Panel Data Econometrics: Empirical Applications

592 Pages·2019·9.142 MB·English

by  Mike Tsionas

Panel Data Econometrics
Panel Data
Econometrics
Empirical Applications
Edited By
Mike Tsionas
AcademicPressisanimprintofElsevier
125LondonWall,LondonEC2Y5AS,UnitedKingdom
525BStreet,Suite1650,SanDiego,CA92101,UnitedStates
50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates
TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom
©2019ElsevierInc.Allrightsreserved.
Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,
electronicormechanical,includingphotocopying,recording,oranyinformationstorageand
retrievalsystem,withoutpermissioninwritingfromthepublisher.Detailsonhowtoseek
permission,furtherinformationaboutthePublisher’spermissionspoliciesandour
arrangementswithorganizationssuchastheCopyrightClearanceCenterandtheCopyright
LicensingAgency,canbefoundatourwebsite:www.elsevier.com/permissions.
Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythe
Publisher(otherthanasmaybenotedherein).
Notices
Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience
broadenourunderstanding,changesinresearchmethods,professionalpractices,ormedical
treatmentmaybecomenecessary.
Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgein
evaluatingandusinganyinformation,methods,compounds,orexperimentsdescribedherein.
Inusingsuchinformationormethodstheyshouldbemindfuloftheirownsafetyandthesafety
ofothers,includingpartiesforwhomtheyhaveaprofessionalresponsibility.
Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,
assumeanyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterof
productsliability,negligenceorotherwise,orfromanyuseoroperationofanymethods,
products,instructions,orideascontainedinthematerialherein.
LibraryofCongressCataloging-in-PublicationData
AcatalogrecordforthisbookisavailablefromtheLibraryofCongress
BritishLibraryCataloguing-in-PublicationData
AcataloguerecordforthisbookisavailablefromtheBritishLibrary
ISBN978-0-12-815859-3
ForinformationonallAcademicPresspublications
visitourwebsiteathttps://www.elsevier.com/books-and-journals
Publisher:CandiceJanco
AcquisitionEditor:ScottBentley
EditorialProjectManager:SusanIkeda
ProductionProjectManager:MariaBernard
CoverDesigner:MilesHitchen
TypesetbySPiGlobal,India
Contributors
Numbersinparaenthesesindicatethepagesonwhichtheauthors’contrbutionsbegin.
PaulD.Allison(547),UniversityofPennsylvania,Philadelphia,PA,UnitedStates
SofiaAnyfantaki(865),AthensUniversityofEconomicsandBusiness;BankofGreece,
Athens,Greece
ScottE.Atkinson(495),DepartmentofEconomics,UniversityofGeorgia,Athens,GA,
UnitedStates
MichaelaBenzeval(709),InstituteforSocialandEconomicResearch,Universityof
Essex,Colchester,UnitedKingdom
KeshabBhattarai(665),UniversityofHullBusinesSchool,Hull,UnitedKingdom
AndradaBilan(743),UniversityofZurichandSwissFinanceInstitute,Z€urich,
Switzerland
ApostolosDavillas(709),InstituteforSocialandEconomicResearch,Universityof
Essex,Colchester,UnitedKingdom
JandeDreu(839),BBVA,GlobalDebtAdvisory,CiudadBBVa,Madrid,Spain
HansDegryse(743),KULeuven,Leuven,Belgium;CEPR,Washington,DC,United
States
DongDing(405),DepartmentofEconomics,RiceUniversity,Houston,TX,UnitedStates
KyriakosDrivas(771),DepartmentofInternational&EuropeanEconomicStudies,
AthensUniversityofEconomicsandBusiness,Athens,Greece
ClaireEconomidou(771),DepartmentofEconomics,UniversityofPiraeus,Piraeus,
Greece
AlmasHeshmati(885,931),DepartmentofEconomics,SogangUniversity,Seoul,South
Korea
DavidHumphrey(609),FloridaStateUniversity,Tallahassee,FL,UnitedStates
VassoIoannidou(839),AccountingandFinanceDepartment,LancasterUniversity
ManagementSchool,Lancaster,UnitedKingdom
GeraintJohnes(467),LancasterUniversityManagementSchool,LancasterUniversity,
Lancaster,UnitedKingdom
JillJohnes(467),HuddersfieldBusinessSchool,UniversityofHuddersfield,
Huddersfield,UnitedKingdom
AndrewM.Jones(709),DepartmentofEconomicsandRelatedStudies,Universityof
York,York,UnitedKingdom;CentreforHealthEconomics,MonashUniversity,
Clayton,VIC,Australia
xiii
xiv Contributors
SarantisKalyvitis(865),AthensUniversityofEconomicsandBusiness,Athens,Greece
MargaritaKatsimi(865),AthensUniversityofEconomicsandBusiness,Athens,Greece
