RegrdiagWord文档下载推荐.docx

RegrdiagWord文档下载推荐.docx

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1.DoestherelationbetweenYandtheX’sfollowalinearpattern?

2.Aretheresidualsapproximatelynormallydistributed?

3.Aretheresidualsreasonablyhomoscedastic?

4.Aretheresidualsautocorrelated?

5.Doalldatapointscontributeroughlyequallytodeterminethepositionofthelineordosomepointsexertundueinfluenceontheoutcome?

6.Arethereanyoutlyingdatapointswhichclearlydonotfitthegeneralpattern?

Anumberofplotscanbepreferred,butithastoberememberedthattheyarenotalwayssufficientlypowerfulandtheycanbemisleading.Suchplotsareasfollows:

1.Scatterplotofyvxi

Usewithcare,butmaysuggestnon-linearity.

2.Residuals/StandardisedResidualsvxi

Thepresenceofacurvilinearrelationshipsuggeststhatahigher-orderterm,e.g.quadratic,shouldbeaddedtothemodel,oratransformation,suchasalog,shouldbeconsidered.Canindicatetheexistenceofoutliers,structuralbreaksandnon-constantvarianceoftheerrorterm,i.e.heteroscedasticity

3.Residualsvexplanatoryvariablesnotinthemodel.

Thepresenceofarelationshipwouldsuggestthattheexplanatoryvariableshouldbeincludedinthemodel.

4.Residualsvy

Ifthevarianceoftheresidualschangeswiththepredictedvalues,thenheteroscedasticityisindicated.Outliers,non-linearityandstructuralbreaksmayalsobeindicated.

5.Residualsvtime

Inthecaseoftimeseriesdata,correlationbetweentheerrortermscanbedetectedsuggestingthepresenceofautocorrelation.Thismayindicatemissingvariable（s）inthemodel.

6.Variablesvtime

Aproblemassociatedwithnon-stationaryvariables,andfrequentlyfacedbyeconometricianswhendealingwithtimeseriesdata,isthespuriousregressionproblem.Ifatleastoneoftheexplanatoryvariablesinaregressionequationisnon-stationaryinthesensethatitdisplaysadistinctstochastictrend,itisverylikelythecasethatthedependentvariableintheequationwilldisplayasimilartrend.Ifsuchaproblemisdetected,thenerrorcorrectionmodels（ECM）andcointegrationanalysiswillhavetobeconsidered.

7.Normalplotoftheresiduals

Theuseofnormalityplotscanhelpdetectabnormalitieswiththedataandthemodel.Ifthemodeliscorrectlyspecified,thentheresidualsshouldlooklikeasamplefromanormaldistribution.

Note:

Anysystematicpatternintheresidualsofaregressionequationshouldberegardedassuggestiveofthepossibilityofmisspecification.

NormalityTests

Recallthatoneoftheassumptionsintheclassicalregressionmodelisthattheerrorshadtobenormallydistributedabouttheirzeromean.Theassumptionisnecessaryiftheinferentialaspectsofclassicalregression（ttest,Ftestsetc.）aretobevalidinsmallsamples.

Thereareseveraltestsofnormalitythatcanbeused.

1.HistogramofResiduals

Asimplegraphicaldevice,butrathersubjective.

2.NormalProbabilityPlot

Arathercomparativelysimplegraphicaldevice.MINITABwillproducenormalscoresbymeansoftheNSCOREScommand.ThesecanbeplottedagainsttheresidualsandanelongatedS-shapedcurveshouldbeproducediftheresidualsarenormallydistributed.Thereshouldalsobeanextremelyhighcorrelationbetweentheresidualsandthenscores–statisticaltableswillberequiredtocheckthesignificanceoftheresults.

3.NormalProbabilityTestsproducedbyMINITAB

Anderson-Darlingnormalitytest

Ryan-Joinernormalitytest

Kolmogorov-Smirnoffnormalitytest

Thesetestsareconstructedusingdifferentassumptionsaboutthedata（fordetailsseetheHELPfacilitywithinMINITAB）.Theyalltakethenullhypothesistobeoneofnormality.Therefore,normalityoftheresidualswillberejectedifthequotedp-valueissmallerthanthesignificancelevel,.

4.TheJarque-BeraTestforNormality

AtestofnormalitywhichisfoundinanumberofeconometricpackagesintheJarque-Bera（JB）test.ThisisanasymptoticorlargesampletestandisbasedonOLSresiduals.Thetesthingesonthevaluesforskewnessandkurtosiswhichforanormaldistributionare0and3respectively.ThesearemeasuredbythethirdandfourthmomentsoftheOLSresiduals.

