ba.boot: Bootstrap after Bootstrap

Description

Bootstrap intervals based on bias-adjusted estimators

Usage

ba.boot(x, nc = 1)

Value

A list of class "sboot" with elements

true: Point estimate of impulse response functions
bootstrap: List of length "nboot" holding bootstrap impulse response functions
SE: Bootstrapped standard errors of estimated covariance decomposition (only if "x" has method "Cramer von-Mises", or "Distance covariances")
nboot: Number of bootstrap iterations
b_length: Length of each block
point_estimate: Point estimate of covariance decomposition
boot_mean: Mean of bootstrapped covariance decompositions
signrest: Evaluated sign pattern
sign_complete: Frequency of appearance of the complete sign pattern in all bootstrapped covariance decompositions
sign_part: Frequency of bootstrapped covariance decompositions which conform the complete predetermined sign pattern. If signrest=NULL, the frequency of bootstrapped covariance decompositions that hold the same sign pattern as the point estimate is provided.
sign_part: Frequency of single shocks in all bootstrapped covariance decompositions which accord to a specific predetermined sign pattern
cov_bs: Covariance matrix of bootstrapped parameter in impact relations matrix
method: Used bootstrap method
VAR: Estimated input VAR object

Arguments

x: SVAR object of class "sboot"
nc: Integer. Number of processor cores

References

Kilian, L. (1998). Small-sample confidence intervals for impulse response functions. Review of Economics and Statistics 80, 218-230.

Examples

Run this code

# \donttest{
# data contains quarterly observations from 1965Q1 to 2008Q3
# x = output gap
# pi = inflation
# i = interest rates
set.seed(23211)
v1 <- vars::VAR(USA, lag.max = 10, ic = "AIC" )
x1 <- id.dc(v1)
summary(x1)

# Bootstrap
bb <- mb.boot(x1, b.length = 15, nboot = 300, n.ahead = 30, nc = 1, signrest = NULL)
summary(bb)
plot(bb, lowerq = 0.16, upperq = 0.84)

# Bias-adjusted bootstrap
bb2 <- ba.boot(bb, nc = 1)
plot(bb2, lowerq = 0.16, upperq = 0.84)
# }