Creates a random coefficient matrix
create_rand_coef_mat(
k,
p,
max_abs_eigval = 0.8,
sparsity_pattern = c("none", "lasso", "hvar"),
sparsity_options = NULL,
decay = 0.5,
...
)Returns a coefficient matrix in companion form of dimension kpxkp.
Number of time series
Number of lags
if < 1, then the VAR will be stable
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
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.
How fast should coefficients shrink when the lag increases.
Not currently used