| ba.boot {svars} | R Documentation |
Bootstrap after Bootstrap
Description
Bootstrap intervals based on bias-adjusted estimators
Usage
ba.boot(x, nc = 1)
Arguments
x |
SVAR object of class "sboot" |
nc |
Integer. Number of processor cores |
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 |
References
Kilian, L. (1998). Small-sample confidence intervals for impulse response functions. Review of Economics and Statistics 80, 218-230.
See Also
Examples
# 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)