svar.hlp

是一个经济学管理应用软件 很难找的 但是经济学学生又必须用到

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

{title:Title}

{p2colset 5 22 24 2}{...}
{p2col:{hi:[TS] var svar} {hline 2}}Structural vector autoregression
models{p_end}
{p2colreset}{...}


{title:Syntax}

{pstd}
Short-run constraints

{p 8 13 2}
{cmd:svar}
{depvarlist}
{ifin}
{cmd:,}
{c -(}
{opt acon:straints(constraints_a)}
{opt ae:q(matrix_aeq)}
{opt ac:ns(matrix_acns)}
{opt bcon:straints(constraints_b)}
{opt be:q(matrix_beq)}
{opt bc:ns(matrix_bcns)}
{c )-}
[{it:{help svar##short_run_options:short_run_options}}]{p_end}

{pstd}
Long-run constraints

{p 8 13 2 }
{cmd:svar}
{depvarlist}
{ifin}
{cmd:,}
{c -(}
{opt lrcon:straints(constraints_lr)}
{opt lre:q(matrix_lreq)}
{opt lrc:ns(matrix_lrcns)}
{c )-}
[{it:{help svar##long_run_options:long_run_options}}]{p_end}

{synoptset 33 tabbed}{...}
{marker short_run_options}{...}
{synopthdr:short_run_options}
{synoptline}
{syntab:Model}
{synopt:{opt nocons:tant}}suppress constant term{p_end}
{p2coldent:* {opt acon:straints(constraints_a)}}apply previously defined {it:constraints_a} to {bf:A}{p_end}
{p2coldent:* {opt ae:q(matrix_aeq)}}define and apply to {bf:A} equality constraint matrix {it:matrix_aeq}{p_end}
{p2coldent:* {opt ac:ns(matrix_acns)}}define and apply to {bf:A} cross-parameter constraint matrix {it:matrix_acns}{p_end}
{p2coldent:* {opt bcon:straints(constraints_b)}}apply previously defined {it:constraints_b} to {bf:B}{p_end}
{p2coldent:* {opt be:q(matrix_beq)}}define and apply {bf:B} equality constraint matrix {it:matrix_beq}{p_end}
{p2coldent:* {opt bc:ns(matrix_bcns)}}define and apply to {bf:B} cross-parameter constraint {it:matrix_bcns}{p_end}
{synopt:{opth la:gs(numlist)}}use lags {it:numlist} in the VAR{p_end}

{syntab:Model 2}
{synopt:{opth ex:og(varlist:varlist_exog)}}use exogenous variables {it:varlist}{p_end}
{synopt:{opt varc:onstraints(constraints_v)}}apply {it:contstraints_v} to underlying VAR{p_end}
{synopt:{opt noislog}}suppress SURE iteration log{p_end}
{synopt:{opt isit:erate(#)}}set maximum number of iterations for SURE; default is {cmd:isit(1600)}{p_end}
{synopt:{opt istol: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 noiden:check}}do not check for local identification{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 f:ull}}show constrained parameters in table{p_end}
{synopt:{opt var}}display underlying {opt var} output{p_end}
{synopt:{opt lut:stats}}report L{c u:}kepohl lag-order selection statistics{p_end}

{syntab:Max options}
{synopt:{it:{help svar##maximize_options:maximize_options}}}control the maximization process;
seldom used{p_end}
{synoptline}
{p 4 6 2}
* {opt aconstraints(constraints_a)}, {opt aeq(matrix_aeq)}, {opt acns(matrix_acns)},
  {opt bconstraints(constraints_b)}, {opt beq(matrix_beq)}, {opt bcns(matrix_bcns)}:
  at least one of these must be specified.{p_end}

{marker long_run_options}{...}
{synopthdr:long_run_options}
{synoptline}
{syntab:Model}
{synopt:{opt nocons:tant}}suppress constant term{p_end}
{p2coldent:* {opt lrcon:straints(constraints_lr)}}apply previously defined {it:constraints_lr} to {bf:C}{p_end}
{p2coldent:* {opt lre:q(matrix_lreq)}}define and apply to {bf:C} equality constraint matrix {it:matrix_lreq}{p_end}
{p2coldent:* {opt lrc:ns(matrix_lrcns)}}define and apply to {bf:C} cross-parameter constraint matrix {it:matrix_lrcns}{p_end}
{synopt:{opth la:gs(numlist:numlist)}}use lags {it:numlist} in the underlying VAR{p_end}

