Estimates a Multi-Level Dynamic Factor Model (MLDFM) using Sequential Least Squares Estimation approach
multiple_blocks(
data,
global,
local,
middle_layer,
block_ind,
tol,
max_iter,
method
)A list with elements:
Matrix of estimated factors.
Matrix of factor loadings.
Matrix of residuals.
Matrix of fitted values.
Initialization method used (CCA or PCA).
Number of iterations before convergence.
List of estimated factors for each node.
A numeric matrix or data frame containing the time series data (T × N).
Integer. Number of global factors extracted from the entire dataset.
Integer vector of length blocks. Specifies the number of local factors for each block.
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.
Integer vector. End column indices for each block. Must be of length blocks and in increasing order.
Numeric. The tolerance level for the residual sum of squares (RSS) minimization process. Used as a convergence criterion.
Integer. The maximum number of iterations allowed for the RSS minimization process.
Integer. Method used to initialize the factors: 0 for Canonical Correlation Analysis (CCA), 1 for Principal Component Analysis (PCA).