Underanullhypothesisofnormallydistributeddisturbances,

wehaveskewness（3）=0andkurtosis（4）=3

Itcanbeshownthat

Z3=3nandZ4=（4-3）n

624

bothhaveastandardnormaldistributioninlargesamples.

Therefore,

（Z32+Z42）willhavea2with2df

Hence,

Ho:

residualsarenormallydistributed

i.e.3=0and4=3

v

H1:

residualsarenotnormallydistributed

i.e.30or43orboth

WerejectH0if

JB=Z32+Z42

=（n/6）.32+（n/24）.（4-3）2

=n[32/6+（4-3）2/24]

>

（2）2at%significancelevel

Wherenisthesamplesize,3isskewnessand4iskurtosis

Now,fromthesampleofresiduals

2=ei2/n,3=ei3/n,4=ei4/n

Itcanbeshownthat

32=32/23and4=4/22

Outliers,LeverageandInfluence

Inregressionanalysis,youshouldalwaysbewareofpointswhichdonotfitthegeneralpatternorexertunderinfluenceontheoutcomeofournumericalsummaries.Therearethreetypesofdatapointswhichshouldconcernus.Theseare:

anoutlier,apointofhighleverage,andaninfluentialpoint.

Anoutlierinaregressionisadatapointwhichhasalargeresidual（usuallymorethanthreestandarddeviationsfromthemean（=0））.

Apointofhighleveragecanbedefinedthus:

‘AdatapointhasahighleverageifitisextremeintheX-direction,i.e.itisadisproportionatedistanceawayfromthemiddlerangeoftheX–values’.ThesepointscanexertundueinfluenceontheoutcomeofanOLSregressionline.Theyarecapableofexertingastrongpullontheslopeoftheregressionline.Whethertheydosoornotisanothermatter.

Aninfluentialpointisapointwhichifremovedfromthesamplewouldmarkedlychangethepositionoftheleastsquaresregressionline.Hence,influentialdatapointspulltheregressionlineintheirdirection.Notethatinfluentialdatapointsdonotnecessarilyproducelargeresiduals,thatis,theyarenotalwaysoutliersaswell,althoughtheycanbe.Itispreciselybecausetheydrawtheregressionlinetowardsthemselvesthattheymayendupwithsmallresiduals.Conversely,anoutlierisnotnecessarilyaninfluentialpoint,particularlywhenitisapointwithlittleleverage.

Ingeneralwenote:

outliersarenotnecessarilyinfluential

buttheycanbeso（dependingonleverage）

yethighleveragepointsarenotalwaysinfluential

andinfluentialpointsarenotnecessarilyoutliers.

Thepresenceofoutliersorofinfluentialpointsoftengivesusaclearsignalthatourmodelisprobablymisspecified.Intermsofvisualdisplays,outlierscanbespottedwithresidualplots,whereasinfluentialpointsreallyneedscatterplotswhicharenotalwayssomeaningfulwhendealingwithseveralexplanatoryvariables.Apartfromthesegraphicalmethods,wecanalsousesomespecialstatisticsdesignedtodetectoutliers,pointsofleverageandpointsofinfluence.

StudentisedResiduals

Agoodwaytodetectoutliersistoinvestigateeachobservationatatime,usinganOLSregressionwiththerelevantobservationexcluded,andtestingwhetherthepredictionerrorforthatobservationissignificantlylarger.Thiscanbemosteasilydonebyincludinganobservation-specificdummyvariable.Forexample,toinvestigatetheithobservationinadataset,wedefineadummyvariabletakingavalueofunityfortheithobservationandzeroforallotherobservations.IfweincludethisdummyintheOLSregression,itscoefficientwillequaltherequiredpredictionerror.Totestthepredictionerrorforsignificance,wecanexamineitstratio.Thistratioisreferredtoasastudentisedresidual.Ithasastudent’stdistributionwith（T-1-K-1）degreesoffreedomwhereKisthenumberofexplanatoryvariables.

Wedefinethestudentisedresiduals（ei*）.

ei*=ei/[s（i）（1-hi）]=sei/s（i）

Wheres（i）isthestandarderrorestimateoftheregressionfittedafterdeletingtheithobservation,andhiisameasureofleverage,andei’isthestandardizedresidual.

UnusualYvalueswillclearlystando