{syntab:Model 2}
{synopt:{opth ex:og(varlist:varlist_exog)}}use exogenous variables {it:varlist}{p_end}
{synopt:{opt varc:onstraints(constraints_v)}}apply {it:constraints_v}
to underlying VAR {p_end}
{synopt:{opt noislog}}suppress SURE iteration log{p_end}
{synopt:{opt isit:erate(#)}}set maximum number of iterations for SURE; default is {cmd:isit(1600)}{p_end}
{synopt:{opt istol: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 noiden:check}}do not check for local identification{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 f:ull}}show constrained parameters in table{p_end}
{synopt:{opt var}}display underlying {opt var} output{p_end}
{synopt:{opt lut:stats}}report L{c u:}kepohl lag-order selection statistics{p_end}

{syntab:Max options}
{synopt:{it:{help svar##maximize_options:maximize_options}}}control the maximization process;
seldom used{p_end}
{synoptline}
{p 4 6 2}
* {opt lrconstraints(constraints_lr)}, {opt lreq(matrix_lreq)},
  {opt lrcns(matrix_lrcns)}: at least one of these must be specified.{p_end}

{p 4 6 2}You must {helpb tsset} your data before using {opt svar}. {p_end}
{p 4 6 2}The {it:depvarlist} and {it:varlist_exog} may contain time-series
operators; see {help tsvarlist}. {p_end}
{p 4 6 2}{opt by}, {opt rolling}, {opt statsby}, or {opt xi} may be used with {opt svar}; see {help prefix}.{p_end}
{p 4 6 2}See {help svar postestimation} for features available after
estimation.{p_end}


{title:Description}

{pstd}
{opt svar} estimates the parameters of a structural vector autoregression
(SVAR) and the parameters of the underlying vector autoregression (VAR).


{title:Options}

{dlgtab:Model}

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

{pstd}
{opt aconstraints(constraints_a)},
{opt aeq(matrix_aeq)},
{opt acns(matrix_acns)}
{break}
{opt bconstraints(constraints_b)},
{opt beq(matrix_beq)},
{opt bcns(matrix_bcns)}{p_end}
{pmore}
    These options specify the short-run constraints in an SVAR.  To specify a
    short-run SVAR model, you must specify at least one of these options.  The
    first list of options specifies constraints on the parameters of the
    {bf:A} matrix; the second list specifies constraints on the parameters of
    the {bf:B} matrix.
    If at least one option is selected from the first list and none are
    selected from the second list, {opt svar} sets {bf:B} to the identity
    matrix.  Similarly, if at least one option is selected from the second
    list and none are selected from the first list, {opt svar} sets {bf:A} to
    the identity matrix.

{pmore}
    None of these options may be specified with any of the options that define
    long-run constraints.

{phang2}
{opt aconstraints(constraints_a)} specifies a {it:{help numlist}} of
    previously defined Stata constraints to be applied to {bf:A}
    during estimation.

{phang2}
{opt aeq(matrix_aeq)} specifies a matrix that defines a set of
equality constraints.  This matrix must be square with dimension equal to the
number of equations in the underlying VAR.  The elements of this matrix must
be {it:missing} or real numbers.  A missing value in the ({it:i,j})
element of this matrix specifies that the ({it:i,j}) element of {bf:A}
is a free parameter.  A real number in the ({it:i,j}) element of this
matrix constrains the ({it:i,j}) element of {bf:A} to this real number.  For
example,

{center:{space 4}{c TLC}{space 11}{c TRC}}
{center:{bf:A} = {c |} 1     0   {c |}}
{center:{space 4}{c |} .    1.5  {c |}}
{center:{space 4}{c BLC}{space 11}{c BRC}}

{pmore2}
   specifies that {bf:A}[1,1]=1, {bf:A}[1,2]=0, {bf:A}[2,2]=1.5, and
   {bf:A}[2,1] is a free parameter.

{phang2}
{opt acns(matrix_acns)} specifies a matrix that defines a set
   of exclusion or cross-parameter equality constraints on {bf:A}.  This
   matrix must be square with dimension equal to the number of equations in
   the underlying VAR.  Each element of this matrix must be {it:missing}, 0,
   or a positive integer.  A missing value in the ({it:i,j}) element of this
   matrix specifies that no constraint be placed on this element of {bf:A}.  A
   zero in the ({it:i,j}) element of this matrix constrains the ({it:i,j})
   element of {bf:A} to be zero.  Any strictly positive integers must be in
   two or more elements of this matrix.  A strictly positive integer in the
   ({it:i,j}) element of this matrix constrains the ({it:i,j}) element of {bf:A}
   to be equal to all the other elements of {bf:A} that correspond to elements
   in this matrix that contain the same integer.  For example, consider the
   matrix

{center:{space 4}{c TLC}{space 10}{c TRC}}
{center:{bf:A} = {c |} .     1  {c |}}