Residual Bootstrap for the Vector Autoregressive Model Using Lasso Regularization

Residual Bootstrap for the Vector Autoregressive Model Using Lasso Regularization

Usage

RBootVARLasso(data, p, B, n_lambdas, crit, max_iter, tol)

Arguments

data

Numeric matrix. The time series data with dimensions t by k, where t is the number of observations and k is the number of variables.

p

Integer. The order of the VAR model (number of lags).

B

Integer. Number of bootstrap samples to generate.

n_lambdas

Integer. Number of lambdas to generate.

crit

Character string. Information criteria to use. Valid values include "aic", "bic", and "ebic".

max_iter

Integer. The maximum number of iterations for the coordinate descent algorithm (e.g., max_iter = 10000).

tol

Numeric. Convergence tolerance. The algorithm stops when the change in coefficients between iterations is below this tolerance (e.g., tol = 1e-5).

Value

List with the following elements:

• est: Numeric matrix. Original Lasso estimate of the coefficient matrix.

• boot: Numeric matrix. Matrix of vectorized bootstrap estimates of the coefficient matrix.

• X: Numeric matrix. Original X

• Y: List of numeric matrices. Bootstrapped Y

See also

Other Fitting Autoregressive Model Functions: FitMLVARDynr(), FitMLVARMplus(), FitVARDynr(), FitVARLassoSearch(), FitVARLasso(), FitVARMplus(), FitVAROLS(), LambdaSeq(), ModelVARP1Dynr(), ModelVARP2Dynr(), OrigScale(), PBootVARExoLasso(), PBootVARExoOLS(), PBootVARLasso(), PBootVAROLS(), RBootVARExoLasso(), RBootVARExoOLS(), RBootVAROLS(), SearchVARLasso(), StdMat()

Author

Ivan Jacob Agaloos Pesigan

Examples

rb <- RBootVARLasso(
  data = dat_p2,
  p = 2,
  B = 5,
  n_lambdas = 10,
  crit = "ebic",
  max_iter = 1000,
  tol = 1e-5
)
str(rb)
#> List of 4
#>  $ est : num [1:3, 1:7] 0.79 1 1.067 0.342 0 ...
#>  $ boot: num [1:5, 1:21] 1.28 1.32 1.1 1.51 1.16 ...
#>  $ X   : num [1:998, 1:7] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Y   :List of 5
#>   ..$ : num [1:998, 1:3] 0.158 0.497 0.591 2.332 -0.44 ...
#>   ..$ : num [1:998, 1:3] 0.624 2.284 0.341 -1.494 1.165 ...
#>   ..$ : num [1:998, 1:3] 1.519 -0.337 0.43 1.218 1.419 ...
#>   ..$ : num [1:998, 1:3] -0.385 2.08 1.167 1.428 0.888 ...
#>   ..$ : num [1:998, 1:3] 2.997 0.728 -1.907 0.79 0.149 ...