Parametric Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Ordinary Least Squares

Parametric Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Ordinary Least Squares

Usage

PBootVARExoOLS(data, exo_mat, p, B, burn_in)

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.

exo_mat

Numeric matrix. Matrix of exogenous variables with dimensions t + burn_in by m. If the number of rows is equal to t, set burn_in = 0.

p

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

B

Integer. Number of bootstrap samples to generate.

burn_in

Integer. Number of burn-in observations to exclude before returning the results in the simulation step.

Value

List with the following elements:

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

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

See also

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

Author

Ivan Jacob Agaloos Pesigan

Examples

data <- dat_p2_exo$data
exo_mat <- dat_p2_exo$exo_mat
pb <- PBootVARExoOLS(
  data = data,
  exo_mat = exo_mat,
  p = 2,
  B = 5,
  burn_in = 0
)
str(pb)
#> List of 2
#>  $ est : num [1:3, 1:10] 1.1645 0.6747 1.1333 0.358 0.0194 ...
#>  $ boot: num [1:5, 1:30] 1.28 0.86 1.3 1.39 1.14 ...