| vec2var {vars} | R Documentation |
An object of formal class 'ca.jo' is transformed to a VAR in level presentation.
vec2var(z, r = 1)
z |
An object of class 'ca.jo' generated by function
|
r |
The cointegration rank (default is |
This function enables the user to transform a vector-error-correction
model (VECM) into a level-VAR form. The rank of the matrix
\bold{Π} has to be submitted, i.e. how many
cointegration relationships have been determined according to the
outcome of ca.jo().
A list with class attribute ‘vec2var’ holding the
following elements:
deterministic |
The matrix of deterministic coefficients. |
A |
A list with matrix object(s) containing the coefficients for the lagged endogenous variables. |
p |
The lag-order of the estimated VAR-process. |
K |
The count of endogenous variables. |
y |
A dataframe with the endogenous variables in levels. |
obs |
An integer signifying the count of used observations. |
totobs |
An integer signifying the total number of observations, i.e including observations taken as starting values.. |
call |
The |
vecm |
The supplied object |
datamat |
A dataframe with the used dataset. |
resid |
A matrix with the residuals from the empirical VAR(p). |
r |
Intefer, the assigned co-integration rank from the call. |
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.
ca.jo, predict, irf,
fevd, Phi, Psi,
normality.test, arch.test,
serial.test, logLik, plot
library(urca) data(finland) sjf <- finland sjf.vecm <- ca.jo(sjf, ecdet = "none", type = "eigen", K = 2, spec = "longrun", season = 4) vec2var(sjf.vecm, r = 2)