arima.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 arima} {right:dialog:  {bf:{dialog arima}}{space 15}}
{right:also see:  {help arima postestimation}}
{hline}

{title:Title}

{p2colset 5 19 21 2}{...}
{p2col:{hi:[TS] arima} {hline 2}}ARIMA, ARMAX, and other dynamic
regression models {p_end}
{p2colreset}{...}


{title:Syntax}

{pstd}
Basic syntax for a regression model with ARMA disturbances

{p 8 14 2}
{cmd:arima}
{depvar}
[{indepvars}]{cmd:,}
{opth ar(numlist)}
{opth ma(numlist)}

{pstd}
Basic syntax for an ARIMA({it:p},{it:d},{it:q}) model

{p 8 14 2}
{cmd:arima}
{depvar}{cmd:,}
{opt arima(#p,#d,#q)}

{pstd}
Basic syntax for a multiplicative seasonal 
         ARIMA({it:p},{it:d},{it:q})*({it:P},{it:D},{it:Q})s model

{p 8 14 2}
{cmd:arima}
{depvar}{cmd:,}
{opt arima(#p,#d,#q)}
{opt sarima(#P,#D,#Q,#s)}

{pstd}
Full syntax

{p 8 14 2}
{cmd:arima}
{depvar}
[{indepvars}]
{ifin}
{weight}
[{cmd:,} {it:options}]

{synoptset 28 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Model}
{synopt:{opt noc:onstant}}suppress constant term{p_end}
{synopt:{opt arima(#p,#d,#q)}}specify ARIMA({it:p,d,q}) model for dependent variable{p_end}
{synopt:{opth ar(numlist)}}autoregressive terms of the structural model
disturbance{p_end}
{synopt:{opth ma(numlist)}}moving-average terms of the structural model
disturbance{p_end}
{synopt:{cmdab:c:onstraints(}{it:{help estimation options##constraints():constraints}}{cmd:)}}apply specified linear constraints{p_end}

{syntab:Model 2}
{synopt:{opt sarima(#P,#D,#Q,#s)}}specify period-{it:#s} multiplicative seasonal ARIMA term{p_end}
{synopt:{cmd:mar(}{it:{help numlist}}{cmd:,} {it:#s}{cmd:)}}multiplicative seasonal autoregressive terms; may be repeated{p_end}
{synopt:{cmd:mma(}{it:{help numlist}}{cmd:,} {it:#s}{cmd:)}}multiplicative seasonal moving-average terms; may be repeated{p_end}

{syntab:Model 3}
{synopt:{opt cond:ition}}use conditional MLE instead of full MLE{p_end}
{synopt:{opt save:space}}conserve memory during estimation{p_end}
{synopt:{opt dif:fuse}}use diffuse prior for starting Kalman filter recursions{p_end}
{synopt:{cmd:state(}{it:#}|{it:matname}{cmd:)}}use alternate state
vector for starting Kalman filter recursions{p_end}
{synopt:{cmd:p0(}{it:#}|{it:matname}{cmd:)}}use alternate prior for
starting Kalman recursions; seldom used{p_end}

{syntab:SE/Robust}
{synopt:{opth vce(vcetype)}}{it:vcetype} may be {opt opg}, {opt r:obust}, or {opt oim}{p_end}
{synopt:{opt r:obust}}synonym for {cmd:vce(robust)}{p_end}

{syntab:Reporting}
{synopt:{opt l:evel(#)}}set confidence level; default is {cmd:level(95)}{p_end}
{synopt:{opt det:ail}}report list of gaps in time series{p_end}

{syntab:Max options}
{synopt:{it:{help arima##maximize_options:maximize_options}}}control the
maximization process; seldom used{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2}
You must {opt tsset} your data before using {opt arima}; see {helpb tsset}.
{p_end}
{p 4 6 2}
{it:depvar} and {it:indepvars} 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 arima;
see {help prefix}.{p_end}
{p 4 6 2}
{opt iweight}s are allowed; see {help weights}.
{p_end}
{p 4 6 2}
See {help arima postestimation} for features available after estimation.
{p_end}


{title:Description}

{pstd}
{opt arima} fits univariate models with time-dependent disturbances.
{opt arima} fits a model of {depvar} on {indepvars} where the
disturbances are allowed to follow a linear autoregressive moving-average
(ARMA) specification.  The dependent and independent variables may be
differenced or seasonally differenced to any degree.  When independent 
variables are included in the specification, such models are frequently 
called ARMAX models; and when independent variables are not specified, 
they reduce to Box-Jenkins autoregressive integrated moving-average (ARIMA) 
models in the dependent variable.  Multiplicative seasonal ARIMA and 
ARMAX models can also be fitted.  Missing data are allowed and are handled 
using the Kalman filter and methods outlined in {bind:{bf:[TS] arima}}.

{pstd}
In the full syntax, {it:depvar} is the variable being modeled, and
the structural or regression part of the model is specified in {it:indepvars}.
{opt ar()} and {opt ma()} specify the lags of autoregressive and
moving-average terms, respectively; and {opt mar()} and {opt mma()} 
specify the multiplicative seasonal autoregressive and moving-average 
terms, respectively.

{pstd}
{opt arima} allows time-series operators in the dependent variable and
independent variable lists, and it is often convenient to make extensive use
of these operators; see {help dates} for an extended discussion of time-series
operators.

{pstd}
{opt arima} typed without arguments redisplays the previous estimates.


{title:Options}

{dlgtab:Model}

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

{phang}
{opt arima(#p,#d,#q)} is an alternative,
shorthand notation for specifying models with ARMA disturbances.  The 
dependent variable and any independent variables are differenced 
{it:#d} times, 1 through {it:#p} lags of autocorrelations
and 1 through {it:#q} lags of moving averages are included in the model.
For example, the specification

{pin2}
{cmd:. arima D.y, ar(1/2) ma(1/3)}

{pmore}
is equivalent to

{pin2}
{cmd:. arima y, arima(2,1,3)}

{pmore}
The latter is easier to write for simple ARMAX and ARIMA models, but if gaps
in the AR or MA lags are to be modeled, of if different operators are to be
applied to independent variables, the first syntax is required.

{phang}
{opth ar(numlist)} specifies the autoregressive terms of the structural model
   disturbance to be included in the model.  For example, {cmd:ar(1/3)}
   specifies that lags of 1, 2, and 3 of the structural disturbance be
   included in the model; and {cmd:ar(1 4)} specifies that lags 1 and 4 be
   included, perhaps to account for additive quarterly effects.

{pmore}
   If the model does not contain regressors, these terms can also be
   considered autoregressive terms for the dependent variable.

{phang}
{opth ma(numlist)} specifies the moving-average terms to be included in the
   model.  These are the terms for the lagged innovations (white-noise
   disturbances).

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

{pmore}
   If constraints are placed between structural model parameters and ARMA
   terms, the first few iterations may attempt steps into nonstationary areas.
   This can be ignored if the final solution is well within the bounds of
   stationary solutions.

{dlgtab:Model 2}

{phang}
{opt sarima(#P,#D,#Q,#s)} is an alternative, shorthand notation for specifying
   the multiplicative seasonal components of models with ARMA disturbances.
   The dependent variable and any independent variables are lag-{it:#s}
   seasonally differenced {it:#D} times, and 1 through {it:#P} seasonal lags
   of autoregressive terms and 1 through {it:#Q} seasonal lags of
   moving-average terms are included in the model.  For example, the
   specification

{pin2}
{cmd:. arima DS12.y, ar(1/2) mar(1/2,12) mma(1/2,12)}

{pmore}
   is equivalent to

{pin2}