var.hlp

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{smcl}
{* 29mar2005}{...}
{cmd:help var}{right:dialog:  {bf:{dialog var}}{space 15}}
{right:also see:  {help var postestimation}}
{hline}

{title:Title}

{p2colset 5 17 19 2}{...}
{p2col:{bf:[TS] var} {hline 2}}Vector autoregression models{p_end}
{p2colreset}{...}


{title:Syntax}

{p 8 12 2}
{cmd:var}
{depvarlist}
{ifin}
[{cmd:,}
{it:options}]

{synoptset 26 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Model}
{synopt:{opt noc:onstant}}suppress constant term{p_end}
{synopt:{opth la:gs(numlist)}}use lags {it:numlist} in the underlying VAR{p_end}
{synopt:{opth ex:og(varlist)}}use exogenous variables {it:varlist}{p_end}

{syntab:Model 2}
{synopt:{cmdab:const:raints(}{it:{help estimation options##constraints():constraints}}{cmd:)}}apply specified linear constraints{p_end}
{synopt:{opt nolo:g}}suppress SURE iteration log{p_end}
{synopt:{opt it:erate(#)}}set maximum number of iterations for SURE; default is {cmd:iterate(1600)}{p_end}
{synopt:{opt tol:erance(#)}}set convergence tolerance of SURE{p_end}
{synopt:{opt nois:ure}}use one-step SURE{p_end}
{synopt:{opt dfk}}make small-sample degrees-of-freedom adjustment{p_end}
{synopt:{opt sm:all}}calculate and report small-sample t and F statistics{p_end}
{synopt:{opt nobig:f}}do not compute parameter vector for coefficients implicitly set to zero
{p_end}

{syntab:Reporting}
{synopt:{opt l:evel(#)}}set confidence level; default is {cmd:level(95)}{p_end}
{synopt:{opt lut:stats}}report L{c u:}kepohl lag-order selection statistics{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2}
You must {helpb tsset} your data before using {opt var}.{p_end}
{p 4 6 2}{it:depvarlist} and {it:varlist} may contain time-series
operators; see {help tsvarlist}.
{p_end}
{p 4 6 2}
{opt by}, {opt rolling}, {opt statsby}, and {opt xi} may be used with
{opt var}; see {help prefix}.{p_end}
{p 4 6 2}See {help var postestimation} for features available after
estimation.{p_end}


{title:Description}

{pstd}
{opt var} fits vector autoregressive (VAR) models.  The lag structure of
the VAR need not be complete, and the model may contain exogenous
variables.  Linear constraints may be placed on any of the coefficients in
the VAR, but {opt var} does not allow constraints on the error
variance-covariance matrix; see {helpb svar} for an estimator that 
allows you to impose structure on the error variance-covariance matrix.


{title:Options}

{dlgtab:Model}

{phang}
{opt noconstant}; see {help estimation options##noconstant:estimation options}.

{phang}
{opth lags(numlist)} specifies the lags to be included in the model.
The default is {cmd:lags(1 2)}.  Note that this option takes a {it:numlist} and
not simply an integer for the maximum lag.  For example, {cmd:lags(2)} would
include only the second lag in the model, whereas {cmd:lags(1/2)} would
include both the first and second lags in the model.  See {it:{help numlist}}
and {bind:{bf:[U] 13.8 Time-series operators}} for a further discussion of numlists
and lags.

{phang}
{opt exog(varlist)} specifies a list of exogenous variables to be
included in the VAR.

{dlgtab:Model 2}

{phang}
{opt constraints(constraints)}; see {help estimation options##constraints():estimation options}.

{phang}
{opt 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 {opt constraints()} option is not specified, the estimates are
obtained via OLS, and {opt nolog} has no effect.  For this reason,
{opt nolog} can only be specified when {opt constraints()} is specified.
Similarly, {opt nolog} cannot be combined with {opt noisure}.

{phang}
{opt 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
{opt constraints()} is not specified, the estimates are obtained using OLS,
and {opt iterate()} has no effect.  For this reason, {opt iterate()} can only
be specified when {opt constraints()} is specified.  Similarly,
{opt iterate()} cannot be combined with {opt noisure}.

{phang}
{opt 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 {cmd:1e-6}.  When the
{opt constraints()} option is not specified, the estimates are obtained using
OLS, and {opt tolerance()} has no effect.  For this reason, {opt tolerance()}
can only be specified when {opt constraints()} is specified.  Similarly,
{opt tolerance()} cannot be combined with {opt noisure}.

{phang}
{opt noisure} specifies that the estimates in the presence of constraints
be obtained through one-step seemingly unrelated regression.  By default,
{opt var} obtains estimates in the presence of constraints through iterated,
seemingly unrelated regression.  When {opt constraints()} is not specified,
the estimates are obtained using OLS, and {opt noisure} has no effect.  For
this reason, {opt noisure} can only be specified when {opt constraints()} is
specified.

{phang}
{opt 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
1/T, where mparms is the average number of parameters in the functional form
for y_t over the K equations.

{phang}
{opt small} causes {opt var} to report small-sample {it:t} and {it:F}
statistics instead of the large-sample normal and chi-squared statistics.

{phang}
{opt nobigf} requests that {opt var} not compute 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.  {cmd:e(bf)}
is used for computing asymptotic standard errors in the postestimation
commands {helpb irf_create:irf create} and {helpb fcast:fcast}.  Therefore,
specifying {opt nobigf} implies that the asymptotic standard errors will not
be available from the {opt irf create} and {opt fcast}
postestimation routines.  See {it:Fitting models with some lags excluded}
in {bf:[TS] var}.

{dlgtab:Reporting}

{phang}
{opt level(#)}; see {help estimation options##level():estimation options}.

{phang}
{opt lutstats} specifies that the L{c u:}tkepohl versions of the lag-order
selection statistics be reported.  See Methods and Formulas in 
{bf:[TS] varsoc} for a discussion of these statistics.


{title:Examples}

{phang}{cmd:. var dlinvestment dlincome dlconsumption}{p_end}

{phang}{cmd:. var dlinv dlinc dlcons if qtr<=q(1978q4)}{p_end}

{phang}{cmd:. var dlinv dlinc dlcons if qtr<=q(1978q4), dfk}{p_end}


{title:Also see}

{psee}
Manual:  {bf:[TS] var}

{psee}
Online:  {help var postestimation};{break}
{helpb arch},
{helpb arima}, 
{helpb reg3}, 
{helpb regress},
{helpb sureg},
{helpb svar}, 
{helpb tsset}, 
{helpb varbasic}, 
{helpb vec}
{p_end}