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Parametric Bootstrap for the Vector Autoregressive Model Using Ordinary Least Squares

Source: R/RcppExports.R
PBootVAROLS.Rd

Parametric Bootstrap for the Vector Autoregressive Model Using Ordinary Least Squares

Usage

PBootVAROLS(data, 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.

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(), PBootVARExoOLS(), PBootVARLasso(), RBootVARExoLasso(), RBootVARExoOLS(), RBootVARLasso(), RBootVAROLS(), SearchVARLasso(), StdMat()

Author

Ivan Jacob Agaloos Pesigan

Examples

pb <- PBootVAROLS(data = dat_p2, p = 2, B = 5, burn_in = 20)
str(pb)
#> List of 2
#>  $ est : num [1:3, 1:7] 0.79 1.0002 1.0667 0.3684 0.0133 ...
#>  $ boot: num [1:5, 1:21] 0.809 0.521 0.692 0.645 0.855 ...