function that implements the fast bootstrap algorithm as described in Roodman et al (2019)
boot_algo1(
preprocessed_object,
boot_iter,
point_estimate,
impose_null,
r,
sign_level,
param,
p_val_type,
nthreads,
type,
full_enumeration,
small_sample_correction,
heteroskedastic,
seed
)A list: output of the preprocess2 function.
number of bootstrap iterations
The point estimate of the test parameter from the regression model.
If TRUE, the null is not imposed on the bootstrap distribution. This is what Roodman et al call the "WCU" bootstrap. With impose_null = FALSE, the null is imposed ("WCR").
Shifts the null hypothesis.
The significance level.
name of the test parameter.
type Type of p-value. By default "two-tailed". Other options: "equal-tailed", ">", "<"
The number of threads. Can be: a) an integer lower than, or equal to, the maximum number of threads; b) 0: meaning all available threads will be used; c) a number strictly between 0 and 1 which represents the fraction of all threads to use. The default is to use 50\ set permanently the number of threads used within this package using the function ...
character or function. The character string specifies the type of boostrap to use: One of "rademacher", "mammen", "norm" and "webb". Alternatively, type can be a function(n) for drawing wild bootstrap factors. "rademacher" by default.
Is full enumeration employed? Full enum. is used if N_G^2 < boot_iter for Mammen and Rademacher weights
The small sample correction to be applied. See ssc().
Logical - if TRUE, run a heteroskedastic. If FALSE, run wild cluster bootstrap.
Integer scalar. Either set via boottest()'s seed argument or inherited from R's global seed (set via set.seed)
A list of ...