differencedrewrite规则
rewrite规则 时间:2021-01-12 阅读:()
Titlestata.
comarimapostestimation—PostestimationtoolsforarimaDescriptionSyntaxforpredictMenuforpredictOptionsforpredictRemarksandexamplesReferenceAlsoseeDescriptionThefollowingpostestimationcommandsareofspecialinterestafterarima:CommandDescriptionestatacplotestimateautocorrelationsandautocovariancesestatarootscheckstabilityconditionofestimatesirfcreateandanalyzeIRFspsdensityestimatethespectraldensityThefollowingstandardpostestimationcommandsarealsoavailable:CommandDescriptionestaticAkaike'sandSchwarz'sBayesianinformationcriteria(AICandBIC)estatsummarizesummarystatisticsfortheestimationsampleestatvcevariance–covariancematrixoftheestimators(VCE)estimatescatalogingestimationresultsforecastdynamicforecastsandsimulationslincompointestimates,standarderrors,testing,andinferenceforlinearcombinationsofcoefcientslrtestlikelihood-ratiotestmarginsmarginalmeans,predictivemargins,marginaleffects,andaveragemarginaleffectsmarginsplotgraphtheresultsfrommargins(proleplots,interactionplots,etc.
)nlcompointestimates,standarderrors,testing,andinferencefornonlinearcombinationsofcoefcientspredictpredictions,residuals,inuencestatistics,andotherdiagnosticmeasurespredictnlpointestimates,standarderrors,testing,andinferenceforgeneralizedpredictionstestWaldtestsofsimpleandcompositelinearhypothesestestnlWaldtestsofnonlinearhypotheses12arimapostestimation—PostestimationtoolsforarimaSyntaxforpredictpredicttypenewvarifin,statisticoptionsstatisticDescriptionMainxbpredictedvaluesformeanequation—thedifferencedseries;thedefaultstdpstandarderrorofthelinearpredictionypredictedvaluesforthemeanequationiny—theundifferencedseriesmsemeansquarederrorofthepredictedvaluesresidualsresidualsorpredictedinnovationsyresidualsresidualsorpredictedinnovationsiny,reversinganytime-seriesoperatorsThesestatisticsareavailablebothinandoutofsample;typepredict.
.
.
ife(sample).
.
.
ifwantedonlyfortheestimationsample.
PredictionsarenotavailableforconditionalARIMAmodelsttopaneldata.
optionsDescriptionOptionsdynamic(timeconstant)howtohandlethelagsofytt0(timeconstant)setstartingpointfortherecursionstotimeconstantstructuralcalculateconsideringthestructuralcomponentonlytimeconstantisa#oratimeliteral,suchastd(1jan1995)ortq(1995q1);seeConvenientlytypingSIFvaluesin[D]datetime.
MenuforpredictStatistics>Postestimation>Predictions,residuals,etc.
OptionsforpredictFivestatisticscanbecomputedusingpredictafterarima:thepredictionsfromthemodel(thedefaultalsogivenbyxb),thepredictionsafterreversinganytime-seriesoperatorsappliedtothedependentvariable(y),theMSEofxb(mse),thepredictionsofresidualsorinnovations(residual),andthepredictedresidualsorinnovationsintermsofy(yresiduals).
GiventhedynamicnatureoftheARMAcomponentandbecausethedependentvariablemightbedifferenced,thereareotherwaysofcomputingeach.
Wecanuseallthedataonthedependentvariablethatisavailablerightuptothetimeofeachprediction(thedefault,whichisoftencalledaone-stepprediction),orwecanusethedatauptoaparticulartime,afterwhichthepredictedvalueofthedependentvariableisusedrecursivelytomakelaterpredictions(dynamic()).
Eitherway,wecanconsiderorignoretheARMAdisturbancecomponent(thecomponentisconsideredbydefaultandisignoredifyouspecifystructural).
Allcalculationscanbemadeinoroutofsample.
arimapostestimation—Postestimationtoolsforarima3Mainxb,thedefault,calculatesthepredictionsfromthemodel.
IfD.
depvaristhedependentvariable,thesepredictionsareofD.
depvarandnotofdepvaritself.
stdpcalculatesthestandarderrorofthelinearpredictionxb.
stdpdoesnotincludethevariationarisingfromthedisturbanceequation;usemsetocalculatestandarderrorsandcondencebandsaroundthepredictedvalues.
yspeciesthatpredictionsofdepvarbemade,evenifthemodelwasspeciedintermsof,say,D.
depvar.
msecalculatestheMSEofthepredictions.
residualscalculatestheresiduals.
