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 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 Constant term of VAR. Default is zero. Must be either a scalar, in which case it will be broadcasted to a k-vector, or a k-vector 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: "none" for a dense VAR, "lasso" (or "L1") for a VAR with random zeroes, and "hvar" (or "HLag") for an elementwise hierarchical sparsity pattern sparsity_options Named list of additional options for when sparsity pattern is lasso (L1) or hvar (HLag). For lasso (L1) the option num_zero determines the number of zeros. For hvar (HLag), 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 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 e_dist

### 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 (kp)x(kp). First k rows correspond to the actual coefficient matrix. is_coef_mat_simulated TRUE if the coef_mat was simulated, 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 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)


[Package bigtime version 0.2.1 Index]