Test Bank For Introductory Econometrics: A Modern Approach 7th Edition By Jeffrey M. Wooldridge

Test Bank For Introductory Econometrics: A Modern Approach 7th Edition By Jeffrey M. Wooldridge. Econometricsisitheibranchiofieconomicsithat . a. studiesitheibehavioriofindividualieconomiciagentsiinimakingieconomicidecisions b. developsiandiusesistatisticalimethodsiforiestimatingieconomicirelationships c. dealsiwithitheiperformance,istructure,ibehavior,iandidecision-makingiofianieconomyiasiaiwhole d. appliesimathematicalimethodsitoirepresentieconomicitheoriesiandisolveieconomiciproblems. Answer:ib iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iWhatis iEconometrics?BUSPROG: i Feedback:iEconometricsisitheibranchiofieconomicsithatidevelopsiandiusesistatisticalimethods iforestimating i ieconomicirelationships. 2. Nonexperimentalidataisicalled . a. cross-sectionalidata b. timeiseriesidata c. observationalidata d. panelidata Answer:ib iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iWhatis iEconometrics?BUSPROG: i Feedback: 3. Whichiofitheifollowingisitrueiofiexperimentalidata? a. Experimentalidataiareicollectedinilaboratoryienvironmentsinitheinaturalisciences. b. Experimentalidataicannotibeicollectediniaicontrolledienvironment. c. Experimentalidataisisometimesicallediobservationalidata. d. Experimentalidataisisometimesicallediretrospectiveidata. Answer:ia iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iWhatisiEconometrics? BUSPROG: Feedback: 4. Aniempiricalianalysisireliesion toitestiaitheory. a. commonisense b. ethicaliconsiderations c. data d. customsiandiconventions Answer:ic iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iStepsiniEmpiricaliEconomic iAnalysisBUSPROG: i Feedback:iAniempiricalianalysisireliesionidataitoitestiaitheory. 5. Theitermi‘u’iinianieconometricimodelisiusuallyireferreditoiasithe . a. erroriterm b. parameter c. hypothesis d. dependentivariable Answer:ia iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iStepsiniEmpiricaliEconomic iAnalysisBUSPROG: i Feedback:iTheitermiuiinianieconometricimodeliisicalleditheierroritermioridisturbanceiterm. 6. Theiparametersiofianieconometricimodel . a. includeialliunobservedifactorsiaffectingitheivariableibeingistudied b. describeitheistrengthiofitheirelationshipibetweenitheivariableiunderistudyianditheifactorsiaffectingiit c. referitoitheiexplanatoryivariablesincludedinitheimodel d. referitoitheipredictionsithaticanibeimadeiusingitheimodel Answer:ib iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iStepsiniEmpiricaliEconomic iAnalysisBUSPROG: i Feedback:iTheiparametersiofianieconometricimodelidescribeitheidirectioniandistrengthiof itherelationship i ibetweenitheivariableiunderistudyianditheifactorsiaffectingiit. 7. Whichiofitheifollowingiisitheifirstistepiiniempiricalieconomicianalysis? a. Collectioniofidata b. Statementiofihypotheses c. Specificationiofianieconometricimodel d. Testingiofihypotheses Answer:ic iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iStepsiniEmpiricaliEconomic iAnalysisBUSPROG: i Feedback:iTheifirstistepiiniempiricalieconomicianalysisisitheispecificationiofitheieconometricimodel. 8. Aidataisetithaticonsistsiofiaisampleiofiindividuals,ihouseholds,ifirms,icities,istates,icountries,ior iavariety i iofiotheriunits,itakeniatiaigivenipointiinitime,iisicalledia(n) . a. cross-sectionalidataiset b. longitudinalidataiset c. timeiseriesidataiset d. experimentalidataiset Answer:ia iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i Feedback:iAidataisetithaticonsistsiofiaisampleiofindividuals,ihouseholds,ifirms,icities,istates,icountries,ori iaivarietyiofiotheriunits,itakeniatiaigivenipointiinitime,iisicallediaicross-sectionalidataiset. 