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1、TheJournalofRealEstateFinanceandEconomics,30:1,33–53,2005#2005SpringerSciences+BusinessMedia,Inc.ManufacturedinTheNetherlands.TheUSHousingMarket:AssetPricingForecastsUsingTimeVaryingCoefficientsHANYS.GUIRGUISManhattanCollege,Riverdale,NY,USAE-mail:hany.guirguis@manhattan.eduCHRISTOSI.GIANNIKOSCol
2、umbiaBusinessSchool,ColumbiaUniversityandBaruchCollege,TheCityUniversityofNewYork,NY,USARANDYI.ANDERSONCollegeofBusinessAdministration,FloridaInternationalUniversity,Miami,FL,USAAbstractTheUShousingmarkethasexperiencedsignificantcyclicalvolatilityoverthelasttwenty-fiveyearsduetomajorstructuralcha
3、ngesandeconomicfluctuations.Inaddition,thehousingmarketisgenerallyconsideredtobeweakforminefficient.Housesarerelativelyilliquid,exceptionallyheterogeneous,andareassociatedwithlargetransactionscosts.Assuch,pastresearchhasshownthatitispossibletopredict,atleastpartially,thetimepathofhousingprices.Th
4、eabilitytopredicthousingpricesisimportantsuchthatinvestorscanmakebetterassetallocationdecisions,includingthepricingandunderwritingofmortgages.MostofthepriorstudiesexaminingtheUShousingmarkethaveemployedconstantcoefficientapproachestoforecasthousepricemovements.However,thisapproachisnotoptimalasan
5、examinationofdatarevealssubstantialsub-sampleparameterinstability.Toaccountfortheparameterinstability,weemployalternativeestimationmethodologieswheretheestimatedparametersareallowedtovaryovertime.TheresultsprovidestrongempiricalevidenceinfavorofutilizingtherollingGeneralizedAutoregressiveConditio
6、nalHeteroskedastic(GARCH)ModelandtheKalmanFilterwithanAutoregressivePresentation(KAR)fortheparameters’timevariation.Lastly,weprovideout-of-sampleforecastsanddemonstratetheprecisionofourapproach.KeyWords:houseprices,Kalmanfilter,rollingGARCH,rollingVECM1.Introduction1.1.OverviewBothacademicsandpra
7、ctitionersareinterestedinunderstandingthedynamicsofthehousingmarketduetoitssignificantimpactonthewholeeconomy.Infact,thehousingsectorconstitutesasignificantshareoftheGDPanditisthelargestcomponentofhouseholdwealthintheU
