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Chapter4. Datafiles
20
importingfromplaintext,theprogramoffers limitedhandlingofcharacter(string)data: : if
agivencolumncontainscharacter dataonly, consecutivenumericcodesaresubstitutedfor
thestrings, andoncetheimportiscompleteatableisprintedshowingthecorrespondence
betweenthestringsandthecodes.
 Dates(orobservationlabels): : Optionally,thefirst columnmaycontainstringssuchasdates,
orlabelsforcross-sectionalobservations.Suchstringshaveamaximumof15characters(as
withvariablenames,longerstringswillbetruncated).Acolumnofthissortshouldbeheaded
withthestringobsordate,orthefirstrowentrymaybeleftblank.
Fordatestoberecognizedassuch,thedatestringsshouldadheretooneorotherofasetof
specificformats,asfollows. Forannualdata:4-digityears. Forquarterlydata:a4-digityear,
followedbyaseparator(eitheraperiod,acolon,ortheletterQ),followedbya1-digitquarter.
Examples:1997.1,2002:3,1947Q1.Formonthlydata:a4-digityear,followedbyaperiodor
acolon,followedbyatwo-digitmonth.Examples:1997.01,2002:10.
Plaintext(“CSV”)filescanusecomma,space,taborsemicolonasthecolumnseparator.Whenyou
opensuchafileviatheGUIyouaregiventheoptionofspecifyingtheseparator,thoughinmost
casesitshouldbedetectedautomatically.
Ifyouuseaspreadsheettoprepareyourdatayouareabletocarryoutvarioustransformationsof
the“raw”datawithease(addingthingsup, takingpercentagesorwhatever): : note,however, , that
youcanalso do thissortof thing easily—perhaps moreeasily—withingretl, byusingthetools
underthe“Add”menu.
Appendingimporteddata
Youmaywishtoestablishadatasetpiecebypiece,byincrementalimportationofdatafromother
sources.Thisissupportedviathe“File,Appenddata”menuitems:gretlwillcheckthenewdatafor
conformabilitywiththeexistingdatasetand,ifeverythingseemsOK,willmergethedata.Youcan
addnewvariablesinthisway,providedthedatafrequencymatchesthatoftheexistingdataset.Or
youcanappendnewobservationsfordataseriesthatarealreadypresent;inthiscasethevariable
names mustmatchupcorrectly. . Notethatbydefault(thatis, , ifyouchoose“Opendata”rather
than“Appenddata”),openinganewdatafileclosesthecurrentone.
Usingthebuilt-inspreadsheet
Underthe“File,Newdataset”menuyoucanchoosethesortofdatasetyouwanttoestablish(e.g.
quarterlytimeseries,cross-sectional).Youwillthenbepromptedforstartingandendingdates(or
observationnumbers)andthenameofthefirstvariabletoaddtothedataset.Aftersupplyingthis
informationyouwillbefacedwithasimplespreadsheetinto whichyoucantypedatavalues. . In
thespreadsheetwindow,clickingtherightmousebuttonwillinvokeapopupmenuwhichenables
youtoaddanewvariable(column),toaddanobservation(appendarowatthefootofthesheet),
ortoinsertanobservationattheselectedpoint(movethedatadownandinsertablankrow.)
Onceyouhaveentereddataintothespreadsheetyouimporttheseintogretl’sworkspaceusingthe
spreadsheet’s“Applychanges”button.
Pleasenotethatgretl’sspreadsheetisquitebasic andhasno supportfor functions or formulas.
Datatransformationsaredoneviathe“Add”or“Variable”menusinthemainwindow.
Selectingfromadatabase
Anotheralternativeistoestablishyourdatasetbyselectingvariablesfromadatabase.
Beginwiththe“File,Databases”menuitem.Thishasfourforks:“Gretlnative”,“RATS4”,“PcGive”
and“Ondatabaseserver”. Youshouldbeabletofindthefilefedstl.bininthefileselectorthat
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Chapter4. Datafiles
21
opensifyouchoosethe“Gretlnative”optionsincethisfile,whichcontainsalargecollectionofUS
macroeconomictimeseries,issuppliedwiththedistribution.
Youwon’tfindanythingunder“RATS4”unlessyouhavepurchasedRATSdata.
2
Ifyoudopossess
RATSdatayoushouldgo intothe“Tools, Preferences, General”dialog, selecttheDatabases tab,
andfillinthecorrectpathtoyourRATSfiles.
