help var dialog: var
also see: var postestimation
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Title
[TS] var -- Vector autoregressive models
Syntax
var depvarlist [if] [in] [, options]
options description
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Model
noconstant suppress constant term
lags(numlist) use lags numlist in the underlying VAR
exog(varlist) use exogenous variables varlist
Model 2
constraints(numlist) apply specified linear constraints
nolog suppress SURE iteration log
iterate(#) set maximum number of iterations for SURE;
default is iterate(1600)
tolerance(#) set convergence tolerance of SURE
noisure use one-step SURE
dfk make small-sample degrees-of-freedom adjustment
small report small-sample t and F statistics
nobigf do not compute parameter vector for
coefficients implicitly set to zero
Reporting
level(#) set confidence level; default is level(95)
lutstats report Lütkepohl lag-order selection statistics
nocnsreport do not display constraints
display_options control spacing
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
You must tsset your data before using var; see [TS] tsset.
depvarlist and varlist may contain time-series operators; see tsvarlist.
by, rolling, statsby, and xi are allowed; see prefix.
See [TS] var postestimation for features available after estimation.
Menu
Statistics > Multivariate time series > Vector autoregression (VAR)
Description
var fits a multivariate time-series regression of each dependent variable
on lags of itself and on lags of all the other dependent variables. var
also fits a variant of vector autoregressive (VAR) models known as the
VARX model, which also includes exogenous variables. See [TS] var intro
for a list of commands that are used in conjunction with var.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
lags(numlist) specifies the lags to be included in the model. The
default is lags(1 2). This option takes a numlist and not simply an
integer for the maximum lag. For example, lags(2) would include only
the second lag in the model, whereas lags(1/2) would include both the
first and second lags in the model. See numlist and tsvarlist for
more discussion of numlists and lags.
exog(varlist) specifies a list of exogenous variables to be included in
the VAR.
+---------+
----+ Model 2 +----------------------------------------------------------
constraints(numlist); see [R] estimation options.
nolog suppresses the log from the iterated seemingly unrelated regression
algorithm. By default, the iteration log is displayed when the
coefficients are estimated through iterated seemingly unrelated
regression. When the constraints() option is not specified, the
estimates are obtained via OLS, and nolog has no effect. For this
reason, nolog can be specified only when constraints() is specified.
Similarly, nolog cannot be combined with noisure.
iterate(#) specifies an integer that sets the maximum number of
iterations when the estimates are obtained through iterated seemingly
unrelated regression. By default, the limit is 1,600. When
constraints() is not specified, the estimates are obtained using OLS,
and iterate() has no effect. For this reason, iterate() can be
specified only when constraints() is specified. Similarly, iterate()
cannot be combined with noisure.
tolerance(#) specifies a number greater than zero and less than 1 for the
convergence tolerance of the iterated seemingly unrelated regression
algorithm. By default, the tolerance is 1e-6. When the
constraints() option is not specified, the estimates are obtained
using OLS, and tolerance() has no effect. For this reason,
tolerance() can be specified only when constraints() is specified.
Similarly, tolerance() cannot be combined with noisure.
noisure specifies that the estimates in the presence of constraints be
obtained through one-step seemingly unrelated regression. By
default, var obtains estimates in the presence of constraints through
iterated seemingly unrelated regression. When constraints() is not
specified, the estimates are obtained using OLS, and noisure has no
effect. For this reason, noisure can be specified only when
constraints() is specified.
dfk specifies that a small-sample degrees-of-freedom adjustment be used
when estimating the error variance-covariance matrix. Specifically,
1/(T-mparms) is used instead of the large-sample divisor 1/T, where
mparms is the average number of parameters in the functional form for
y_t over the K equations.
small causes var to report small-sample t and F statistics instead of the
large-sample normal and chi-squared statistics.
nobigf requests that var not save the estimated parameter vector that
incorporates coefficients that have been implicitly constrained to be
zero, such as when some lags have been omitted from a model. e(bf)
is used for computing asymptotic standard errors in the
postestimation commands irf create and fcast compute. Therefore,
specifying nobigf implies that the asymptotic standard errors will
not be available from irf create and fcast compute. See Fitting
models with some lags excluded in [TS] var.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
lutstats specifies that the Lütkepohl versions of the lag-order selection
statistics be reported. See Methods and formulas in [TS] varsoc for
a discussion of these statistics.
nocnsreport; see [R] estimation options.
display_options: vsquish; see [R] estimation options.
