伍德里奇计量经济学英文版各章总结K12教育文档.docx

伍德里奇计量经济学英文版各章总结K12教育文档.docx

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伍德里奇计量经济学英文版各章总结K12教育文档

伍德里奇计量经济学英文版各章总结（word版可编辑修改）

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CHAPTER1

TEACHINGNOTES

YouhavesubstantiallatitudeaboutwhattoemphasizeinChapter1。

Ifinditusefultotalkabouttheeconomicsofcrimeexample（Example1.1）andthewageexample（Example1.2）sothatstudentssee，attheoutset，thateconometricsislinkedtoeconomicreasoning,eveniftheeconomicsisnotcomplicatedtheory.

Iliketofamiliarizestudentswiththeimportantdatastructuresthatempiricaleconomistsuse,focusingprimarilyoncross—sectionalandtimeseriesdatasets,asthesearewhatIcoverinafirst—semestercourse.Itisprobablyagoodideatomentionthegrowingimportanceofdatasetsthathavebothacross—sectionalandtimedimension。

Ispendalmostanentirelecturetalkingabouttheproblemsinherentindrawingcausalinferencesinthesocialsciences.Idothismostlythroughtheagriculturalyield,returntoeducation，andcrimeexamples.Theseexamplesalsocontrastexperimentalandnonexperimental（observational）data。

Studentsstudyingbusinessandfinancetendtofindthetermstructureofinterestratesexamplemorerelevant，althoughtheissuethereistestingtheimplicationofasimpletheory,asopposedtoinferringcausality。

Ihavefoundthatspendingtimetalkingabouttheseexamples，inplaceofaformalreviewofprobabilityandstatistics,ismoresuccessful（andmoreenjoyableforthestudentsandme）。

CHAPTER2

TEACHINGNOTES

ThisisthechapterwhereIexpectstudentstofollowmost,ifnotall,ofthealgebraicderivations。

InclassIliketoderiveatleasttheunbiasednessoftheOLSslopecoefficient,andusuallyIderivethevariance。

Ataminimum,Italkaboutthefactorsaffectingthevariance.Tosimplifythenotation，afterIemphasizetheassumptionsinthepopulationmodel，andassumerandomsampling,Ijustconditiononthevaluesoftheexplanatoryvariablesinthesample.Technically,thisisjustifiedbyrandomsamplingbecause,forexample,E（ui｜x1,x2，…,xn）=E（ui｜xi）byindependentsampling。

IfindthatstudentsareabletofocusonthekeyassumptionSLR.4andsubsequentlytakemywordabouthowconditioningontheindependentvariablesinthesampleisharmless.（Ifyouprefer，theappendixtoChapter3doestheconditioningargumentcarefully。

）Becausestatisticalinferenceisnomoredifficultinmultipleregressionthaninsimpleregression,IpostponeinferenceuntilChapter4.（Thisreducesredundancyandallowsyoutofocusontheinterpretivedifferencesbetweensimpleandmultipleregression。

）

Youmightnoticehow，comparedwithmostothertexts，IuserelativelyfewassumptionstoderivetheunbiasednessoftheOLSslopeestimator，followedbytheformulaforitsvariance。

ThisisbecauseIdonotintroduceredundantorunnecessaryassumptions.Forexample，onceSLR.4isassumed，nothingfurtherabouttherelationshipbetweenuandxisneededtoobtaintheunbiasednessofOLSunderrandomsampling.

CHAPTER3

TEACHINGNOTES

Forundergraduates，Idonotworkthroughmostofthederivationsinthischapter，atleastnotindetail。

Rather，Ifocusoninterpretingtheassumptions,whichmostlyconcernthepopulation.Otherthanrandomsampling，theonlyassumptionthatinvolvesmorethanpopulationconsiderationsistheassumptionaboutnoperfectcollinearity，wherethepossibilityofperfectcollinearityinthesample（evenifitdoesnotoccurinthepopulation）shouldbetouchedon.Themoreimportantissueisperfectcollinearityinthepopulation，butthisisfairlyeasytodispensewithviaexamples。

Thesecomefrommyexperienceswiththekindsofmodelspecificationissuesthatbeginnershavetroublewith。

Thecomparisonofsimpleandmultipleregressionestimates–basedontheparticularsampleathand，asopposedtotheirstatisticalproperties –usuallymakesastrongimpression.SometimesIdonotbotherwiththe“partiallingout”interpretationofmultipleregression。

Asfarasstatisticalproperties，noticehowItreattheproblemofincludinganirrelevantvariable：

noseparatederivationisneeded，astheresultfollowsformTheorem3.1。

Idoliketoderivetheomittedvariablebiasinthesimplecase.ThisisnotmuchmoredifficultthanshowingunbiasednessofOLSinthesimpleregressioncaseunderthefirstfourGauss-Markovassumptions。

Itisimportanttogetthestudentsthinkingaboutthisproblemearlyon,andbeforetoomanyadditional（unnecessary）assumptionshavebeenintroduced.

