simVAR: Simulates a VAR(p) with various sparsity patterns

In YiHou98/bigtime: Sparse Estimation of Large Time Series Models

Description

Simulates a VAR(p) with various sparsity patterns

Usage

simVAR(
  periods,
  k,
  p,
  coef_mat = NULL,
  const = rep(0, k),
  e_dist = rnorm,
  init_y = rep(0, k * p),
  max_abs_eigval = runif(1, 0, 1),
  burnin = periods,
  sparsity_pattern = c("none", "lasso", "hvar"),
  sparsity_options = NULL,
  decay = 1/p,
  seed = NULL,
  ...
)

Arguments

periods	Number of periods to simulate
k	Number of time series
p	Maximum lag number. In case of sparsity_patter="none" this will be the actual number of lags for all variables
coef_mat	Coefficient matrix in companion form. If not provided, one will be simulated
const	constent term of VAR. Default is zero
e_dist	Either a function taking argument n indicating the number of variables in the system, or a matrix of dimensions k x (periods+burnin)
init_y	Initial values. Defaults to zero
max_abs_eigval	Maximum Eigenvalue of companion matrix. Only applicable if coefficient matrix is being simulated
burnin	Number of periods to be used for burnin
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 hirichical 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 cofficients of a variable on itself should be zero.
decay	How much smaller should parameters for laters lags be. The smaller, the larger will early parameters be w.r.t. later ones.
seed	Seed to be used for the simulation
...	additional arguments passed to e_dist

Value

Returns an object of S3 class bigtime.simVAR containing the following

Y	Simulated Data
periods	Number of periods of simulation
k	Number of endogenous variables
p	Maximum lag length; might be shorter due to sparsity patterns
coef_mat	Coefficient Matrix used
is_coef_mat_simulated	TRUE if the coef_mat was simulted, FALSE if it was user provided
const	Constant term
e_dist	Errors used in the construction of the data
init_y	Initial conditions
max_abs_eigval	Maximum eigenvalue to which the companion matrix was constraint
burnin	Burnin period used
sparsity_pattern	Which sparsity pattern was used
sparsity_options	Extra options for the sparsity patterns that were used
seed	seed used for the simulation

YiHou98/bigtime documentation built on Dec. 18, 2021, 7:26 p.m.