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1、Chapter8EstimatsInprobabilitytheyweaimtodeducethelikelihoodofdifferentoutcomesbasedonknownprobabilitydistributions.Statisticsistheinverseproblem.Wewanttoinferunknownprobabilitydistributionsfromoutcomesweobserve.Inthischa
2、pterwebegintoexpletheseideas.8.1TheEstimationProblemLet’sstartoffbyclarifyingthenatureoftheproblemwewanttostudy.8.1.1DefinitionsFromapedagogicalpointofviewit’sbesttostartourstudyofstatisticsinasettingwheresuccessiveobser
3、vationsarebothindependentidenticallydistributed.Laterwe’llextendtodependentdata.InanIIDsettingthefundamentalproblemofeconometricsstatisticsisthis:Problem8.1.1WeobserveindependentZvalueddrawsz1...zNfromacommonbutunknowndi
4、stributionP∈PwherePisaclassofdistributionsonZ.WewishtoinfersomefeaturesofPfromthissample.ThesetPistheuniverseofdistributionswearewillingtoconsider.Itcanbeanythingincludingthesetofalldistributionsontheoutcomespace.Oneofth
5、emaintasksofeconomictheyistorestrictPtherebynarrowdownthesetofdistributionswehavetosearchover.Example8.1.1Benhabibetal.(2015)studythewealthdistributioninamodelwithidiosyncraticcapitalincomerisk.Themodelpredictsthatthewea
6、lthdistributionwill213Estimats215?crelationsacrosscodinatesofP?thevariance–covariancematrixassociatedwithP?parameterscontrollingdependencewhensayPismodeledviaacopulaovercertainmarginals.8.1.1.1FeaturesWereferredaboveto“f
7、eatures“ofPthatwemightbeinterestedinestimating.Let’sdefineafeatureofPtobeanobjectofthefmγ(P)fsomeγ:P→S(8.1)ThesetSisleftarbitrarytoaccommodateallpossiblefeatures.WhenPisunderstoodwe’llwriteγ(P)asγ.Herearesomeexamplesfuni
8、variateProutinelyestimatedineconometricstudies:?γ(P)=?skP(ds)thekthmomentofP.?γ(P)=infs∈R:P(?∞s]?12themedianofP.?γ(P)=PwhenwewanttoestimatePitself.?γ(P)=thedensityofPwhenPisabsolutelycontinuous.IfPismultivariateoverz=(xy
9、)thenonefeatureofinterestistheregressionfunctionf?(x):=E[y|x].ThisfunctionisuniquelydeterminedbyP(see5.2.5).8.1.1.2ParametricversusNonparametricClassesInthestatisticalproblemslistedaboveitisassumedthattheunknowndistribut
10、ionbelongstosomeclassP.WecallPaparametricclassifitcanbeexpressedasP=Pθθ∈Θ:=:Pθ:θ∈ΘfsomeΘ?RKInotherwdsaclassofdistributionsisparametricifitcanbeindexedbyfinitelymanyparameters.Aclassofdistributionsiscallednonparametricifi
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