CESifo,Munich,Germany
NamSeokKim(885),DepartmentofEconomics,MaxwellSchoolofCitizenshipand
PublicAffairs,SyracuseUniversity,Syracuse,NY,UnitedStates
KonstantinosN.Konstantakis(953),NationalTechnicalUniversityofAthens,Athens,
Greece
GeorgiaKosmopoulou(521),UniversityofOklahoma,Norman,OK,UnitedStates
EsfandiarMaasoumi(931),DepartmentofEconomics,EmoryUniversity,Atlanta,
GA,UnitedStates
E.C.Mamatzakis(801),UniversityofSussexBusinessSchool,UniversityofSussex,
Brighton,UnitedKingdom
RicoMerkert(583),TheUniversityofSydneyBusinessSchool,NSW,Sydney,Australia
PanayotisG.Michaelides(953),NationalTechnicalUniversityofAthens,Athens,
Greece
EnriqueMoral-Benito(547),BankofSpain,Madrid,Spain
CorinneMulley(583),TheUniversityofSydneyBusinessSchool,NSW,Sydney,
Australia
DanielNedelescu(521),UniversityofOklahoma,Norman,OK,UnitedStates
KuchulainO’Flynn(743),UniversityofZurichandSwissFinanceInstitute,Z€urich,
Switzerland
StevenOngena(743),UniversityofZurichandSwissFinanceInstitute,Z€urich,
Switzerland;KULeuven,Leuven,Belgium;CEPR,Washington,DC,UnitedStates
FletcherRehbein(521),UniversityofOklahoma,Norman,OK,UnitedStates
RobinC.Sickles(405),DepartmentofEconomics,RiceUniversity,Houston,TX,
UnitedStates
ChristophSiebenbrunner(639),UniversityofOxford,MathematicalInstitute;
InstituteforNewEconomicThinking,Oxford,UnitedKingdom
MichaelSigmund(639),OesterreichischeNationalbank(OeNB),Vienna,Austria
C.Staikouras(801),SchoolofBusinessAdministration,AthensUniversityof
EconomicsandBusiness,Athens,Greece
BiweiSu(931),DepartmentofEconomics,SogangUniversity,Seoul,SouthKorea
EiriniThomaidou(865),AthensUniversityofEconomicsandBusiness,Athens,Greece
MikeG.Tsionas(771,801,953),DepartmentofEconomics,LancasterUniversity
ManagementSchool,Lancaster,UnitedKingdom
RichardWilliams(547),UniversityofNotreDame,NotreDame,IN,UnitedStates
General Introduction
Panel data always have been at the center of econometric research and have
been used extensively in applied economic research to refute a variety of
hypotheses. The chapters in these two volumes represent, to a large extent,
muchofwhathasbeenaccomplishedintheprofessionduringthelastfewyears.
Naturally,thisisaselectivepresentationandmanyimportanttopicshavebeen
left out because of space limitations. The books cited at the end of this Intro-
duction, however, are well known and provide more details about specific
topics.Thecoverageextendsfromfixedandrandomeffectformulationstonon-
linear models and cointegration. Such themes have been instrumental in the
development of modern theoretical andapplied econometrics.
Paneldataareusedquiteofteninapplications,asweseeinVolume2ofthis
book.Therangeofapplicationsisvast,extendingfromindustrialorganization
andlaboreconomicstogrowth,development,health,banking,andthemeasure-
ment of productivity. Although panel data provide more degrees of freedom,
their proper use is challenging. The modeling of heterogeneity cannot be
exhausted to fixed and random effect formulations, and slope heterogeneity
has to be considered. Dynamic formulations are highly desirable, but they
are challenging both because of estimation issues and because unit roots and
cointegrationcannotbeignored.Moreover,causalityissuesfigureprominently,
althoughtheyseemtohavereceivedlessattentionrelativetotime-seriesecono-
metrics.Relativetotime-seriesorcross-sections,thedevelopmentofspecifica-
tion tests for panel data seems to have been slower than usual.
Thechaptersinthesetwovolumesshowthegreatpotentialofpaneldatafor
both theoretical and applied research. There are more opportunities as more
problems arise, particularly when practitioners and economic theorists get
together todiscuss the empirical refutation oftheir theories orconjectures.In
myview,opportunitiesarelikelytoarisefromthreedifferentareas:theinter-
actionofeconometricswithgametheoryandindustrialorganization;theprom-
inence of both nonparametric and Bayesian techniques in econometrics; and
structural models that explain heterogeneity beyond the familiar paradigm of
fixed/randomeffects.
1. Detailed Presentation
In Chapter 1, Stephen Hall provides background material about econometric
methods that is useful in making thisvolume self-contained.
