kalman_filter_arma: Kalman filtering a univariate stationary zero-mean ARMA...

Description Usage Arguments Value Examples

Description

Function that applies the Kalman filter to a univariate stationary zero-mean ARMA process. To get the Kalman filter to work the process should be written as a dynamic linear model. The ARMA process written as a dynamic linear model has the form, where y_t = F\bm{x}_t is the observation equation and \bm{x}_t = G\bm{x}_{t-1} + H w_t is the state equation, and Q = \mbox{Var}(H w_t).

Usage

1
kalman_filter_arma(ts, F, G, Q, m0, C0)

Arguments

ts

A univariate time series with zero mean

F

The coefficient matrix in the observation equation, as shown above.

G

The matrix in the state equation as shown above.

Q

The variance matrix of the state equation.

m0

The initial value of \bm{x}_t.

C0

The initial value of the state variance.

Value

The return is a list of the innovations, standardised residuals, and predicted values.

innovations

The innovations.

sd

the standardised residuals.

y_predicted

the predicted values.

Examples

1
kalman_filter_arma(ts = data, F=F, G=G, Q=Q, m0=m0, C0=C0)

hannea/monitor documentation built on May 17, 2019, 2:28 p.m.