create_rand_coef_mat: Creates a random coefficient matrix

In ineswilms/bigtime: Sparse Estimation of Large Time Series Models

Creates a random coefficient matrix

Description

Creates a random coefficient matrix

Usage

create_rand_coef_mat(
  k,
  p,
  max_abs_eigval = 0.8,
  sparsity_pattern = c("none", "lasso", "hvar"),
  sparsity_options = NULL,
  decay = 0.5,
  ...
)

Arguments

k	Number of time series
p	Number of lags
max_abs_eigval	if < 1, then the VAR will be stable
sparsity_pattern	The sparsity pattern that should be simulated. Options are: "none" for a dense VAR, "lasso" for a VAR with random zeroes, and "hvar" for an elementwise hierarchical sparsity pattern
sparsity_options	Named list of additional options for when sparsity pattern is lasso or hvar. For lasso the option num_zero determines the number of zeros. For hvar, the options zero_min (zero_max) give the minimum (maximum) of zeroes for each variable in each equation, and the option zeroes_in_self (boolean) determines if any of the coefficients of a variable on itself should be zero.
decay	How fast should coefficients shrink when the lag increases.
...	Not currently used

Value

Returns a coefficient matrix in companion form of dimension kpxkp.

ineswilms/bigtime documentation built on Aug. 24, 2023, 6:57 a.m.