In Chapter 2, Jeffrey M. Wooldridge and Wei Lin study testing and est-
imation in panel data models with two potential sources of endogeneity: that
xv
xvi GeneralIntroduction
because ofcorrelation ofcovariateswith time-constant, unobserved heteroge-
neity and that because of correlation of covariates with time-varying idiosyn-
cratic errors. In the linear case, they show that two control function
approachesallowustotestexogeneitywithrespecttotheidiosyncraticerrors
whilebeingsilentonexogeneitywithrespecttoheterogeneity.Thelinearcase
suggests a general approach for nonlinear models. The authors consider two
leading cases of nonlinear models: an exponential conditional mean function
for nonnegative responses and a probit conditional mean function for binary
or fractional responses. In the former case, they exploit the full robustness of
the fixed effects Poisson quasi-MLE; for the probit case, they propose corre-
lated randomeffects.
InChapter3,WilliamH.GreeneandQiushiZhangpointoutthatthepanel
data linear regression model has been studied exhaustively in a vast body of
literature that originates with Nerlove (1966) and spans the entire range of
empiricalresearchineconomics.Thischapterdescribestheapplicationofpanel
data methods to some nonlinear models such as binary choice and nonlinear
regression,wherethetreatmenthasbeenmorelimited.Someofthemethodol-
ogyoflinearpaneldatamodelingcanbecarriedoverdirectlytononlinearcases,
while other aspects must be reconsidered. The ubiquitous fixed effects linear
model is the most prominent case of this latter point. Familiar general issues,
includingdealingwithunobservedheterogeneity,fixedandrandomeffects,ini-
tial conditions, and dynamic models, are examined. Practical considerations,
such as incidental parameters, latent class and random parameters models,
robustcovariance matrix estimation,attrition,andmaximumsimulatedlikeli-
hood estimation, are considered. The authors review several practical specifi-
cations that have been developed around a variety of specific nonlinear
models, including binary and ordered choice, models for counts, nonlinear
regressions, stochasticfrontier, and multinomial choicemodels.
InChapter4,JeffreyS.RacineandChristopherF.Parmeterprovideasurvey
ofnonparametricmethodsforestimationandinferenceinapaneldatasetting.
Methodssurveyedincludeprofilelikelihood,kernelsmoothers,andseriesand
sieveestimators.Thepracticalapplicationofnonparametricpanel-basedtech-
niquesislessprevalentthannonparametricdensityandregressiontechniques.
Thematerialcoveredinthischapterwillproveusefulandfacilitatetheiradop-
tionby practitioners.
InChapter5,KienTranandLeventKutluprovidearecentdevelopmentin
panelstochasticfrontiermodelsthatallowsforheterogeneity,endogeneity,or
both. Specifically, consistent estimation of the models’ parameters as well as
observation-specific technical inefficiency is discussed.
In Chapter 6, Stefanos Dimitrakopoulos and Michalis Kolossiatis discuss
how Bayesian techniques can be used to estimate the Poisson model, a well-
knownpanelcountdatamodel,withexponentialconditionalmean.Inparticu-
lar,theyfocusontheimplementationofMarkovChainMonteCarlomethodsto
various specifications of this model that allow for dynamics, latent
GeneralIntroduction xvii
heterogeneityand/orserialerrorcorrelation.Thelatentheterogeneitydistribu-
tionisassignedanonparametricstructure,whichisbasedontheDirichletpro-
cessprior.Theinitialconditionsproblemalsoisaddressed.Foreachresulting
modelspecification,theyprovidetheassociatedinferentialalgorithmforcon-
ductingposteriorsimulation.Relevantcomputercodesarepostedasanonline
supplement.
In Chapter 7, Chih-Hwa Kao and Fa Wang review and explain the tech-
niques used in Hahn and Newey (2004) and Fernandez-Val and Weidner
(2016)toderivethelimitdistributionofthefixedeffectsestimatorofsemipara-
metricpanelswhenthetimedimensiontendstoinfinityjointlywiththecross-
section dimension. The techniques of these two papers are representative and
understandingtheirworkingmechanismisagoodstartingpoint.Underauni-
fied framework, this paper explicitly points out the difficulties in extending
frommodelswithfixeddimensionalparameterspacetopanelswithindividual
effectsandfrompanelwithindividualeffectstopanelwithbothindividualand
timeeffects,andhowHahnandNewey(2004)andFernandez-ValandWeidner
(2016) solve them.
InChapter8,BoHonoreandEkateriniKyriazidoustudytheidentification
ofmultivariatedynamicpaneldatalogitmodelswithunobservedfixedeffects.
