arch_postestimation.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}
{* 09mar2005}{...}
{cmd:help arch postestimation}{...}
{right:dialog:  {bf:{dialog arch_p:predict}}}
{right:also see:  {helpb arch}{space 3}}
{hline}

{title:Title}

{p2colset 5 33 35 2}{...}
{p2col:{hi:[TS] arch postestimation} {hline 2}}Postestimation tools for arch{p_end}
{p2colreset}{...}


{title:Description}

{pstd}
The following postestimation commands are available for {cmd:arch}:

{synoptset 11}{...}
{synopt:command}description{p_end}
{synoptline}
INCLUDE help post_estat
INCLUDE help post_estimates
INCLUDE help post_lincom
INCLUDE help post_mfx
INCLUDE help post_nlcom
{p2col :{helpb arch postestimation##predict:predict}}predictions, residuals, influence statistics, and other diagnostic measures{p_end}
INCLUDE help post_predictnl
INCLUDE help post_test
INCLUDE help post_testnl
{synoptline}


{marker predict}{...}
{title:Syntax for predict}

{p 8 16 2}
{cmd:predict}
{dtype}
{newvar}
{ifin}
[{cmd:,}
{it:{help arch postestimation##statistic:statistic}}
{it:{help arch postestimation##options:options}}]

{marker statistic}{...}
{synoptset 14 tabbed}{...}
{synopthdr:statistic}
{synoptline}
{syntab:Main}
{synopt:{opt xb}}predicted values for mean equation{hline 2}the differenced
		series; the default{p_end}
{synopt:{opt y}}predicted values for the mean equation in y{hline 2}the undifferenced series{p_end}
{synopt:{opt v:ariance}}predicted values for the conditional
		variance{p_end}
{synopt:{opt h:et}}predicted values of the variance, considering
		only the multiplicative heteroskedasticity{p_end}
{synopt:{opt r:esiduals}}residuals or predicted innovations{p_end}
{synopt:{opt yr:esiduals}}residuals or predicted innovations in
		y{hline 2}the undifferenced series{p_end}
{synoptline}
{p2colreset}{...}
INCLUDE help esample

{marker options}{...}
{synoptset 35 tabbed}{...}
{synopthdr:options}
{synoptline}
{syntab:Options}
{synopt:{opt d:ynamic(time_constant)}}how to handle the lags of y_t{p_end}
{synopt:{cmd:at(}{it:varname_e}|{it:#e} {it:varname_s2}|{it:#s2}{cmd:)}}make static predictions{p_end}
{synopt:{opt t0(time_constant)}}set starting point for the recursions to {it:time_constant}{p_end}
{synopt:{opt str:uctural}}calculate considering the structural component only{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2}
{it:time_constant} is a {it:#} or a time literal, such as
{cmd:d(1jan1995)} or {cmd:q(1995q1)}, etc.; see {help tfcn}.


{title:Options for predict}

{dlgtab:Main}

{phang}
{opt xb}, the default, calculates the predictions from the mean
   equation.  If {opt D.}{depvar} is the dependent variable, these predictions
   are of {opt D.}{it:depvar} and not of {it:depvar} itself.

{phang}
{opt y} specifies that predictions of {depvar} are to be made even
if the model was specified in terms of, say, {opt D.}{it:depvar}.

{phang}
{opt variance} calculates predictions of the conditional variance.

{phang}
{opt het} calculates predictions of the multiplicative heteroskedasticity
   component of variance.

{phang}
{opt residuals} calculates the residuals.  If no other options are
   specified, these are the predicted innovations; i.e., they include any ARMA
   component.  If option {opt structural} is specified, these are the
   residuals from the mean equation, ignoring any ARMA terms; see
   {opt structural} below.  The residuals are always from the estimated
   equation, which may have a differenced dependent variable; if {depvar} is
   differenced, they are not the residuals of the undifferenced {it:depvar}.