Ifnootheroptionsarespecied,thesearethepredictedinnovationst;thatis,theyincludetheARMAcomponent.
Ifstructuralisspecied,thesearetheresidualstfromthestructuralequation;seestructuralbelow.
yresidualscalculatestheresidualsintermsofdepvar,evenifthemodelwasspeciedintermsof,say,D.
depvar.
Aswithresiduals,theyresidualsarecomputedfromthemodel,includinganyARMAcomponent.
Ifstructuralisspecied,anyARMAcomponentisignored,andyresidualsaretheresidualsfromthestructuralequation;seestructuralbelow.
Optionsdynamic(timeconstant)specieshowlagsofytinthemodelaretobehandled.
Ifdynamic()isnotspecied,actualvaluesareusedeverywherethatlaggedvaluesofytappearinthemodeltoproduceone-step-aheadforecasts.
dynamic(timeconstant)producesdynamic(alsoknownasrecursive)forecasts.
timeconstantspecieswhentheforecastistoswitchfromonestepaheadtodynamic.
Indynamicforecasts,referencestoytevaluatetothepredictionofytforallperiodsatoraftertimeconstant;theyevaluatetotheactualvalueofytforallpriorperiods.
Forexample,dynamic(10)wouldcalculatepredictionsinwhichanyreferencetoytwitht00otherwisemeaningthatpredictnewvar,xbcalculatespredictionsbyusingthemetricofthedependentvariable.
Inthisexample,thedependentvariablerepresentedchangesinln(wpit),andsothepredictionsarelikewiseforchangesinthatvariable.
Ifweinsteaduse.
predicty,yStatacomputesytasyt=xbt+ln(wpit1)sothatytrepresentsthepredictedlevelsofln(wpit).
Ingeneral,predictnewvar,ywillreverseanytime-seriesoperatorsappliedtothedependentvariableduringestimation.
IfwewanttoignoretheARMAerrorcomponentswhenmakingpredictions,weusethestructuraloption,.
predictxbs,xbstructuralwhichgeneratesxbst=β0becausetherearenoregressorsinthismodel,and.
predictys,ystructuralgeneratesyst=β0+ln(wpit1)arimapostestimation—Postestimationtoolsforarima5Example1:DynamicforecastsAnattractivefeatureofthearimacommandistheabilitytomakedynamicforecasts.
Inexample4of[TS]arima,wetthemodelconsumpt=β0+β1m2t+tt=ρt1+θt1+tFirst,weretthemodelbyusingdataupthroughtherstquarterof1978,andthenwewillevaluatetheone-step-aheadanddynamicforecasts.
.
usehttp://www.
stata-press.
com/data/r13/friedman2.
keepiftimechi2=0.
0000OPGDS4.
lnm1Coef.
Std.
Err.
zP>|z|[95%Conf.
Interval]ARMAarL1.
.
3551862.
05030117.
060.
000.
2565979.
4537745L4.
-.
3275808.
0594953-5.
510.
000-.
4441895-.
210972/sigma.
0112678.
000488223.
080.
000.
0103109.
0122246Note:Thetestofthevarianceagainstzeroisonesided,andthetwo-sidedconfidenceintervalistruncatedatzero.
.
irfcreatenonseasonal,set(myirf)step(30)(filemyirf.
irfcreated)(filemyirf.
irfnowactive)(filemyirf.
irfupdated)WetthefollowingseasonalARIMAmodel(1ρ1L)(1ρ4,1L4)4lnm1t=tThecodebelowtsthisnonseasonalARIMAmodelandsavesasetofIRFresultstotheactiveIRFle,whichismyirf.
irf.
.
arimaDS4.
lnm1,ar(1)mar(1,4)noconstantnologARIMAregressionSample:1961q2-2008q2Numberofobs=189Waldchi2(2)=119.
78Loglikelihood=588.
6689Prob>chi2=0.
0000OPGDS4.
lnm1Coef.
Std.
Err.
zP>|z|[95%Conf.
Interval]ARMAarL1.
.
489277.
05380339.
090.
000.
3838245.
5947296ARMA4arL1.
-.
4688653.
0601248-7.
800.
000-.
5867076-.
3510229/sigma.
0107075.
000474722.
560.
000.
0097771.
0116379Note:Thetestofthevarianceagainstzeroisonesided,andthetwo-sidedconfidenceintervalistruncatedatzero.
.
irfcreateseasonal,step(30)(filemyirf.
irfupdated)8arimapostestimation—PostestimationtoolsforarimaWenowhavetwosetsofIRFresultsinthelemyirf.
irf.
