transformSSM: Transform the SSModel object with multivariate observations

In jrnold/KFAS: Kalman Filter and Smoother for Exponential Family State Space Models.

Description

Function transform.SSModel transforms original model by LDL decomposition or state vector augmentation,

Usage

  transformSSM(object, type = c("ldl", "augment"))

Arguments

object	State space model object from function SSModel.
type	Option "ldl" performs LDL decomposition for covariance matrix H[t], and multiplies the observation equation with the L[t]^-1, so ε[t]* ~ N(0,D[t]). Option "augment" adds ε[t] to the state vector, when Q[t] becomes block diagonal with blocks Q[t] and H[t]. In case of univariate series, option "ldl" only changes the H_type argument of the model to "Diagonal".

Details

As all the functions in KFAS use univariate approach, H[t], a covariance matrix of an observation equation needs to be either diagonal or zero matrix. Function transformSSM performs either the LDL decomposition of the covariance matrix of the observation equation, or augments the state vector with the disturbances of the observation equation.

In case of a LDL decomposition, the new H[t] contains the diagonal part of the decomposition, whereas observations Z[t] and system matrices Z[t] are multiplied with the inverse of L[t].

Value

model

Transformed model.

jrnold/KFAS documentation built on May 19, 2019, 11:55 p.m.