Ihaveintentionallykeptthediscussionofmulticollinearitytoaminimum.Thispartlyindicatesmybias，butitalsoreflectsreality。

Itis,ofcourse,veryimportantforstudentstounderstandthepotentialconsequencesofhavinghighlycorrelatedindependentvariables。

Butthisisoftenbeyondourcontrol,exceptthatwecanasklessofourmultipleregressionanalysis。

Iftwoormoreexplanatoryvariablesarehighlycorrelatedinthesample,weshouldnotexpecttopreciselyestimatetheirceterisparibuseffectsinthepopulation.

Ifindextensivetreatmentsofmulticollinearity，whereone“tests"orsomehow“solves"themulticollinearityproblem,tobemisleading，atbest。

EventheorganizationofsometextsgivestheimpressionthatimperfectmulticollinearityissomehowaviolationoftheGauss—Markovassumptions:

theyincludemulticollinearityinachapterorpartofthebookdevotedto“violationofthebasicassumptions，”orsomethinglikethat。

Ihavenoticedthatmaster'sstudentswhohavehadsomeundergraduateeconometricsareoftenconfusedonthemulticollinearityissue.Itisveryimportantthatstudentsnotconfusemulticollinearityamongtheincludedexplanatoryvariablesinaregressionmodelwiththebiascausedbyomittinganimportantvariable.

IdonotprovetheGauss—Markovtheorem.Instead,Iemphasizeitsimplications。

Sometimes，andcertainlyforadvancedbeginners,IputaspecialcaseofProblem3。

12onamidtermexam，whereImakeaparticularchoiceforthefunctiong（x）.Ratherthanhavethestudentsdirectlycomparethevariances，theyshouldappealtotheGauss-MarkovtheoremforthesuperiorityofOLSoveranyotherlinear，unbiasedestimator。

CHAPTER4

TEACHINGNOTES

AtthestartofthischapterisgoodtimetoremindstudentsthataspecificerrordistributionplayednoroleintheresultsofChapter3.ThatisbecauseonlythefirsttwomomentswerederivedunderthefullsetofGauss-Markovassumptions。

Nevertheless，normalityisneededtoobtainexactnormalsamplingdistributions（conditionalontheexplanatoryvariables）.IemphasizethatthefullsetofCLMassumptionsareusedinthischapter,butthatinChapter5werelaxthenormalityassumptionandstillperformapproximatelyvalidinference.Onecouldarguethattheclassicallinearmodelresultscouldbeskippedentirely，andthatonlylarge—sampleanalysisisneeded。

But,fromapracticalperspective,studentsstillneedtoknowwherethetdistributioncomesfrombecausevirtuallyallregressionpackagesreporttstatisticsandobtainp—valuesoffofthetdistribution。

IthenfinditveryeasytocoverChapter5quickly，byjustsayingwecandropnormalityandstillusetstatisticsandtheassociatedp—valuesasbeingapproximatelyvalid。

Besides，occasionallystudentswillhavetoanalyzesmallerdatasets,especiallyiftheydotheirownsmallsurveysforatermproject.

Itiscrucialtoemphasizethatwetesthypothesesaboutunknownpopulationparameters。

ItellmystudentsthattheywillbepunishediftheywritesomethinglikeH0：

 =0onanexamor,evenworse，H0:

。

632=0。

OneusefulfeatureofChapter4isitsillustrationofhowtorewriteapopulationmodelsothatitcontainstheparameterofinterestintestingasinglerestriction。

Ifindthisiseasier,boththeoreticallyandpractically,thancomputingvariancesthatcan,insomecases,dependonnumerouscovarianceterms.Theexampleoftestingequalityofthereturntotwo-andfour—yearcollegesillustratesthebasicmethod，andshowsthattherespecifiedmodelcanhaveausefulinterpretation.Ofcourse,somestatisticalpackagesnowprovideastandarderrorforlinearcombinationsofestimateswithasimplecommand，andthatshouldbetaught，too.

OnecanuseanFtestforsinglelinearrestrictionsonmultipleparameters，butthisislesstransparentthanattestanddoesnotimmediatelyproducethestandarderrorneededforaconfidenceintervalorfortestingaone-sidedalternative。

Thetrickofrewritingthepopulationmodelisusefulinseveralinstances，includingobtainingconfidenceintervalsforpredictionsinChapter6,aswellasforobtainingconfidenceintervalsformarginaleffectsinmodelswithinteractions（alsoinChapter6）。

Themajorleaguebaseballplayersalaryexampleillustratesthedifferencebetweenindividualandjointsignificancewhenexplanatoryvariables（rbisyrandhrunsyrinthiscase）arehighlycorrelated。

ItendtoemphasizetheR-squaredformoftheFstatisticbecause,inpractice,itisapplicablealargepercentageofthetime,anditismuchmorereadilycomputed。

Idoregretthatthisexampleisbiasedtowardstudentsincountrieswherebaseballisplayed.Still，itisoneofthebetterexamplesofmulticollinearitythatIhavecomeacross，andstudentsofallbackgroundsseemtogetthepoint。

CHAPTER5

TEACHINGNOTES

Chapter5isshort,butitisconceptuallymoredifficultthantheearlierchapters,primarilybecauseitrequiressomeknowledgeofasymptoticpropertiesofestimators.Inclass，Igiveabrief，heuristicdescriptionofconsistencyandasym