WecangraphbothIRFfunctionssidebysidebycallingirfgraph.
.
irfgraphirfThetrajectoriesoftheIRFfunctionsaresimilar:eachgureshowsthatashocktolnm1causesatemporaryoscillationinlnm1thatdiesoutafterabout15timeperiods.
Thisbehaviorischaracteristicofshort-memoryprocesses.
See[TS]psdensityforanintroductiontoestimatingspectraldensitiesusingtheparametersestimatedbyarima.
ReferenceEnders,W.
2004.
AppliedEconometricTimeSeries.
2nded.
NewYork:Wiley.
Alsosee[TS]arima—ARIMA,ARMAX,andotherdynamicregressionmodels[TS]estatacplot—Plotparametricautocorrelationandautocovariancefunctions[TS]estataroots—CheckthestabilityconditionofARIMAestimates[TS]irf—CreateandanalyzeIRFs,dynamic-multiplierfunctions,andFEVDs[TS]psdensity—Parametricspectraldensityestimationafterarima,arma,anducm[U]20Estimationandpostestimationcommands
comarimapostestimation—PostestimationtoolsforarimaDescriptionSyntaxforpredictMenuforpredictOptionsforpredictRemarksandexamplesReferenceAlsoseeDescriptionThefollowingpostestimationcommandsareofspecialinterestafterarima:CommandDescriptionestatacplotestimateautocorrelationsandautocovariancesestatarootscheckstabilityconditionofestimatesirfcreateandanalyzeIRFspsdensityestimatethespectraldensityThefollowingstandardpostestimationcommandsarealsoavailable:CommandDescriptionestaticAkaike'sandSchwarz'sBayesianinformationcriteria(AICandBIC)estatsummarizesummarystatisticsfortheestimationsampleestatvcevariance–covariancematrixoftheestimators(VCE)estimatescatalogingestimationresultsforecastdynamicforecastsandsimulationslincompointestimates,standarderrors,testing,andinferenceforlinearcombinationsofcoefcientslrtestlikelihood-ratiotestmarginsmarginalmeans,predictivemargins,marginaleffects,andaveragemarginaleffectsmarginsplotgraphtheresultsfrommargins(proleplots,interactionplots,etc.
)nlcompointestimates,standarderrors,testing,andinferencefornonlinearcombinationsofcoefcientspredictpredictions,residuals,inuencestatistics,andotherdiagnosticmeasurespredictnlpointestimates,standarderrors,testing,andinferenceforgeneralizedpredictionstestWaldtestsofsimpleandcompositelinearhypothesestestnlWaldtestsofnonlinearhypotheses12arimapostestimation—PostestimationtoolsforarimaSyntaxforpredictpredicttypenewvarifin,statisticoptionsstatisticDescriptionMainxbpredictedvaluesformeanequation—thedifferencedseries;thedefaultstdpstandarderrorofthelinearpredictionypredictedvaluesforthemeanequationiny—theundifferencedseriesmsemeansquarederrorofthepredictedvaluesresidualsresidualsorpredictedinnovationsyresidualsresidualsorpredictedinnovationsiny,reversinganytime-seriesoperatorsThesestatisticsareavailablebothinandoutofsample;typepredict.
.
.
ife(sample).
.
.
ifwantedonlyfortheestimationsample.
PredictionsarenotavailableforconditionalARIMAmodelsttopaneldata.
optionsDescriptionOptionsdynamic(timeconstant)howtohandlethelagsofytt0(timeconstant)setstartingpointfortherecursionstotimeconstantstructuralcalculateconsideringthestructuralcomponentonlytimeconstantisa#oratimeliteral,suchastd(1jan1995)ortq(1995q1);seeConvenientlytypingSIFvaluesin[D]datetime.
MenuforpredictStatistics>Postestimation>Predictions,residuals,etc.
OptionsforpredictFivestatisticscanbecomputedusingpredictafterarima:thepredictionsfromthemodel(thedefaultalsogivenbyxb),thepredictionsafterreversinganytime-seriesoperatorsappliedtothedependentvariable(y),theMSEofxb(mse),thepredictionsofresidualsorinnovations(residual),andthepredictedresidualsorinnovationsintermsofy(yresiduals).
GiventhedynamicnatureoftheARMAcomponentandbecausethedependentvariablemightbedifferenced,thereareotherwaysofcomputingeach.
Wecanuseallthedataonthedependentvariablethatisavailablerightuptothetimeofeachprediction(thedefault,whichisoftencalledaone-stepprediction),orwecanusethedatauptoaparticulartime,afterwhichthepredictedvalueofthedependentvariableisusedrecursivelytomakelaterpredictions(dynamic()).