Ifyour computerisconnectedto theinternetyoushouldfindseveraldatabases (atWakeForest
University)under“Ondatabaseserver”. Youcanbrowsetheseremotely;youalsohavetheoption
of installing them onto your owncomputer. . Theinitialremote e databases s windowhas an item
showing,foreachfile,whetheritisalreadyinstalledlocally(andifso,ifthelocalversionisupto
datewiththeversionatWakeForest).
Assumingyouhavemanagedtoopenadatabaseyoucanimportselectedseriesintogretl’sworkspace
byusingthe“Series,Import”menuiteminthedatabasewindow,or viathepopupmenuthatap-
pearsifyouclicktherightmousebutton,orbydraggingtheseriesintotheprogram’smainwindow.
Creatingagretldatafileindependently
Itis possible e to o create e adata filein oneor r other of gretl’s own n formats using atext editor r or
softwaretools suchas awk, sedor perl. . This s maybeagoodchoice e ifyouhavelargeamounts
ofdataalreadyinmachinereadableform. Youwill, , ofcourse,needto studythesedataformats
(XML-basedor“traditional”)asdescribedinAppendixA.
4.4 Structuringadataset
Onceyourdataarereadbygretl,itmaybenecessarytosupplysomeinformationonthenatureof
thedata.Wedistinguishbetweenthreekindsofdatasets:
1. Crosssection
2. Timeseries
3. Paneldata
Theprimarytoolfordoingthisisthe“Data,Datasetstructure”menuentryinthegraphicalinter-
face,orthesetobscommandforscriptsandthecommand-lineinterface.
Crosssectionaldata
Byacrosssectionwemeanobservationsonasetof“units”(whichmaybefirms,countries,indi-
viduals,orwhatever)atacommonpointintime. Thisisthedefaultinterpretationforadatafile:
ifthereisinsufficientinformationtointerpretdataastime-seriesorpaneldata,theyareautomat-
icallyinterpretedas across section. . Intheunlikelyeventthatcross-sectional l data arewrongly
interpretedas timeseries, youcancorrect thisbyselecting the“Data, Datasetstructure”menu
item. Clickthe“cross-sectional”radiobuttoninthedialogboxthatappears,thenclick“Forward”.
Click“OK”toconfirmyourselection.
Timeseriesdata
Whenyouimportdatafromaspreadsheetorplaintextfile,gretlwillmakefairlystrenuousefforts
togleantime-seriesinformationfromthefirstcolumnofthedata,ifitlooksatallplausiblethat
suchinformationmaybepresent.Iftime-seriesstructureispresentbutnotrecognized,againyou
canusethe“Data,Datasetstructure”menuitem.Select“Timeseries”andclick“Forward”;selectthe
appropriatedatafrequencyandclick“Forward”again;thenselectorenterthestartingobservation
2
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Chapter4. Datafiles
22
andclick“Forward”oncemore. Finally,click“OK”toconfirmthetime-seriesinterpretationifitis
correct(orclick“Back”tomakeadjustmentsifneedbe).
Besidesthebasicbusinessofgettingadatasetinterpretedastimeseries,furtherissuesmayarise
relating to thefrequency of time-series data. . Ina a gretltime-series s dataset, alltheseries must
havethesamefrequency.Supposeyouwishtomakeacombineddatasetusingseriesthat,intheir
originalstate,arenotallofthesamefrequency. Forexample,someseriesaremonthlyandsome
arequarterly.
Yourfirststepistoformulateastrategy:Doyouwanttoendupwithaquarterlyoramonthlydata
set?Abasicpointtonotehereisthat“compacting”datafromahigherfrequency(e.g.monthly)to
alowerfrequency(e.g.quarterly)isusuallyunproblematic. Youloseinformationindoingso,but
ingeneralitisperfectlylegitimatetotake(say)theaverageofthreemonthlyobservationstocreate
aquarterlyobservation.Ontheotherhand,“expanding”datafromalowertoahigherfrequencyis
not,ingeneral,avalidoperation.
Inmostcases,then,thebeststrategyistostartbycreatingadatasetofthelowerfrequency,and
thentocompactthehigherfrequencydatatomatch.Whenyouimporthigher-frequencydatafrom
adatabaseintothecurrentdataset,youaregivenachoiceofcompactionmethod(average,sum,
startofperiod,orendofperiod).Inmostinstances“average”islikelytobeappropriate.
