Target group
Time series modelers and analysts who want to take advantage of a flexible and visual approach to modeling sequential data
Course structure
State Space Models
- introduction
- reasons for using a state space model
- state space model framework
Basic Modeling Using the SSM Procedure
- identifying state space model components
- fitting basic models
Introduction to the Kalman Filter and Estimation in the SSM Framework
- state space models and regression
- filtering
- diffuse starting values
- filtering results
- smoothed estimates
More Modeling Examples Using the SSM Procedure
- accommodating an endogenous input variable in an SSM
- Demonstration: Specifying and estimating a transfer function model in the SSM procedure
- a multivariate model
- cointegration
