FCVARboot: Bootstrap Likelihood Ratio Test

View source: R/FCVAR_post.R

FCVARbootR Documentation

Bootstrap Likelihood Ratio Test


FCVARboot generates a distribution of a likelihood ratio test statistic using a wild bootstrap, following the method of Boswijk, Cavaliere, Rahbek, and Taylor (2016). It takes two sets of options as inputs to estimate the model under the null and the unrestricted model.


FCVARboot(x, k, r, optRES, optUNR, B)



A matrix of variables to be included in the system.


The number of lags in the system.


The cointegrating rank.


An S3 object of class FCVAR_opt that stores the chosen estimation options for the restricted model, as generated from FCVARoptions(), with adjustments as necessary.


An S3 object of class FCVAR_opt that stores the chosen estimation options for the unrestricted model.


The number of bootstrap samples.


A list FCVARboot_stats containing the estimation results, including the following parameters:


A B x 1 vector of simulated likelihood ratio statistics


An approximate p-value for the likelihood ratio statistic based on the bootstrap distribution.


A list containing the likelihood ratio test results. It is identical to the output from FCVARhypoTest, with one addition, namely H$pvBS which is the bootstrap p-value


The model estimates under the null hypothesis.


The model estimates under the alternative hypothesis.


Boswijk, Cavaliere, Rahbek, and Taylor (2016) "Inference on co-integration parameters in heteroskedastic vector autoregressions," Journal of Econometrics 192, 64-85.

See Also

FCVARoptions to set default estimation options. FCVARestn is called to estimate the models under the null and alternative hypotheses.

Other FCVAR postestimation functions: FCVARhypoTest(), GetCharPolyRoots(), MVWNtest(), plot.FCVAR_roots(), summary.FCVAR_roots(), summary.MVWN_stats()


opt <- FCVARoptions()
opt$gridSearch   <- 0 # Disable grid search in optimization.
opt$dbMin        <- c(0.01, 0.01) # Set lower bound for d,b.
opt$dbMax        <- c(2.00, 2.00) # Set upper bound for d,b.
opt$constrained  <- 0 # Impose restriction dbMax >= d >= b >= dbMin ? 1 <- yes, 0 <- no.
x <- votingJNP2014[, c("lib", "ir_can", "un_can")]
opt$plotRoots <- 0
optUNR <- opt
optRES <- opt
optRES$R_Beta <- matrix(c(1, 0, 0), nrow = 1, ncol = 3)
FCVARboot_stats <- FCVARboot(x, k = 2, r = 1, optRES, optUNR, B = 2)
# In practice, set the number of bootstraps so that (B+1)*alpha is an integer,
# where alpha is the chosen level of significance.
# For example, set B = 999 (but it takes a long time to compute).

FCVAR documentation built on May 5, 2022, 9:06 a.m.