9. Dataionitheiincomeiofilawigraduatesicollectediatidifferentitimesiduringitheisameiyearis . a. panelidata b. experimentalidata c. timeiseriesidata d. cross-sectionalidata Answer:id iDifficulty:iEasy Bloom’s:iApplication A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i iAnalytic Feedback:iAidataisetithaticonsistsiofiaisampleiofindividuals,ihouseholds,ifirms,icities,istates,icountries,ori iaivarietyiofiotheriunits,itakeniatiaigivenipointiinitime,iisicallediaicross-sectionalidataiset. iTherefore,data i ionitheiincomeiofilawigraduatesioniaiparticulariyeariareiexamplesioficross-sectionalidata. 10. Aidataisetithaticonsistsiofiobservationsioniaivariableioriseveralivariablesioveritimeiisicallediai i dataiset. a. binary b. cross-sectional c. timeiseries d. experimental Answer:ic iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i Feedback:iAitime-seriesidataiseticonsistsiofiobservationsioniaivariableioriseveralivariablesioveritime. 11. Whichiofitheifollowingiisianiexampleiofitimeiseriesidata? a. Dataionitheiunemploymentiratesinidifferentipartsiofiaicountryiduringiaiyear. b. Dataionitheiconsumptioniofiwheatibyi200ihouseholdsiduringiaiyear. c. Dataionitheigrossidomesticiproductiofiaicountryioveriaiperiodiofi10iyears. d. Dataionitheinumberiofivacanciesinivariousidepartmentsiofianiorganizationioniaiparticularimonth. Answer:ic iDifficulty:iEasy Bloom’s:iApplication A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i iAnalytic Feedback:iAitime-seriesidataiseticonsistsiofiobservationsioniaivariableioriseveralivariablesiover time.iTherefore,idataionitheigrossidomesticiproductiofiaicountryioveriaiperiodiofi10iyearsisianiexampleofi itimeiseriesidata. 12. Whichiofitheifollowingirefersitoipanelidata? a. Dataionitheiunemploymentirateiiniaicountryioveriai5-yeariperiod b. Dataionitheibirthirate,ideathirateiandipopulationigrowthirateinidevelopingicountriesioveriai10- yearperiod. i c. Dataionitheincomeiofi5imembersiofiaifamilyioniaiparticulariyear. d. Dataionitheipriceiofiaicompany’sishareiduringiaiyear. Answer:ib iDifficulty:iEasy Bloom’s:iApplication A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i iAnalytic Feedback:iAipanelidataiseticonsistsiofiaitimeiseriesiforieachicross-sectionalimemberiinitheidataiset. iTherefore,idataionitheibirthirate,ideathirateiandinfantimortalityirateinidevelopingicountriesioveriai10-year i iperiodirefersitoipanelidata. 13. Whichiofitheifollowingiisiaidifferenceibetweenipaneliandipooledicross-sectionalidata? a. Aipanelidataiseticonsistsiofidataionidifferenticross-sectionaliunitsioveriaigiveniperiodiofitimeiwhile iapooled i idataiseticonsistsiofidataionitheisameicross-sectionaliunitsioveriaigiveniperiodiofitime. b. Aipanelidataiseticonsistsiofidataionitheisameicross-sectionaliunitsioveriaigiveniperiodiofitimeiwhile iapooled i idataiseticonsistsiofidataionidifferenticross-sectionaliunitsioveriaigiveniperiodiofitime c. Aipanelidataiconsistsiofidataioniaisingleivariableimeasurediatiaigivenipointiinitimeiwhileiaipooled idataset i iconsistsiofidataionitheisameicross-sectionaliunitsioveriaigiveniperiodiofitime. d. Aipanelidataiseticonsistsiofidataioniaisingleivariableimeasurediatiaigivenipointiinitimeiwhileia ipooleddata i iseticonsistsiofidataionimoreithanioneivariableiatiaigivenipointiinitime. Answer:ib iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i Feedback:iAipanelidataiseticonsistsiofidataionitheisameicross-sectionaliunitsioveriaigiveniperiodiof itimewhile i iaipooledidataiseticonsistsiofidataionitheisameicross-sectionaliunitsioveriaigiveniperiodiofitime. 