Object Reference : Object View and Procedure Reference : Var
Declare a var (Vector Autoregression) object.
var var_name
var var_name.ls(options) lag_pairs endog_list [@ exog_list]
var var_name.ec(trend, n) lag_pairs endog_list [@ exog_list]
Declare the var as a name, or a name followed by an estimation method and specification.
The Var::ls method estimates an unrestricted VAR using equation-by-equation OLS. You must specify the order of the VAR (using one or more pairs of lag intervals), and then provide a list of series or groups to be used as endogenous variables. You may include exogenous variables such as trends and seasonal dummies in the VAR by including an “@-sign” followed by a list of series or groups. A constant is automatically added to the list of exogenous variables; to estimate a specification without a constant, you should use the option “noconst”.
See Var::ec for the error correction specification of a VAR.
Do not include a constant in the VAR specification (when combining declaration with Var::ls method).
Force the dialog to appear from within a program.
Print the estimation result if the estimation procedure is specified.
var mvar.ls 1 4 8 8 m1 gdp tb3 @ @trend
declares and estimates an unrestricted VAR named MVAR with three endogenous variables (M1, GDP, TB3), five lagged terms (lags 1 through 4, and 8), a constant, and a linear trend.
var jvar.ec(c,2) 1 4 m1 gdp tb3
declares and estimates an error correction model named JVAR with three endogenous variables (M1, GDP, TB3), four lagged terms (lags 1 through 4), two cointegrating relations. The c” option assumes a linear trend in data but only a constant in the cointegrating relations.
See “Vector Autoregression and Error Correction Models” for a discussion of vector autoregressions.
See Var::ls for standard VAR estimation, and Var::ec for estimation of error correction models.