Eitherway,wecanconsiderorignoretheARMAdisturbancecomponent(thecomponentisconsideredbydefaultandisignoredifyouspecifystructural).
Allcalculationscanbemadeinoroutofsample.
arimapostestimation—Postestimationtoolsforarima3Mainxb,thedefault,calculatesthepredictionsfromthemodel.
IfD.
depvaristhedependentvariable,thesepredictionsareofD.
depvarandnotofdepvaritself.
stdpcalculatesthestandarderrorofthelinearpredictionxb.
stdpdoesnotincludethevariationarisingfromthedisturbanceequation;usemsetocalculatestandarderrorsandcondencebandsaroundthepredictedvalues.
yspeciesthatpredictionsofdepvarbemade,evenifthemodelwasspeciedintermsof,say,D.
depvar.
msecalculatestheMSEofthepredictions.
residualscalculatestheresiduals.
Ifnootheroptionsarespecied,thesearethepredictedinnovationst;thatis,theyincludetheARMAcomponent.
Ifstructuralisspecied,thesearetheresidualstfromthestructuralequation;seestructuralbelow.
yresidualscalculatestheresidualsintermsofdepvar,evenifthemodelwasspeciedintermsof,say,D.
depvar.
Aswithresiduals,theyresidualsarecomputedfromthemodel,includinganyARMAcomponent.
Ifstructuralisspecied,anyARMAcomponentisignored,andyresidualsaretheresidualsfromthestructuralequation;seestructuralbelow.
Optionsdynamic(timeconstant)specieshowlagsofytinthemodelaretobehandled.
Ifdynamic()isnotspecied,actualvaluesareusedeverywherethatlaggedvaluesofytappearinthemodeltoproduceone-step-aheadforecasts.
dynamic(timeconstant)producesdynamic(alsoknownasrecursive)forecasts.
timeconstantspecieswhentheforecastistoswitchfromonestepaheadtodynamic.
Indynamicforecasts,referencestoytevaluatetothepredictionofytforallperiodsatoraftertimeconstant;theyevaluatetotheactualvalueofytforallpriorperiods.
Forexample,dynamic(10)wouldcalculatepredictionsinwhichanyreferencetoytwitht00otherwisemeaningthatpredictnewvar,xbcalculatespredictionsbyusingthemetricofthedependentvariable.
Inthisexample,thedependentvariablerepresentedchangesinln(wpit),andsothepredictionsarelikewiseforchangesinthatvariable.
Ifweinsteaduse.
predicty,yStatacomputesytasyt=xbt+ln(wpit1)sothatytrepresentsthepredictedlevelsofln(wpit).
Ingeneral,predictnewvar,ywillreverseanytime-seriesoperatorsappliedtothedependentvariableduringestimation.
IfwewanttoignoretheARMAerrorcomponentswhenmakingpredictions,weusethestructuraloption,.
predictxbs,xbstructuralwhichgeneratesxbst=β0becausetherearenoregressorsinthismodel,and.
predictys,ystructuralgeneratesyst=β0+ln(wpit1)arimapostestimation—Postestimationtoolsforarima5Example1:DynamicforecastsAnattractivefeatureofthearimacommandistheabilitytomakedynamicforecasts.
Inexample4of[TS]arima,wetthemodelconsumpt=β0+β1m2t+tt=ρt1+θt1+tFirst,weretthemodelbyusingdataupthroughtherstquarterof1978,andthenwewillevaluatetheone-step-aheadanddynamicforecasts.
.
usehttp://www.
stata-press.
com/data/r13/friedman2.
keepiftimechi2=0.
0000OPGDS4.
lnm1Coef.
Std.
Err.
zP>|z|[95%Conf.
Interval]ARMAarL1.
.
3551862.
05030117.
060.
000.
2565979.
4537745L4.
-.
3275808.
0594953-5.
510.
000-.
4441895-.
210972/sigma.
0112678.
000488223.
080.
000.
0103109.
0122246Note:Thetestofthevarianceagainstzeroisonesided,andthetwo-sidedconfidenceintervalistruncatedatzero.
.
irfcreatenonseasonal,set(myirf)step(30)(filemyirf.
irfcreated)(filemyirf.
irfnowactive)(filemyirf.
irfupdated)WetthefollowingseasonalARIMAmodel(1ρ1L)(1ρ4,1L4)4lnm1t=tThecodebelowtsthisnonseasonalARIMAmodelandsavesasetofIRFresultstotheactiveIRFle,whichismyirf.
irf.