14. i hasiaicausalieffection . a. Income;iunemployment b. Height;ihealth c. Income;iconsumption d. Age;iwage Answer:ic Difficulty: iModerateBloom’s: i iApplication A-Head:iCausalityianditheiNotioniofiCeterisiParibusiniEconometric iAnalysisBUSPROG: i iAnalytic Feedback:iIncomeihasiaicausalieffectioniconsumptionibecauseianiincreaseiniincomeileadsitoianincrease i iiniconsumption. 15. Whichiofitheifollowingiisitrue? a. Aivariableihasiaicausalieffectionianotherivariableiifibothivariablesincreaseior idecreasesimultaneously. i b. Theinotioniofi‘ceterisiparibus’iplaysianimportantiroleinicausalianalysis. c. Difficulty ininferringicausalityidisappearsiwhenistudyingidataiatifairlyihighilevelsiofiaggregation. d. Theiproblemiofinferringicausalityiarisesifiexperimentalidataiisiusediforianalysis. Answer: i b iDifficulty:iModerate Bloom’s:iKnowledge A-Head:iCausalityianditheiNotioniofiCeterisiParibusiniEconometric iAnalysisBUSPROG: i Feedback:iTheinotioniofi‘ceterisiparibus’iplaysianimportantiroleinicausalianalysis. 16. Experimentalidataiareisometimesicallediretrospectiveidata. Answer:iFalse iDifficulty:iEasy iBloom’s:iKnowledge A-Head:iWhatis iEconometrics?BUSPROG: i Feedback:iNonexperimentalidataiareisometimesicallediretrospectiveidata. 17. Anieconomicimodeliconsistsiofimathematicaliequationsithatidescribeivariousirelationships ibetweeneconomic i ivariables. Answer:iTrue iDifficulty:iEasy iBloom’s:iKnowledge A-Head:iStepsiniEmpiricaliEconomic iAnalysisBUSPROG: i Feedback:iAnieconomicimodeliconsistsiofimathematicaliequationsithatidescribeivarious irelationshipsbetween i ieconomicivariables. 18. Aicross-sectionalidataiseticonsistsiofiobservationsioniaivariableioriseveralivariablesioveritime. Answer:iFalse iDifficulty:iEasy iBloom’s:iKnowledge A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i Feedback:iAitimeiseriesidataiseticonsistsiofiobservationsioniaivariableioriseveralivariablesiovertime. i 19. Aitimeiseriesidataiisialsoicallediailongitudinalidataiset. Answer:iFalse iDifficulty:iEasy iBloom’s:iKnowledge A-Head:iTheiStructureiofiEconomic iDataBUSPROG: i Feedback:iAitimeiseriesidataiisialsoicallediailongitudinalidataiset. 20. Theinotionioficeterisiparibusimeansi“otherifactorsibeing iequal.”Answer: i iTrue Difficulty:iEasy iBloom’s:iKnowledge A-Head:iCausalityianditheiNotioniofiCeterisiParibusiniEconometric iAnalysisBUSPROG: i Feedback:iTheinotionioficeterisiparibusimeansi“otherifactorsibeingiequal.” Chapteri2 1. Aidependentivariableiisialsoiknowniasia(n) . a. explanatoryivariable b. controlivariable c. predictorivariable d. responseivariable Answer:id iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iDefinitioniofitheiSimpleiRegression iModelBUSPROG: i Feedback:iAidependentivariableiisiknowniasiairesponseivariable. 2. Ifiaichangeiinivariableixicausesiaichangeiinivariableiy,ivariableixiisicalledithe . a. dependentivariable b. explainedivariable c. explanatoryivariable d. response ivariableAnswer: i ic Difficulty:iEasy Bloom’s:iComprehension A-Head:iDefinitioniofitheiSimpleiRegression iModelBUSPROG: i Feedback:iIfiaichangeiinivariableixicausesiaichangeinivariableiy,ivariableixiisicalleditheiindependentvariable i ioritheiexplanatoryivariable. 