Youcanalsoimportlower-frequencydataintoahigh-frequencydataset,butthisisgenerallynot
recommended. Whatgretldoesinthiscaseissimplyreplicatethevaluesofthelower-frequency
seriesasmanytimesasrequired. Forexample,supposewehaveaquarterlyserieswiththevalue
35.5in1990:1,thefirstquarterof1990. Onexpansiontomonthly,thevalue35.5willbeassigned
totheobservationsforJanuary,FebruaryandMarchof1990. Theexpandedvariableistherefore
uselessfor fine-grainedtime-seriesanalysis,outsideofthespecialcasewhereyouknowthatthe
variableinquestiondoesinfactremainconstantoverthesub-periods.
When the currentdata frequency is appropriate, gretl offers both “Compact data”and“Expand
data”optionsunderthe“Data”menu. Theseoptionsoperateonthewholedataset,compactingor
exandingallseries.Theyshouldbeconsidered“expert”optionsandshouldbeusedwithcaution.
Paneldata
Paneldataareinherentlythreedimensional—thedimensionsbeingvariable, cross-sectionalunit,
andtime-period. Forexample,aparticularnumberinapaneldatasetmightbeidentifiedasthe
observation on capital stock k for General l Motors in 1980. . (A A note on n terminology: : we e use the
terms“cross-sectionalunit”,“unit”and“group”interchangeablybelowtorefertotheentitiesthat
composethecross-sectionaldimensionofthepanel.Thesemight,forinstance,befirms,countries
orpersons.)
For representationinatextualcomputerfile(andalsofor gretl’s internalcalculations)thethree
dimensions mustsomehowbeflattened intotwo. . This s “flattening”involves takinglayers of the
datathatwouldnaturallystackinathirddimension,andstackingthemintheverticaldimension.
gretlalwaysexpectsdatatobearranged“byobservation”,thatis,suchthateachrowrepresentsan
observation(andeachvariableoccupiesoneandonlyonecolumn).Inthiscontexttheflatteningof
apaneldatasetcanbedoneineitheroftwoways:
 Stacked d timeseries: : thesuccessiveverticalblocks s eachcompriseatimeseries for agiven
unit.
 Stacked d cross sections: : thesuccessive e vertical blocks s each comprise e across-sectionfor a
givenperiod.
Youmayinputdatainwhicheverarrangementismoreconvenient.Internally,however,gretlalways
storespaneldataintheformofstackedtimeseries.
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Chapter4. Datafiles
23
4.5 Paneldataspecifics
Whenyouimportpaneldataintogretlfromaspreadsheetorcommaseparatedformat,thepanel
natureof thedatawill not berecognizedautomatically(most likelythe datawill be treated as
“undated”).Apanelinterpretationcanbeimposedonthedatausingthegraphicalinterfaceorvia
thesetobscommand.
Inthe graphical interface, usethe menu item “Data, , Datasetstructure”. . In n thefirst dialog box
thatappears,select“Panel”. Inthenextdialogyouhaveathree-waychoice. Thefirsttwooptions,
“Stackedtimeseries”and“Stackedcrosssections”areapplicableifthedatasetisalreadyorganized
inoneofthesetwoways.Ifyouselecteitheroftheseoptions,thenextstepistospecifythenumber
ofcross-sectionalunitsinthedataset. Thethirdoption,“Useindexvariables”,isapplicableifthe
datasetcontainstwovariablesthatindextheunitsandthetimeperiodsrespectively;thenextstep
isthentoselectthosevariables.Forexample,adatafilemightcontainacountrycodevariableand
avariablerepresentingtheyear oftheobservation. . In n that casegretlcanreconstructthepanel
structureofthedataregardlessofhowtheobservationrowsareorganized.
Thesetobscommandhasoptionsthatparallelthoseinthegraphicalinterface. Ifsuitableindex
variablesareavailableyoucando,forexample
setobs unitvar timevar --panel-vars
whereunitvarisavariablethatindexestheunitsandtimevarisavariableindexingtheperiods.
Alternativelyyoucanusetheformsetobsfreq1:1structure,wherefreqisreplacedbythe“block
size”ofthedata(thatis,thenumberofperiodsinthecaseofstackedtimeseries,orthenumber
ofunitsinthecaseofstackedcross-sections)andstructureiseither--stacked-time-seriesor
--stacked-cross-section. Two o examples are e given below: : thefirst t is suitablefor r apanel l in
theformofstackedtimeseries withobservationsfrom20periods;thesecondforstackedcross
sectionswith5units.
