vars (version 1.5-3)

vec2var: Transform a VECM to VAR in levels

Description

An object of formal class 'ca.jo' is transformed to a VAR in level presentation.

Usage

vec2var(z, r = 1)

Arguments

z

An object of class 'ca.jo' generated by function ca.jo() in package 'urca'.

r

The cointegration rank (default is r=1).

Value

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 call to vec2var.

vecm

The supplied object z of formal class ca.jo.

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.

Details

This function enables the user to transform a vector-error-correction model (VECM) into a level-VAR form. The rank of the matrix \(\bold{\Pi}\) has to be submitted, i.e. how many cointegration relationships have been determined according to the outcome of ca.jo().

References

Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.

L<U+34AE5C2F>hl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.

See Also

ca.jo, predict, irf, fevd, Phi, Psi, normality.test, arch.test, serial.test, logLik, plot

Examples

# NOT RUN {
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)
# }