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.
|Number of pages
|Journal of Time Series Analysis
|Published - 1991
- Kalman filter
- State space model