State Space Models in R

Abstract

We give an overview of some of the software tools available in R, either as built- in functions or contributed packages, for the analysis of state space models. Several illustrative examples are included, covering constant and time-varying models for both univariate and multivariate time series. Maximum likelihood and Bayesian methods to obtain parameter estimates are considered.

Keywords

• Kalman filter
• state space models
• unobserved components
• software tools
• R