Table Of ContentMarnoVerbeek
PanelMethodsforFinance
De Gruyter Studies in the
Practice of Econometrics
|
Series Editor
MarnoVerbeek
Volume 1
Marno Verbeek
Panel Methods for
Finance
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A Guide to Panel Data Econometrics for Financial
Applications
ISBN978-3-11-066013-5
e-ISBN(PDF)978-3-11-066073-9
e-ISBN(EPUB)978-3-11-066081-4
ISSN2570-0928
e-ISSN2570-0936
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ToMarcella
Preface
Financial data are typically characterised by a time-series dimension and a cross-
sectionaldimension.Forexample,wemayobservefinancialinformationonagroup
offirmsoveranumberofyears,orwemayobservereturnsofallstockstradedatthe
NewYorkStockExchange(NYSE)overaperiodof120months.Accordingly,economet-
ricmodellinginfinancerequiresappropriateattentiontothesetwo–andoccasionally
morethantwo–dimensionsofthedata.Paneldatatechniquesaredevelopedtodo
exactlythis.Thisbookprovidesanoverviewofcommonlyappliedpanelmethodsfor
financialapplications,withafocusoncaseswherethecross-sectionaldimensionis
large(andthetime-seriesdimensionmaybelimited).
Campbelletal.(1997)isoneofthefirstmonographsfocusingontheuseofecono-
metricsinfinance,mostlyintheassetpricingarea.Afewyearslater,Gourierouxand
Jasiak(2001)provideaguideto“financialeconometrics”atanadvancedlevelwitha
strongfocusontime-seriesmodels(includingcontinuoustimemodelsandhighfre-
quencydata).ThetextbookbyBrooks(2019),withafirsteditionpublishedin2002,
deals with econometrics at an introductory level, targeted to finance students, em-
phasizing linear regression and time-series models (including event studies). More
recently,Linton(2019)providesanoverviewofeconometricmodelsandmethodsin
finance,againwithafocusonfinancialmarketsandinvestments.Cochrane(2005)
presentsasystematicoverviewoftheassetpricingliterature,withanemphasison
empirical methodology. Econometric textbooks with a clear relevance for financial
work,particularlyinthecorporatefinancearea,areCameronandTrivedi(2005)and
Wooldridge(2010),bothpayingelaborateattentiontomicroeconometricissuesand
paneldata.Forcausalinference,themonographsofAngristandPischke(2009)and
AngristandPischke(2015)provideattractivesourcesformanyscholars.
DedicatedtextbooksonpaneldataareArellano(2003),Baltagi(2013)andHsiao
(2014),whilePesaran(2015)focussesbothontime-seriesandpaneldataeconomet-
rics.Despitetheseresources,thereisnotyetasystematictreatmentofeconometric
techniquesbasedonpaneldatainempiricalfinance.Tosomeextent,thisgapisfilled
withcomprehensivepapersfocussingonspecificissues,forexample,Petersen(2009)
ontheestimationofstandarderrorsinpaneldatasets,RobertsandWhited(2013)on
endogeneityproblemsincorporatefinance,andGormleyandMatsa(2014)andGrieser
andHadlock(2019)ontheestimationofmodelswithfixedeffects(FE).
My goal with this monograph was to provide an intuitive and relatively non-
technicaloverviewofeconometricapproachesexploitingpaneldata.Keytopicsare
alternative assumptions regarding exogeneity on explanatory variables and their
consequencesformodelestimation,theinclusionofavarietyoffixedeffectstocon-
trolforunobservabledifferencesacrossunits,theincidentalparametersproblemin
nonlinear models, the use of clustered-standard errors as a means of making stan-
dard (cross-sectional) estimation methods “panel robust”, the problem of having
a lagged dependent variable, and the estimation of – potentially heterogeneous –
https://doi.org/10.1515/9783110660739-201
VIII | Preface
treatmenteffects.Furtherattentionisgiventooutliers,measurementerror,missing
data and other data problems. Along the way, references are given to a variety of
empiricalpapersinfinanceasanillustrationoftheemployedmethods.Intermsof
methods, I discuss pooled Ordinary Least Squares (OLS), within and between esti-
mators,feasibleGeneralisedLeastSquares(GLS),Fama-MacBethregressions,robust
and clustered standard errors, instrumental variables (IV), the generalised method
ofmoments(GMM),(conditional)maximumlikelihood(ML),regressiondiscontinu-
itydesign(RDD),difference-in-differencesmethods(DiD),matchingandweighting.
Apartfromlinearmodelswithorwithoutdynamics,Idiscussmodelsforbinaryde-
pendent variables, such as logit and probit, models with censoring or truncation,
durationmodelsandcountmodels.
Giventhelengthandscopeofthisbook,Ihadtomakemanychoicesofwhattodis-
cuss.Tocompensatethis,Ihaveincludedawiderangeofreferencestoothersources
thatgodeeperintocertainmethodsorissues.Thisisparticularlytruefortheareaof
theestimationoftreatmenteffects,wherenewpapersarecomingoutalmostevery
day.Ihopethereadersappreciatetheattempttocollectanddiscussawidevariety
ofmethodsandapproaches,atareasonableintuitivelevelandasmuchaspossible
inuniformnotation.Almostallmethodsdiscussedinthistextarereadilyavailable
inStata,occasionallyafterinstallationofuser-writtenroutines.Forconvenience,spe-
cificdetailsonrelevantStatafunctionsareprovidedwhenappropriate.Averyuseful
anddetailedguidetomicroeconometricswithStataisCameronandTrivedi(2021).
Rotterdam,TheNetherlands MarnoVerbeek
July27,2021
Acknowledgments
I wish to thank my colleagues at the finance department of Rotterdam School of
Management,ErasmusUniversity,forsupportandinspiration.Particularthanksgo
to Rómulo Alves, Mathijs van Dijk, Gelly Fu, Francesco Mazzola, Mikael Paaso and
GuosongXuforhelpfulcommentsandsuggestions.Iamalsogratefulto#econtwitter,
whichhasrepeatedlyalertedmetorecentdevelopmentsanddiscussions.Ialsothank
SteveHardmanforhiscontinuousconfidenceandprofessionalsupportoverthepast
22years,andthepeopleatDeGruyter,whohavethecouragetostartnewbookprojects
inthisturbulentageforacademicpublishers.Finally,mywifeMarcellahasbeenin-
crediblysupportivewithherloveandpatience,alsointhepresenceofCOVID-19.
M.V.
https://doi.org/10.1515/9783110660739-202
