<code>kf</code> computes the innovations and the conditional states with the Kalman
filter algorithm.

Build customized transfer function and ARIMA models with multiple operators
and parameter restrictions. Provides tools for model identification, estimation
using exact or conditional maximum likelihood, diagnostic checking, automatic outlier
detection, calendar effects, forecasting, and seasonal adjustment. The new version
also supports unobserved component ARIMA model specification and estimation
for structural time series analysis.

Jose L. Gallego

tfarima

Transfer Function and ARIMA Models

kf function

<dl><dt>mdl</dt>
<dd>an object of class <code>ssm</code>.</dd>
<dt>z</dt>
<dd>time series to be filtered when it differs from the model series.</dd>
<dt>x0</dt>
<dd>initial state vector.</dd>
<dt>P0</dt>
<dd>covariance matrix of x1.</dd>
<dt>filtered</dt>
<dd>logical. If TRUE, the filtered states x_{t|t} and their
covariance matrices P_{t|t} are returned. Otherwise, the forecasted states 
x_{t|t-1} and thier covariance matrices P_{t|t-1} are returned.</dd>
<dt>...</dt>
<dd>additional arguments.</dd></dl>

Arguments

Kalman filter for SS models — kf

<dl>

<dt>mdl</dt>
<dd>an object of class <code>ssm</code>.</dd>


<dt>z</dt>
<dd>time series to be filtered when it differs from the model series.</dd>


<dt>x0</dt>
<dd>initial state vector.</dd>


<dt>P0</dt>
<dd>covariance matrix of x1.</dd>


<dt>filtered</dt>
<dd>logical. If TRUE, the filtered states x_{t|t} and their
covariance matrices P_{t|t} are returned. Otherwise, the forecasted states 
x_{t|t-1} and thier covariance matrices P_{t|t-1} are returned.</dd>


<dt>...</dt>
<dd>additional arguments.</dd>

</dl>

kf: Kalman filter for SS models

Description

Usage

Value

Arguments