simVAR {bigtime}  R Documentation 
Simulates a VAR(p) with various sparsity patterns
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 = 0.8,
burnin = periods,
sparsity_pattern = c("none", "lasso", "L1", "hvar", "HLag"),
sparsity_options = NULL,
decay = 1/p,
seed = NULL,
...
)
Arguments
periods 
Scalar indicating the desired time series length 
k 
Number of time series 
p 
Maximum lag number. In case of 
coef_mat 
Coefficient matrix in companion form. If not provided, one will be simulated 
const 
Constant term of VAR. Default is zero. Must be either a scalar, in which case it will be broadcasted to a kvector, or a kvector 
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. Expects either a scalar or a vector of length (k*p) 
max_abs_eigval 
Maximum allowed eigenvalue of companion matrix. Only applicable if coefficient matrix is being simulated 
burnin 
Number of time points to be used for burnin 
sparsity_pattern 
The sparsity pattern that should be simulated.
Options are: 
sparsity_options 
Named list of additional options for
when sparsity pattern is lasso (L1) or hvar (HLag). For lasso (L1) the option 
decay 
How much smaller should parameters for later 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 
Value
Returns an object of S3 class bigtime.simVAR
containing the following
Y 
Simulated Data 
periods 
Time series length 
k 
Number of endogenous variables 
p 
Maximum lag length; effective lag length might be shorter due to sparsity patterns 
coef_mat 
Companion form of the coefficient matrix. Will be of
dimensions ( 
is_coef_mat_simulated 

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 
Sparsity pattern used 
sparsity_options 
Extra options for the sparsity patterns used 
seed 
Seed used for the simulation 
Examples
periods < 200 # time series length
k < 5 # number of variables
p < 10 # maximum lag
sparsity_pattern < "HLag" # HLag sparsity structure
sparsity_options < list(zero_min = 0, # variables can be included with all lags
zero_max = 10, # but some could also include no lags
zeroes_in_self = TRUE)
sim < simVAR(periods=periods, k=k, p=p, sparsity_pattern=sparsity_pattern,
sparsity_options=sparsity_options, seed = 12345)
summary(sim)