Estimation of a VAR by utilising OLS per equation.
VAR(y, p = 1, type = c("const", "trend", "both", "none"),
season = NULL, exogen = NULL, lag.max = NULL,
ic = c("AIC", "HQ", "SC", "FPE"))
# S3 method for varest
print(x, digits = max(3, getOption("digits") - 3), ...)A list with class attribute ‘varest’ holding the
following elements:
list of ‘lm’ objects.
The data matrix of the endogenous and explanatory variables.
The data matrix of the endogenous variables
A character, specifying the deterministic regressors.
An integer specifying the lag order.
An integer specifying the dimension of the VAR.
An integer specifying the number of used observations.
An integer specifying the total number of observations.
Either NULL or a matrix object containing
the zero restrictions of the VAR(p).
The call to VAR().
Data item containing the endogenous variables
Integer for the lag order (default is p=1).
Type of deterministic regressors to include.
Inlusion of centered seasonal dummy variables (integer value of frequency).
Inlusion of exogenous variables.
Integer, determines the highest lag order for lag
length selection according to the choosen ic.
Character, selects the information criteria, if
lag.max is not NULL.
Object with class attribute ‘varest’.
the number of significant digits to use when printing.
further arguments passed to or from other methods.
Bernhard Pfaff
Estimates a VAR by OLS per equation. The model is of the following form:
$$ \bold{y}_t = A_1 \bold{y}_{t-1} + \ldots + A_p \bold{y}_{t-p} + CD_t + \bold{u}_t $$
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 as well as centered
seasonal dummy variables and/or exogenous variables (term \(CD_T\), by
setting the type argument to the corresponding value and/or
setting season to the desired frequency (integer) and/or providing a
matrix object for exogen, respectively. The default for type is
const and for season and exogen the default is
set to NULL.
If for lag.max an integer value is provided instead of
NULL (the default), the lag length is determined by the
selected information criteria in ic, the default is Akaike.
Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.
Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.
data(Canada)
VAR(Canada, p = 2, type = "none")
VAR(Canada, p = 2, type = "const")
VAR(Canada, p = 2, type = "trend")
VAR(Canada, p = 2, type = "both")
Run the code above in your browser using DataLab