.
arimaDS4.
lnm1,ar(1)mar(1,4)noconstantnologARIMAregressionSample:1961q2-2008q2Numberofobs=189Waldchi2(2)=119.
78Loglikelihood=588.
6689Prob>chi2=0.
0000OPGDS4.
lnm1Coef.
Std.
Err.
zP>|z|[95%Conf.
Interval]ARMAarL1.
.
489277.
05380339.
090.
000.
3838245.
5947296ARMA4arL1.
-.
4688653.
0601248-7.
800.
000-.
5867076-.
3510229/sigma.
0107075.
000474722.
560.
000.
0097771.
0116379Note:Thetestofthevarianceagainstzeroisonesided,andthetwo-sidedconfidenceintervalistruncatedatzero.
.
irfcreateseasonal,step(30)(filemyirf.
irfupdated)8arimapostestimation—PostestimationtoolsforarimaWenowhavetwosetsofIRFresultsinthelemyirf.
irf.
WecangraphbothIRFfunctionssidebysidebycallingirfgraph.
.
irfgraphirfThetrajectoriesoftheIRFfunctionsaresimilar:eachgureshowsthatashocktolnm1causesatemporaryoscillationinlnm1thatdiesoutafterabout15timeperiods.
Thisbehaviorischaracteristicofshort-memoryprocesses.
See[TS]psdensityforanintroductiontoestimatingspectraldensitiesusingtheparametersestimatedbyarima.
ReferenceEnders,W.
2004.
AppliedEconometricTimeSeries.
2nded.
NewYork:Wiley.
Alsosee[TS]arima—ARIMA,ARMAX,andotherdynamicregressionmodels[TS]estatacplot—Plotparametricautocorrelationandautocovariancefunctions[TS]estataroots—CheckthestabilityconditionofARIMAestimates[TS]irf—CreateandanalyzeIRFs,dynamic-multiplierfunctions,andFEVDs[TS]psdensity—Parametricspectraldensityestimationafterarima,arma,anducm[U]20Estimationandpostestimationcommands
- differencedrewrite规则相关文档
- seinrewrite规则
- Akrewrite规则
- Application-centric
- 银行rewrite规则
- Dueckrewrite规则
- Pizirewrite规则
限时新网有提供5+个免费域名
有在六月份的时候也有分享过新网域名注册商发布的域名促销活动（这里）。这不在九月份发布秋季域名促销活动，有提供年付16元的.COM域名，同时还有5个+的特殊后缀的域名是免费的。对于新网服务商是曾经非常老牌的域名注册商，早年也是有在他们家注册域名的。我们可以看到，如果有针对新用户的可以领到16元的.COM域名。包括还有首年免费的.XYZ、.SHOP、Space等等后缀的域名。除了.COM域名之外的其他...
Raksmart VPS主机如何设置取消自动续费
今天有看到Raksmart账户中有一台VPS主机即将到期，这台机器之前是用来测试评测使用的。这里有不打算续费，这不面对万一导致被自动续费忘记，所以我还是取消自动续费设置。如果我们也有类似的问题，这里就演示截图设置Raksmart取消自动续费。这里我们可以看到上图，在对应VPS主机的【其余操作】中可以看到默认已经是不自动续费，所以我们也不要担心被自动续费的。当然，如果有被自动续费，我们确实不想续费的...
老周互联24小时无理由退款，香港原生IP，28元起
老周互联怎么样？老周互联隶属于老周网络科技部旗下，创立于2019年12月份，是一家具有代表性的国人商家。目前主营的产品有云服务器，裸金属服务器。创办一年多以来，我们一直坚持以口碑至上，服务宗旨为理念，为用户提供7*24小时的轮班服务，目前已有上千多家中小型站长选择我们!服务宗旨：老周互联提供7*24小时轮流值班客服，用户24小时内咨询问题可提交工单，我们会在30分钟内为您快速解答!另免费部署服务器...
rewrite规则为你推荐
-
免费云主机免费云主机哪家好？已备案域名查询如何查询网站的域名是否已经备案免费虚拟主机申请谁有1年免费的虚拟主机申请地址吖？中国域名注册中国十大域名注册服务商有哪些?免费网站域名申请哪里可以申请到免费网站域名？域名购买在网上购买域名 会受骗吗虚拟空间免费试用目前哪里有免费试用的虚拟主机 或者服务器用啊?asp虚拟空间怎样在一个虚拟空间里放上一个ASP和一个PHP的网站免费网站空间申请哪里有永久免费的域名空间可以申请北京网站空间求永久免费的网站服务器！