setobs 20 1:1 --stacked-time-series
setobs 5 5 1:1 1 --stacked-cross-section
Paneldataarrangedbyvariable
Publiclyavailablepaneldatasometimescomearranged“byvariable.”Supposewehavedataontwo
variables, x1andx2, for each of50states ineachof 5years (givingatotalof250observations
per variable). . Onetextualrepresentationofsuchadatasetwouldstartwithablockforx1, , with
50rows corresponding tothestates and5columns correspondingto theyears. . This s wouldbe
followed,vertically,byablockwiththesamestructureforvariablex2. Afragmentofsuchadata
fileisshownbelow, withquinquennialobservations1965–1985. . Imaginethetablecontinuedfor
48morestates,followedbyanother50rowsforvariablex2.
x1
1965
1970
1975
1980
1985
AR
100.0
110.5
118.7
131.2
160.4
AZ
100.0
104.3
113.8
120.9
140.6
Ifadatafilewiththissortofstructureisreadintogretl,
3
theprogramwillinterpretthecolumnsas
distinctvariables,sothedatawillnotbeusable“asis.”Butthereisamechanismforcorrectingthe
situation,namelythestackfunctionwithinthegenrcommand.
Considerthefirstdatacolumninthefragmentabove:thefirst50rowsofthiscolumnconstitutea
cross-sectionforthevariablex1intheyear1965.Ifwecouldcreateanewvariablebystackingthe
3
Notethatyouwillhavetomodifysuchadatafileslightlybeforeitcanbereadatall.Thelinecontainingthevariable
name(inthisexamplex1)willhavetoberemoved,andsowilltheinitialrowcontainingtheyears,otherwisetheywillbe
takenasnumericaldata.
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Chapter4. Datafiles
24
first50entriesinthesecondcolumnunderneaththefirst50entriesinthefirst,wewouldbeonthe
waytomakingadataset“byobservation”(inthefirstofthetwoformsmentionedabove,stacked
cross-sections).Thatis,we’dhaveacolumncomprisingacross-sectionforx1in1965,followedby
across-sectionforthesamevariablein1970.
Thefollowinggretlscriptillustrateshowwecanaccomplishthestacking,forbothx1andx2. We
assumethattheoriginaldatafileiscalledpanel.txt,andthatinthisfilethecolumnsareheaded
with“variablenames”v1,v2,...,v5.(Thecolumnsarenotreallyvariables,butinthefirstinstance
we“pretend”thattheyare.)
open panel.txt
genr x1 1 = = stack(v1..v5) --length=50
genr x2 2 = = stack(v1..v5) --offset=50 --length=50
setobs 50 1:1 --stacked-cross-section
store panel.gdt x1 1 x2
Thesecondlineillustratesthesyntaxofthestackfunction.Thedoubledotswithintheparenthe-
sesindicatearangeofvariablestobestacked:herewewanttostackall5columns(forall5years).4
Thefulldatasetcontains100rows;inthestackingofvariablex1wewishtoreadonlythefirst50
rowsfromeachcolumn:weachievethisbyadding--length=50. Notethatifyouwanttostacka
non-contiguoussetofcolumnsyoucangiveacomma-separatedlistofvariablenames,asin
genr x = = stack(v1,v3,v5)
oryoucanprovidewithintheparenthesesthenameofapreviouslycreatedlist(seechapter14).
Online3wedothestackingforvariablex2.Againwewantalengthof50forthecomponentsof
thestackedseries,butthistimewewantgretltostartreadingfromthe50throwoftheoriginal
data,andwespecify--offset=50. Line4imposesapanelinterpretationonthedata; ; finally,we
savethedataingretlformat,withthepanelinterpretation,discardingtheoriginal“variables”v1
throughv5.
Theillustrativescriptaboveisappropriatewhenthenumberofvariabletobeprocessedissmall.
Whenthenaremanyvariablesinthedatasetitwillbemoreefficientto useacommandloopto
accomplishthestacking,asshowninthefollowingscript. Thesetupispresumedtobethesame
asintheprevioussection(50units,5periods),butwith20variablesratherthan2.
open panel.txt
loop i=1..20
genr k k = = ($i - 1) ) * * 50
genr x$i = stack(v1..v5) --offset=k --length=50
endloop
setobs 50 1.01 --stacked-cross-section
store panel.gdt x1 1 x2 2 x3 x4 x5 5 x6 6 x7 x8 x9 x10 \
x11 x12 x13 x14 x15 x16 x17 7 x18 8 x19 x20
Paneldatamarkerstrings
Itcanbehelpful withpaneldata to o havetheobservations identified by y mnemonic markers. . A
specialfunctioninthegenrcommandisavailableforthispurpose.
