VAR.Wald | R Documentation |
Wald test for zero parameter restrictions based on system VAR estimation
Bootstrap option is available: iid bootstrap or wild bootstrap
Bootstrap is conducted under the null hypothesis using estimated GLS estimation: see Kim (2014)
VAR.Wald(x, p, restrict, type = "const",bootstrap=0,nb=500)
x |
data matrix in column |
p |
VAR order |
restrict |
Restriction matrix under H0 |
type |
"const" for the AR model with intercept only, "const+trend" for the AR model with intercept and trend |
bootstrap |
0 for no bootstrap; 1 for iid bootstrap; 2 for wild bootstrap |
nb |
the number of bootstrap iterations |
Restriction matrix is of m by 3 matrix where m is the number of restrictions. A typical row of this matrix (k,i,j), which means that (i,j) element of Ak matrix is set to 0. Ak is a VAR coefficient matrix (k = 1,....p). Under H1, the model is full VAR.
The bootstrap test is conducted using the GLS estimation under the parameter restrictions implied by the null hypothesis: see Kim (2014) for details.
Kim (2014) found that the bootstrap based on OLS can show inferior small sample properties.
There are two versions of the bootstrap: the first is based on the iid resampling and the second based on wild bootstrapping.
The Wild bootstrap is conducted with Mammen's two-point distribution.
Fstat |
Wald test statistic |
pval |
p-value of the test based on F-distribution |
Boot.pval |
p-value of the test based on bootstrapping |
See Chapter 3 of Lutkepohl
Jae H. Kim
Lutkepohl, H. 2005, New Introduction to Multiple Time Series Analysis, Springer.
Kim, J.H. 2014, Testing for parameter restrictions in a stationary VAR model: a bootstrap alternative. Economic Modelling, 41, 267-273.
data(dat)
#replicating Section 3.6.2 of Lutkepohl (2005)
restrict = rbind( c(1,1,2),c(1,1,3), c(2,1,2),c(2,1,3))
VAR.Wald(dat,p=2,restrict,type="const")
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