Estimation of a VAR by utilising OLS per equation.
Usage
VAR(y, p = 1, type = c("const", "trend", "both", "none"))
Arguments
y
Data item containing the endogenous variables
p
Integer for the lag order (default is p=1).
type
Type of deterministic regressors to include.
Value
A list with class attribute varest holding the
following elements:
varresultlist of lm objects.
residA matrix object of the residuals.
datamatThe data matrix of the endogenous and explanatory variables.
yThe data matrix of the endogenous variables
typeA character, specifying the deterministic regressors.
pAn integer specifying the lag order.
KAn integer specifying the dimension of the VAR.
obsAn integer specifying the number of used observations.
totobsAn integer specifying the total number of observations.
restrictionsEither NULL or a matrix object containing
the zero restrictions of the VAR(p).
callThe call to VAR().
encoding
latin1
concept
VAR
Vector autoregressive model
VAR
Details
Estimates a VAR by OLS per equation. The model is of the following
form:
$$\bold{y}_t = CD_t + A_1 \bold{y}_{t-1} + \ldots + A_p \bold{y}_{t-p} + \bold{u}_t$$
where $\bold{y}_t$ is a $K \times 1$ vector of endogenous
variables and $u_t$ assigns a spherical disturbance term of the
same dimension. The coefficient matrices $A_1, \ldots, A_p$ are of
dimension $K \times K$. In addition, either a constant and/or a
trend can be included as deterministic regressors (term $CD_T$, by
setting the type argument to the corresponding value. The
default is const.
References
Hamilton, J. (1994), Time Series Analysis, Princeton
University Press, Princeton.
L�tkepohl, H. (2006), New Introduction to Multiple Time Series
Analysis, Springer, New York.