{phang}
{opt yresiduals} calculates the residuals in terms of {depvar}, even
   if the model was specified in terms of, say, {opt D.}{it:depvar}.  As with
   {opt residuals}, the {opt yresiduals} are computed from the model,
   including any ARMA component.  If option {opt structural} is specified, any
   ARMA component is ignored and {opt yresiduals} are the residuals from the
   structural equation; see {opt structural} below.

{dlgtab:Options}

{phang}
{opt dynamic(time_constant)} specifies how lags of y_t in the model are to be
   handled.  If {opt dynamic()} is not specified, actual values are used
   everywhere lagged values of y_t appear in the model to produce one-step-ahead
   forecasts.

{pmore}
   {opt dynamic(time_constant)} produces dynamic (also known as recursive)
   forecasts.  {it:time_constant} specifies when the forecast is to switch
   from one-step ahead to dynamic.  In dynamic forecasts, references to y_t
   evaluate to the prediction of y_t for all periods at or after
   {it:time_constant}; they evaluate to the actual value of y_t for all prior
   periods.

{pmore}
   {cmd:dynamic(10)}, for example, would calculate predictions where any
   reference to y_t with t < 10 evaluates to the actual value of y_t and any
   reference to y_t with t {ul:>} 10 evaluates to the prediction of y_t.  This
   means that one-step-ahead predictions would be calculated for t < 10 and
   dynamic predictions would be calculated thereafter.  Depending on the lag
   structure of the model, the dynamic predictions might still reference some
   actual values of y_t.

{pmore}
   In addition, you may specify {cmd:dynamic(.)} to have {opt predict}
   automatically switch from one-step to dynamic predictions at p + q, where p
   is the maximum AR lag and q is the maximum MA lag.

{phang}
{opt at(varname_e|#e varname_s2|#s2)}
   makes static predictions.  {opt at()} and {opt dynamic()} may not be
   specified together.

{pmore}
   Specifying {opt at()} allows static evaluation of results for a given set of
   disturbances.  This is useful, for instance, in generating the news response
   function.  {opt at()} specifies two sets of values to be used for e_t and
   s_t^2, the dynamic components in the model.  These specifies values are
   treated as given.  In addition any lagged values of {depvar} in the model
   are obtained from the real values of the dependent variable.  All
   computations are based on actual data and the given values.

{pmore}
   {opt at()} requires that you specify two arguments,  which can either be a
   variable name or a number.  The first argument supplies the values to be
   used for e_t; the second supplies the values to be used for s_t^2.  If
   s_t^2 plays no role in your model, the second argument may be specified as
   '{opt .}' to indicate missing.

{phang}
{opt t0(time_constant)} specifies the starting point for the
   recursions to compute the predicted statistics; disturbances are assumed to
   be 0 for t < {opt t0()}.  The default is to set {opt t0()} to the
   minimum t observed in the estimation sample, meaning that observations
   before that are assumed to have disturbances of 0.

{pmore}
{opt t0()} is irrelevant if {opt structural} is specified because, in that
case, all observations are assumed to have disturbances of 0.

{pmore}
{cmd:t0(5)}, for example, would begin recursions at t = 5.  If you data were
   quarterly, you might instead type {cmd:t0(q(1961q2))} to obtain the same
   result.

{pmore}
   Note that any ARMA component in the mean equation or GARCH term in the
   conditional-variance equation makes {opt arch} recursive and dependent on
   the starting point of the predictions.  This includes one-step-ahead
   predictions.

{phang}
{opt structural} makes the calculation considering the structural component
   only, ignoring any ARMA terms, and producing the steady-state equilibrium
   predictions.


{title:Examples}

{phang}{cmd:. predict sigma2, variance at(et 1)}

{phang}{cmd:. lincom [ARCH]L.arch = 0}


{title:Also see}

{psee}
Manual:  {bf:[TS] arch postestimation}

{psee}
Online:  {helpb arch};{break}
{helpb estimates},
{helpb lincom},
{helpb mfx},
{helpb nlcom},
{helpb predictnl},
{helpb test},
{helpb testnl}
{p_end}