Psi | R Documentation |
Returns the estimated orthogonalised coefficient matrices of the moving average representation of a stable VAR(p) as an array.
## S3 method for class 'varest'
Psi(x, nstep=10, ...)
## S3 method for class 'vec2var'
Psi(x, nstep=10, ...)
x |
An object of class ‘ |
nstep |
An integer specifying the number of othogonalised moving error coefficient matrices to be calculated. |
... |
Dots currently not used. |
In case that the components of the error process are instantaneously
correlated with each other, that is: the off-diagonal elements of the
variance-covariance matrix \Sigma_u
are not null, the impulses
measured by the \Phi_s
matrices, would also reflect disturbances
from the other variables. Therefore, in practice a Choleski
decomposition has been propagated by considering \Sigma_u = PP'
and the
orthogonalised shocks \bold{\epsilon}_t = P^{-1}\bold{u}_t
. The
moving average representation is then in the form of:
\bold{y}_t = \Psi_0 \bold{\epsilon}_t + \Psi_1
\bold{\epsilon}_{t-1} + \Psi \bold{\epsilon}_{t-2} + \ldots ,
whith \Psi_0 = P
and the matrices \Psi_s
are computed
as \Psi_s = \Phi_s P
for s = 1, 2, 3, \ldots
.
An array with dimension (K \times K \times nstep + 1)
holding the
estimated orthogonalised coefficients of the moving average representation.
The first returned array element is the starting value, i.e.,
\Psi_0
. Due to the utilisation of the Choleski decomposition,
the impulse are now dependent on the ordering of the vector elements
in \bold{y}_t
.
Bernhard Pfaff
Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.
Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.
Phi
, VAR
, SVAR
,
vec2var
data(Canada)
var.2c <- VAR(Canada, p = 2, type = "const")
Psi(var.2c, nstep=4)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.