FCVARboot: Bootstrap Likelihood Ratio Test

In FCVAR: Estimation and Inference for the Fractionally Cointegrated VAR

Bootstrap Likelihood Ratio Test

Description

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.

Usage

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

Arguments

x	A matrix of variables to be included in the system.
k	The number of lags in the system.
r	The cointegrating rank.
optRES	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.
optUNR	An S3 object of class FCVAR_opt that stores the chosen estimation options for the unrestricted model.
B	The number of bootstrap samples.

Value

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

LRbs

A B x 1 vector of simulated likelihood ratio statistics

pv

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

H

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

mBS

The model estimates under the null hypothesis.

mUNR

The model estimates under the alternative hypothesis.

References

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()

Examples

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)
set.seed(42)
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.