wild.boot: Wild bootstrap for IRFs of identified SVARs
In alexanderlange53/svars: Data-Driven Identification of SVAR Models

wild.boot

R Documentation

Wild bootstrap for IRFs of identified SVARs

Description

Calculating confidence bands for impulse response functions via wild bootstrap techniques (Goncalves and Kilian, 2004).

Usage

wild.boot(
  x,
  design = "fixed",
  distr = "rademacher",
  n.ahead = 20,
  nboot = 500,
  nc = 1,
  dd = NULL,
  signrest = NULL,
  signcheck = TRUE,
  itermax = 300,
  steptol = 200,
  iter2 = 50,
  rademacher = "deprecated"
)

Arguments

`x`	SVAR object of class "svars"
`design`	character. If design="fixed", a fixed design bootstrap is performed. If design="recursive", a recursive design bootstrap is performed.
`distr`	character. If distr="rademacher", the Rademacher distribution is used to generate the bootstrap samples. If distr="mammen", the Mammen distribution is used. If distr = "gaussian", the gaussian distribution is used.
`n.ahead`	Integer specifying the steps
`nboot`	Integer. Number of bootstrap iterations
`nc`	Integer. Number of processor cores
`dd`	Object of class 'indepTestDist'. A simulated independent sample of the same size as the data. roxIf not supplied, it will be calculated by the function
`signrest`	A list with vectors containing 1 and -1, e.g. c(1,-1,1), indicating a sign pattern of specific shocks to be tested with the help of the bootstrap samples.
`signcheck`	Boolean. Whether the sign pattern should be checked for each bootstrap iteration. Note that this procedure is computationally extremely demanding for high dimensional VARs, since the number of possible permutations of B is K!, where K is the number of variables in the VAR.
`itermax`	Integer. Maximum number of iterations for DEoptim
`steptol`	Integer. Tolerance for steps without improvement for DEoptim
`iter2`	Integer. Number of iterations for the second optimization
`rademacher`	deprecated, use "design" instead.

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
`distr`	Character, whether the Gaussian, Rademacher or Mammen distribution is used in the bootstrap
`design`	character. Whether a fixed design or recursive design bootstrap is performed
`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

Goncalves, S., Kilian, L., 2004. Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics 123, 89-120.
Herwartz, H., 2017. Hodges Lehmann detection of structural shocks - An analysis of macroeconomic dynamics in the Euro Area, Oxford Bulletin of Economics and Statistics

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)

# impulse response analysis with confidence bands
# Checking how often theory based impact relations appear
signrest <- list(demand = c(1,1,1), supply = c(-1,1,1), money = c(-1,-1,1))
bb <- wild.boot(x1, nboot = 500, n.ahead = 30, nc = 1, signrest = signrest)
summary(bb)

# Plotting IRFs with confidance bands
plot(bb, lowerq = 0.16, upperq = 0.84)

# With different confidence levels
plot(bb, lowerq = c(0.05, 0.1, 0.16), upperq = c(0.95, 0.9, 0.84))

# Halls percentile
plot(bb, lowerq = 0.16, upperq = 0.84, percentile = 'hall')

# Bonferroni bands
plot(bb, lowerq = 0.16, upperq = 0.84, percentile = 'bonferroni')

alexanderlange53/svars documentation built on Jan. 31, 2023, 7:50 a.m.