3. Initheiequationiyi= β0 + β1 i i i i ixi+iu, β0 isithe . a. dependentivariable b. independentivariable c. slopeiparameter d. interceptiparameter Answer:id iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iDefinitioniofitheiSimpleiRegression iModelBUSPROG: i Feedback:iInitheiequationiyi= β0 + β1 i i i i ixi+iu, β0 isitheinterceptiparameter. 4. Initheiequationiyi= β0 + β1 i i i i ixi+iu,iwhatiisitheiestimatedivalueiofi i i β0 i i i i ? a. ´y−β ^ 1 ix´ b. ´y+i βi 1 ix´ y iyi−i ´ ¿ ¿ ¿ c. (xi i−´x)¿i n ∑¿ i=1 ¿ n d. ∑xyi i=1 Answer:ia iDifficulty:iEasy Bloom’s:iKnowledge 0 1 1 A-Head:iDerivingitheiOrdinaryiLeastiSquares iEstimatesBUSPROG: i Feedback:iTheiestimatedivalueiof β0 is ´y−β ^ 1 ix´ . 5. Initheiequationici= β0 + β1 i i i i i ii+iu,icidenotesiconsumptioniandi idenotesiincome.iWhatiisithe residualiforithei5 th iobservationif c5 i i i i i=$500 iand a.i$975 b.i$300 c.i$25 d.i$50 c^5 i i i i i=$475? Answer:ic iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iDerivingitheiOrdinaryiLeastiSquares iEstimatesBUSPROG: i Feedback:iTheiformulaiforicalculatingitheiresidualiforithei th iobservationis u^i=yi i i−^yi i i i i i .iInithisicase, theiresidualiis u^5=c5 i−c^5 =$500i-$475=i$25. 6. Whatidoesitheiequation ^y=β ^ 0+i βi ^ 1 ix denoteiifitheiregressioniequationiisiyi=iβi i+iβ ixi i+iu? a. Theiexplainedisumiofisquares b. Theitotalisumiofisquares c. Theisampleiregressionifunction d. Theipopulationiregressionifunction Answer:ic iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iDerivingitheiOrdinaryiLeastiSquares iEstimatesBUSPROG: i Feedback:iTheiequation ^y=β ^ 0+i βi ^ 1 ix denotesitheisampleiregressionifunctioniofitheigiveniregression model. 7. Consideritheifollowingiregressionimodel:iyi=iβ0 i+iβ1x1 i+iu.iWhichiofitheifollowingiisiaiproperty iofOrdinary i iLeastiSquarei(OLS)iestimatesiofithisimodelianditheiriassociatedistatistics? a. Theisum,iandithereforeitheisampleiaverageiofitheiOLSiresiduals,iisipositive. i i i i b. TheisumiofitheiOLSiresidualsiisinegative. c. TheisampleicovarianceibetweenitheiregressorsianditheiOLSiresidualsisipositive. d. Theipointi(i i i i x´ , ´yi i i)ialwaysiliesionitheiOLSiregressioniline. Answer:id iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iPropertiesiofiOLSioniAnyiSampleiofiDataBUSPROG: i Feedback:iAniimportantipropertyiofitheiOLSiestimatesisithatitheipointi( , y´ )ialwaysiliesionithe OLSiregressioniline.iIniotheriwords,iif x=´xi i i ,itheipredictedivalueiof yisi i´yi i i . 8. Theiexplainedisumiofisquaresiforitheiregressionifunction, yi=β0 i+βi 1 i x1+ui 1 i i i i ,iisidefinedias . n ∑(yi i−´i y)i 2 i=1 n ∑(yi i−^y)i 2 i=1 n c. ∑ui^i i=1 n ∑(u i) 2 i=1 Answer:ib iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iPropertiesiofiOLSioniAnyiSampleiof iDataBUSPROG: i Feedback:iTheiexplainedisumiofisquaresisidefinedias n ∑(yi i−^y)i 2 i=1 9. Ifitheitotalisumiofisquaresi(SST)iiniairegressioniequationiisi81,ianditheiresidualisumiofisquaresi(SSR) iis25, i iwhatiisitheiexplainedisumiofisquaresi(SSE)? a. 64 b. 56 a. b. d. Thank you, EMAIL ME @ For help with Assignments/Essay/Projects/Test Banks/practice Exams and any other classwork. c. 32 d. 18 Answer: i b iDifficulty:iModerate Bloom’s:iApplication A-Head:iPropertiesiofiOLSioniAnyiSampleiof iDataBUSPROG: i iAnalytic Feedback:iTotalisumiofisquaresi(SST)iisigivenibyitheisumiofiexplainedisumiofisquaresi(SSE)iandiresidualsumi iofisquaresi(SSR).iTherefore,iinithisicase,iSSE=81-25=56. 