arch.test {vars} | R Documentation |
ARCH-LM test
Description
This function computes univariate and multivariate ARCH-LM tests for a VAR(p).
Usage
arch.test(x, lags.single = 16, lags.multi = 5, multivariate.only = TRUE)
Arguments
x |
Object of class ‘ |
lags.single |
An integer specifying the lags to be used for the univariate ARCH statistics. |
lags.multi |
An integer specifying the lags to be used for the multivariate ARCH statistic. |
multivariate.only |
If |
Details
The multivariate ARCH-LM test is based on the following regression (the univariate test can be considered as special case of the exhibtion below and is skipped):
vech(\bold{\hat{u}}_t \bold{\hat{u}}_t') = \bold{\beta}_0 + B_1
vech(\bold{\hat{u}}_{t-1} \bold{\hat{u}}_{t-1}') + \ldots + B_q
vech(\bold{\hat{u}}_{t-q} \bold{\hat{u}}_{t-q}' + \bold{v}_t)
whereby \bold{v}_t
assigns a spherical error process and
vech
is the column-stacking operator for symmetric matrices
that stacks the columns from the main diagonal on downwards. The
dimension of \bold{\beta}_0
is \frac{1}{2}K(K +1)
and for
the coefficient matrices B_i
with i=1, \ldots, q
,
\frac{1}{2}K(K +1) \times \frac{1}{2}K(K +1)
. The null
hypothesis is: H_0 := B_1 = B_2 = \ldots = B_q = 0
and the
alternative is: H_1: B_1 \neq 0 or B_2 \neq 0 or \ldots B_q \neq
0
.
The test statistic is:
VARCH_{LM}(q) = \frac{1}{2}T K (K + 1)R_m^2 \quad ,
with
R_m^2 = 1 - \frac{2}{K(K+1)}tr(\hat{\Omega} \hat{\Omega}_0^{-1})
\quad ,
and \hat{\Omega}
assigns the covariance matrix of the above
defined regression model. This test statistic is distributed as
\chi^2(qK^2(K+1)^2/4)
.
Value
A list with class attribute ‘varcheck
’ holding the
following elements:
resid |
A matrix with the residuals of the VAR. |
arch.uni |
A list with objects of class ‘ |
arch.mul |
An object with class attribute ‘ |
Note
This function was named arch
in earlier versions of package
vars; it is now deprecated. See vars-deprecated
too.
Author(s)
Bernhard Pfaff
References
Doornik, J. A. and D. F. Hendry (1997), Modelling Dynamic Systems Using PcFiml 9.0 for Windows, International Thomson Business Press, London.
Engle, R. F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50: 987-1007.
Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.
Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.
See Also
Examples
data(Canada)
var.2c <- VAR(Canada, p = 2, type = "const")
arch.test(var.2c)