multiple_blocks: Multi-level Dynamic Factor Model - Multiple Blocks (MLDFM)

In FARS: Factor-Augmented Regression Scenarios

Multi-level Dynamic Factor Model - Multiple Blocks (MLDFM)

Description

Estimates a Multi-Level Dynamic Factor Model (MLDFM) using Sequential Least Squares Estimation approach

Usage

multiple_blocks(
  data,
  global,
  local,
  middle_layer,
  block_ind,
  tol,
  max_iter,
  method
)

Arguments

data	A numeric matrix or data frame containing the time series data (T × N).
global	Integer. Number of global factors extracted from the entire dataset.
local	Integer vector of length blocks. Specifies the number of local factors for each block.
middle_layer	Named list. Each name is a string specifying a group of blocks (e.g., "1-3" or "2-3"), and each value is the number of factors to extract.
block_ind	Integer vector. End column indices for each block. Must be of length blocks and in increasing order.
tol	Numeric. The tolerance level for the residual sum of squares (RSS) minimization process. Used as a convergence criterion.
max_iter	Integer. The maximum number of iterations allowed for the RSS minimization process.
method	Integer. Method used to initialize the factors: 0 for Canonical Correlation Analysis (CCA), 1 for Principal Component Analysis (PCA).

Value

A list with elements:

factors

Matrix of estimated factors.

loadings

Matrix of factor loadings.

residuals

Matrix of residuals.

fitted

Matrix of fitted values.

method

Initialization method used (CCA or PCA).

iterations

Number of iterations before convergence.

factors_list

List of estimated factors for each node.

FARS documentation built on Feb. 17, 2026, 5:06 p.m.