Time Series Analysis and Examples

Kalman Filter Subroutines

This section describes a collection of Kalman filtering and smoothing subroutines for time series analysis; immediately following are three examples using Kalman filtering subroutines. The state space model is a method for analyzing a wide range of time series models. When the time series is represented by the state space model (SSM), the Kalman filter is used for filtering, prediction, and smoothing of the state vector. The state space model is composed of the measurement and transition equations.

The following Kalman filtering and smoothing subroutines are supported:

KALCVF

performs covariance filtering and prediction.

KALCVS

performs fixed-interval smoothing.

KALDFF

performs diffuse covariance filtering and prediction.

KALDFS

performs diffuse fixed-interval smoothing.

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Getting Started

Syntax

Example 10.2: Kalman Filtering: Likelihood Function Evaluation

Example 10.3: Kalman Filtering: SSM Estimation With the EM Algorithm

Example 10.4: Diffuse Kalman Filtering