations.

Original languageEnglish
Pages (from-to)143-157
Number of pages15
JournalJournal of Time Series Analysis
Issue number2
Publication statusPublished - 1991
Externally publishedYes


  • diffuse
  • Kalman filter
  • smoothing
  • State space model


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