The following option is available with var but is not shown in the dialog
box:
coeflegend; see [R] estimation options.
Examples
Setup
. webuse lutkepohl2
. tsset
Fit vector autoregressive model with 2 lags (the default)
. var dln_inv dln_inc dln_consump
Fit vector autoregressive model restricted to specified period
. var dln_inv dln_inc dln_consump if qtr<=tq(1978q4)
Same as above, but include first, second, and third lags in model
. var dln_inv dln_inc dln_consump if qtr<=tq(1978q4), lags(1/3)
Same as above, but report the Lütkepohl versions of the lag-order
selection statistics
. var dln_inv dln_inc dln_consump if qtr<=tq(1978q4), lags(1/3)
lutstats
Replay results with 99% confidence interval
. var, level(99)
Saved results
var saves the following in e():
Scalars
e(N) number of observations
e(N_gaps) number of gaps in sample
e(k) number of parameters in all equations
e(k_eq) number of equations in e(b)
e(k_dv) number of dependent variables
e(df_eq) average number of parameters in an equation
e(df_m) model degrees of freedom
e(df_r) residual degrees of freedom (small only)
e(ll) log likelihood
e(ll_dfk) dfk adjusted log likelihood (dfk only)
e(obs_#) number of observations on equation #
e(k_#) number of parameters in equation #
e(df_m#) model degrees of freedom for equation #
e(df_r#) residual degrees of freedom for equation # (small
only)
e(r2_#) R-squared for equation #
e(ll_#) log likelihood for equation #
e(chi2_#) chi-squared for equation #
e(F_#) F statistic for equation # (small only)
e(rmse_#) root mean squared error for equation #
e(aic) Akaike information criterion
e(hqic) Hannan-Quinn information criterion
e(sbic) Schwarz-Bayesian information criterion
e(fpe) final prediction error
e(mlag) highest lag in VAR
e(tmin) first time period in sample
e(tmax) maximum time
e(detsig) determinant of e(Sigma)
e(detsig_ml) determinant of Sigma_ml hat
e(rank) rank of e(V)
Macros
e(cmd) var
e(cmdline) command as typed
e(depvar) names of dependent variables
e(endog) names of endogenous variables, if specified
e(exog) names of exogenous variables, and their lags, if
specified
e(exogvars) names of exogenous, if specified
e(eqnames) names of equations
e(lags) lags in model
e(exlags) lags of exogenous variables in model, if specified
e(title) title in estimation output
e(constraints) constraints, if specified
e(cnslist_var) list of specified constraints
e(small) small, if specified
e(lutstats) lutstats, if specified
e(timevar) time variable specified in tsset
e(tsfmt) format for the current time variable
e(dfk) dfk, if specified
e(properties) b V
e(predict) program used to implement predict
e(marginsok) predictions allowed by margins
e(marginsnotok) predictions disallowed by margins
Matrices
e(b) coefficient vector
e(Cns) constraints matrix
e(Sigma) Sigma hat matrix
e(V) variance-covariance matrix of the estimators
e(bf) constrained coefficient vector
e(exlagsm) matrix mapping lags to exogenous variables
e(G) Gamma matrix; see Methods and formulas in [TS] var
Functions
e(sample) marks estimation sample
Also see
Manual: [TS] var
Help: [TS] var postestimation;
[TS] tsset, [TS] dfactor, [TS] dvech, [TS] sspace, [TS] var
svar, [TS] varbasic, [TS] vec; [TS] var intro
