kfilter: Kalman Filtering

In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Kalman Filtering

Description

Function kfilter runs the Kalman filter for the given model, and returns the filtered estimates and one-step-ahead predictions of the states \alpha_t given the data up to time t.

Usage

kfilter(model, ...)

## S3 method for class 'lineargaussian'
kfilter(model, ...)

## S3 method for class 'nongaussian'
kfilter(model, ...)

Arguments

model	Model of class lineargaussian, nongaussian or ssm_nlg.
...	Ignored.

Details

For non-Gaussian models, the filtering is based on the approximate Gaussian model.

Value

List containing the log-likelihood (approximate in non-Gaussian case), one-step-ahead predictions at and filtered estimates att of states, and the corresponding variances Pt and Ptt up to the time point n+1 where n is the length of the input time series.

See Also

bootstrap_filter

Examples

x <- cumsum(rnorm(20))
y <- x + rnorm(20, sd = 0.1)
model <- bsm_lg(y, sd_level = 1, sd_y = 0.1)
ts.plot(cbind(y, x, kfilter(model)$att), col = 1:3)

bssm documentation built on Nov. 2, 2023, 6:25 p.m.