10. Ifitheiresidualisumiofisquaresi(SSR)iiniairegressionianalysisiisi66ianditheitotalisumiofisquaresi(SST) iisequal i itoi90,iwhatiisitheivalueiofitheicoefficientiofidetermination? a.i0.73 b.i0.55 c.i0.27 d.i1.2 Answer:ic Difficulty: iModerateBloom’s: i iApplication A-Head:iPropertiesiofiOLSioniAnyiSampleiofiDataBUSPROG: i iAnalytic Feedback:iTheiformulaiforicalculatingitheicoefficientiofideterminationis R 2=1−i 66i =0.27 90 R 2=1−i SSR SST .iInithisicase, 11. Whichiofitheifollowingiisiainonlineariregression imodel?a. i iyi=iβ0 i+iβ1x 1/2 i+iu yi=iβ0 i+iβ1logixi+u =i1i/i(β0 i+iβ1x)i+iu =iβ0 i+iβ1xi+iu Answer:ic Difficulty:iModerate iBloom’s:iComprehension A-Head:iPropertiesiofiOLSioniAnyiSampleiof iDataBUSPROG: i Feedback:iAiregressionimodeliisinonlineariifitheiequationiisinonlinearinitheiparameters.iInithisicase,y=1 i i/i(β0 i+iβ1x)i+iuiisinonlineariasiitiisinonlineariiniitsiparameters. 12. WhichiofitheifollowingiisiassumediforiestablishingitheiunbiasednessiofiOrdinaryiLeastiSquare i(OLS)estimates? i a. Theierroritermihasianiexpectedivalueiofi1igivenianyivalueiofitheiexplanatoryivariable. b. Theiregressioniequationiisilinearinitheiexplainediandiexplanatoryivariables. c. Theisampleioutcomesionitheiexplanatoryivariableiareiallitheisameivalue. d. Theierroritermihasitheisameivarianceigivenianyivalueiofitheiexplanatoryivariable. Answer:id iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iExpectediValuesiandiVariancesiofitheiOLS iEstimatorsBUSPROG: i Feedback:iTheierroriuihasitheisameivarianceigivenianyivalueiofitheiexplanatoryivariable. 13. Theierroritermiiniairegressioniequationiisisaiditoiexhibitihomoskedastictyif . a. itihasizeroiconditionalimean b. itihasitheisameivarianceiforiallivaluesiofitheiexplanatoryivariable. c. itihasitheisameivalueiforiallivaluesiofitheiexplanatoryivariable d. ifitheierroritermihasiaivalueiofioneigivenianyivalueiofitheiexplanatoryivariable. Answer:ib iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iExpectediValuesiandiVariancesiofitheiOLS iEstimatorsBUSPROG: i Feedback:iTheierroriterminiairegressioniequationiisisaiditoiexhibitihomoskedastictyifiitihasithe isamevariance i iforiallivaluesiofitheiexplanatoryivariable. 14. Initheiregressioniofiyionix,itheierroritermiexhibitsiheteroskedasticityiif . a. itihasiaiconstantivariance b. Var(y|x)iisiaifunctioniofix c. xiisiaifunctioniofiy d. yiisiaifunctioniofix Answer:ib iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iExpectediValuesiandiVariancesiofitheiOLS iEstimatorsBUSPROG: i i i i n Feedback:iHeteroskedasticityisipresentiwheneveriVar(y|x)isiaifunctioniofixibecauseiVar(u|x)i=iVar(y|x). 15. Whatiisitheiestimatedivalueiofitheislopeiparameteriwhenitheiregressioniequation,iyi=iβ0 i+iβ1x1 i+ iupasses i ithroughitheiorigin? n a. ∑yi i i=1 y ¿ ¿ b. ¿ ) ∑¿ i=1 n ∑xi i i iyi i=1 c. n ∑xi i 2 i=1 n ∑(yi i−´i y)i 2 i=1 Answer:ic iDifficulty:iEasy Bloom’s:iKnowledge A-Head:iRegressionithroughitheiOriginiandiRegressionionia iConstantBUSPROG: i Feedback:iTheiestimatedivalueiofitheislopeiparameteriwhenitheiregressioniequationipassesithroughithe originiis n ∑xi i iyi i=1 in . ∑xi i 2 i=1 16. Ainaturalimeasureiofitheiassociationibetweenitwoirandomivariablesisitheicorrelationicoefficient. Answer:iTrue iDifficulty:iEasy iBloom’s:iKnowledge A-Head:iDefinitioniofitheiSimpleiRegression iModelBUSPROG: