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

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

Usage

RBootVARExoOLS(data, exo_mat, p, B)

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 by m.

p

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

B

Integer. Number of bootstrap samples to generate.

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.

• 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(), RBootVARLasso(), RBootVAROLS(), SearchVARLasso(), StdMat()

Author

Ivan Jacob Agaloos Pesigan

Examples

data <- dat_p2_exo$data
exo_mat <- dat_p2_exo$exo_mat
rb <- RBootVARExoOLS(data = data, exo_mat = exo_mat, p = 2, B = 5)
str(rb)
#> List of 4
#>  $ est : num [1:3, 1:10] 1.1645 0.6747 1.1333 0.358 0.0194 ...
#>  $ boot: num [1:5, 1:30] 1.332 1.029 1.344 0.856 1.414 ...
#>  $ X   : num [1:998, 1:10] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Y   :List of 5
#>   ..$ : num [1:998, 1:3] 1.776 1.491 1.832 -0.341 1.055 ...
#>   ..$ : num [1:998, 1:3] 2.942 2.634 3.101 -0.228 0.443 ...
#>   ..$ : num [1:998, 1:3] 0.521 0.71 1.355 0.522 0.683 ...
#>   ..$ : num [1:998, 1:3] 1.41 1.99 2.45 1.29 2.34 ...
#>   ..$ : num [1:998, 1:3] 1.54 3.78 0.98 0.56 2.01 ...