convert: Convert DFM to Other State Space Model Formats

In dfms: Dynamic Factor Models

Convert DFM to Other State Space Model Formats

Description

Converts a dfm object to the state space representation used by the dlm or KFAS packages, enabling their forecasting, smoothing, and prediction-interval functionality.

Usage

convert(object, ...)

## S3 method for class 'dfm'
convert(object, to = c("KFAS", "dlm"), ...)

Arguments

object	an object of class 'dfm'.
...	other arguments
to	character, target package format: "dlm" or "KFAS".

Details

The DFM is defined by the state space model:

• Observation equation: X_t = C F_t + e_t, e_t \sim N(0, R)

• Transition equation: F_t = A F_{t-1} + u_t, u_t \sim N(0, Q)

where F_t is the companion-form state vector of length r \cdot p and A is the companion transition matrix [r \cdot p \times r \cdot p].

For dlm: The system matrices map directly — FF = C, GG = A, V = R, W = Q, with initial state m0 = F[1,] and covariance C0 = P[,,1]. The user should pass standardized (scaled and centered) data when using further dlm functions.

For KFAS: An SSModel is built via SSMcustom with Z = C, T = A, R = I, Q = Q, H = R with initial state a1 = F[1,] and covariance P1 = P[,,1]. The standardized data with original missing values restored is embedded in the model object y = X.

Value

For to = "dlm": a dlm object (see dlm).

For to = "KFAS": an SSModel object (see SSModel).

Examples

 ## Not run: 

mod <- DFM(BM14_Q, r=2, p=3)
 # Convert to dlm and run filter extract prediction variance
 if (requireNamespace("dlm", quietly = TRUE)) {
   dlm_mod <- convert(mod, to = "dlm")
   # Pass standardized data (scale each column) to dlmFilter
   fitf <- dlm::dlmFilter(scale(BM14_Q), dlm_mod)
   dlm::dlmSvd2var(fitf$U.R, fitf$D.R)
 }
 # Convert to KFAS and compute prediction intervals
 if (requireNamespace("KFAS", quietly = TRUE)) {
   kfas_mod <- convert(mod, to = "KFAS")
   predict(kfas_mod, n.ahead = 10, interval = "prediction",
           level = 0.95)
 }

## End(Not run)

dfms documentation built on June 19, 2026, 1:06 a.m.