Exact initial kalman filtering and smoothing for nonstationary time series models

Siem Jan Koopman*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This article presents a new exact solution for the initialization of the Kalman filter for state space models with diffuse initial conditions. For example, the regression model with stochastic trend, seasonal and other nonstationary autoregressive integrated moving average components requires a (partially) diffuse initial state vector. The proposed analytical solution is easy to implement and computationally efficient. The exact solution for smoothing is also given. Missing observations are handled in a straightforward manner. All proofs rely on elementary results.

Original languageEnglish
Pages (from-to)1630-1638
Number of pages9
JournalJournal of the American Statistical Association
Volume92
Issue number440
DOIs
Publication statusPublished - 1 Dec 1997
Externally publishedYes

Keywords

  • Autoregressive integrated moving average component models
  • Diffuse initial conditions
  • Likelihood function and score vector
  • Missing observations
  • State space

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