astsa: Kfilter0 – R documentation

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Kfilter0

Kalman Filter - Time Invariant Model

Returns the filtered values for the basic time invariant state-space model; inputs are not allowed.

Kfilter0(num, y, A, mu0, Sigma0, Phi, cQ, cR)

`num`	number of observations
`y`	data matrix, vector or time series
`A`	time-invariant observation matrix
`mu0`	initial state mean vector
`Sigma0`	initial state covariance matrix
`Phi`	state transition matrix
`cQ`	Cholesky-type decomposition of state error covariance matrix Q – see details below
`cR`	Cholesky-type decomposition of observation error covariance matrix R – see details below

`xp`	one-step-ahead state prediction
`Pp`	mean square prediction error
`xf`	filter value of the state
`Pf`	mean square filter error
`like`	the negative of the log likelihood
`innov`	innovation series
`sig`	innovation covariances
`Kn`	last value of the gain, needed for smoothing

D.S. Stoffer

See also https://www.stat.pitt.edu/stoffer/tsa4/chap6.htm for an explanation of the difference between levels 0, 1, and 2.

Applied Statistical Time Series Analysis

v1.12

GPL-3

Authors

David Stoffer

Initial release

2020-12-20