They show that in the pure VAR(1) case (without exogenous covariates) the
parameters are identified with as few as four waves of observations and can
beestimatedconsistentlyatratesquare-root-nwithanasymptoticnormaldis-
tribution.Furthermore,theyshowthattheidentificationstrategyofHonoreand
Kyriazidou (2000) carries over inthe multivariate logit case whenexogenous
variables are included in the model. The authors also present an extension of
the bivariate simultaneous logit model of Schmidt and Strauss (1975) to the
panel case, allowing for contemporaneous cross-equation dependence both in
static and dynamic frameworks. The results of this chapter are of particular
interest for short panels, that is, for small T.
InChapter9,SubalKumbhakarandChristopherF.Parmeternoticethat,in
thelast5years,wehaveseenamarkedincreaseinpaneldatamethodsthatcan
handle unobserved heterogeneity, persistent inefficiency, and time-varying
inefficiency.Althoughthisadvancementhasopeneduptherangeofquestions
and topics for applied researchers, practitioners,andregulators, there are var-
iousestimationproposalsforthesemodelsand,todate,nocomprehensivedis-
cussion about how these estimators work or compare to one another. This
chapter lays outin detail the various estimators and how they can be applied.
Several recent applications of these methods are discussed, drawing connec-
tions from the econometricframework toreal applications.
InChapter10,PeterPedronidiscussesthechallengesthatshapepanelcoin-
tegration techniques, with an emphasis on the challenge of maintaining the
robustnessofcointegrationmethods whentemporal dependenciesinteractwith
both cross-sectional heterogeneities and dependencies. It also discusses some
of the open challenges that lie ahead, including the challenge of generalizing
xviii GeneralIntroduction
tononlinearandtimevaryingcointegratingrelationships.Thechapteriswritten
inanontechnicalstylethatisintendedtomaketheinformationaccessibletonon-
specialists,withanemphasisonconveyingtheunderlyingconceptsandintuition.
In Chapter 11, by P.A.V.B. Swamy, Peter von zur Muehlen, Jatinder
S.Mehta, andI-Lok Changshowthatestimatorsofthe coefficients ofecono-
metric models are inconsistent if their coefficients and error terms are not
unique.Theypresentmodelshavinguniquecoefficientsanderrorterms,with
specificapplicabilitytotheanalysesofpaneldatasets.Theyshowthatthecoef-
ficientonanincludednonconstantregressorofamodelwithuniquecoefficients
anderrortermisthesumofbias-freeandomitted-regressorbiascomponents.
Thissum,whenmultipliedbythenegativeratioofthemeasurementerrortothe
observedregressor,providesameasurement-errorbiascomponentofthecoef-
ficient.Thisresultisimportant becauseoneneedsthebias-freecomponent of
thecoefficientontheregressortomeasurethecausaleffectofanincludednon-
constant regressorof amodelon itsdependent variable.
InChapter 12, Arne Heggingsen and Geraldine Henningsen give practical
guidelines for the analysis of panel data with the statistical software R. They
start by suggesting procedures for exploring and rearranging panel data sets
and for preparing them for further analyses. A large part of this chapter dem-
onstrates the application of various traditional panel data estimators that fre-
quently are used in scientific and applied analyses. They also explain the
estimation of several modern panel data models such as panel time series
models and dynamic panel data models. Finally, this chapter shows how to
use statistical tests to test critical hypotheses under different assumptions and
howtheresultsofthesetestscanbeusedtoselectthepaneldataestimatorthat
is mostsuitablefor a specific empirical panel data analysis.
InChapter13,RobinSicklesandDongDingempiricallyassesstheimpact
ofcapitalregulationsoncapitaladequacyratios,portfoliorisklevelsandcost
efficiencyforbanksintheUnitedStates.UsingalargepaneldataofUSbanks
from 2001 to 2016, they first estimate the model using two-step generalized
method of moments (GMM) estimators. After obtaining residuals from the
regressions,theyproposeamethodtoconstructthenetworkbasedonclustering
oftheseresiduals.Theresidualscapturetheunobservedheterogeneitythatgoes
beyondsystematicfactorsandbanks’businessdecisionsthataffectitslevelof
capital,risk,andcostefficiency,andthusrepresentunobservednetworkhetero-
geneityacrossbanks.Theythenreestimatethemodelinaspatialerrorframe-
work. The comparisons of Fixed Effects, GMM Fixed Effect models with
spatial fixed effects models provide clear evidence of the existence of unob-
servedspatialeffectsintheinterbanknetwork.Theauthorsfindastrictercapital
requirementcausesbankstoreduceinvestmentsinrisk-weightedassets,butat
thesametime,increaseholdingsofnonperformingloans,suggestingtheunin-
tendedeffectsofhighercapitalrequirementsoncreditrisks.Theyalsofindthe
amountofcapitalbuffershasanimportantimpactonbanks’managementprac-
tices even when regulatory capital requirements are notbinding.