Inthe example above, , supposeall thestates are e identified by two-letter codes s inthe e left-most
column ofthe originaldatafile. . Whenthestacking g operation is performed, , thesecodes s will be
stackedalongwiththedatavalues. IfthefirstrowismarkedARforArkansas,thenthemarkerAR
willendupbeingshownoneachrowcontaininganobservationforArkansas.That’sallverywell,
butthesemarkersdon’ttellusanythingaboutthedateoftheobservation.Torectifythiswecould
do:
4
You can also specifya listof seriesusingthe wildcard ‘*’;for example stack(p*)would stack allseries whose
namesbeginwith‘v’.
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Chapter4. Datafiles
25
genr time
genr year = 1960 + + (5 5 * time)
genr markers = "%s:%d", marker, year
Thefirstlinegeneratesa1-basedindexrepresentingtheperiodofeachobservation,andthesecond
lineuses thetimevariableto generateavariablerepresentingtheyear oftheobservation. . The
thirdlinecontainsthisspecialfeature:if(andonlyif)thenameofthenew“variable”togenerateis
markers,theportionofthecommandfollowingtheequalssignistakenasaC-styleformatstring
(which must be wrapped in double quotes), , followed d by a comma-separated list of f arguments.
Thearguments willbeprintedaccordingto thegiven formatto createanewsetofobservation
markers. Validarguments s areeither thenamesofvariablesinthedataset,orthestringmarker
whichdenotesthepre-existing observationmarker. . Theformatspecifiers s whicharelikelyto be
useful in this context are %s for r a a string g and d %d for r an integer. . Strings s can betruncated: : for
example%.3swillusejustthefirstthreecharacters ofthestring. . Tochop p initialcharactersoff
anexistingobservationmarkerwhenconstructinganewone,youcanusethesyntaxmarker + n,
wherenisapositiveinteger:inthecasethefirstncharacterswillbeskipped.
Afterthecommandsaboveareprocessed,then,theobservationmarkerswilllooklike,forexample,
AR:1965,wherethetwo-letterstatecodeandtheyearoftheobservationaresplicedtogetherwith
acolon.
Paneldummyvariables
Inapanelstudyyoumaywishtoconstructdummyvariablesofoneorbothofthefollowingsorts:
(a)dummiesasuniqueidentifiersfortheunitsorgroups,and(b)dummiesasuniqueidentifiersfor
thetimeperiods. Theformermaybeusedtoallowtheinterceptoftheregressiontodifferacross
theunits,thelattertoallowtheintercepttodifferacrossperiods.
Two special l functions areavailable to createsuch dummies. . Thesearefoundunder r the“Add”
menuintheGUI,orunderthegenrcommandinscriptmodeorgretlcli.
1. “unit t dummies”(script command genr r unitdum). . This s command creates asetofdummy
variables identifying thecross-sectionalunits. . Thevariabledu_1 1 willhavevalue1ineach
rowcorrespondingtoaunit1observation,0otherwise; du_2willhavevalue1ineachrow
correspondingtoaunit2observation,0otherwise;andsoon.
2. “timedummies”(scriptcommand d genr r timedum). . Thiscommandcreatesaset t ofdummy
variablesidentifyingtheperiods.Thevariabledt_1willhavevalue1ineachrowcorrespond-
ingtoaperiod1observation,0otherwise;dt_2willhavevalue1ineachrowcorresponding
toaperiod2observation,0otherwise;andsoon.
IfapaneldatasethastheYEARoftheobservationenteredasoneofthevariablesyoucancreatea
periodicdummytopickoutaparticularyear,e.g.genr dum = = (YEAR=1960). . Youcanalsocreate
periodic dummyvariablesusingthemodulusoperator, %. . Forinstance, , to createadummywith
value1forthefirstobservationandeverythirtiethobservationthereafter,0otherwise,do
genr index
genr dum m = = ((index-1) % 30) = = 0
Lags,differences,trends
Ifthetimeperiodsareevenlyspacedyoumaywanttouselagged valuesofvariablesinapanel
regression(butseealsochapter19);youmayalsowishtoconstructfirstdifferencesofvariablesof
interest.
Onceadatasetisidentifiedasapanel,gretlwillhandlethegenerationofsuchvariablescorrectly.
Forexamplethecommandgenr x1_1 = = x1(-1)willcreateavariablethatcontainsthefirstlag
Chapter4. Datafiles
26
ofx1whereavailable,andthemissingvaluecodewherethelagisnotavailable(e.g.atthestartof
thetimeseriesforeachgroup). Whenyourunaregressionusingsuchvariables,theprogramwill
automaticallyskipthemissingobservations.
Whenapaneldatasethasafairlysubstantialtimedimension,youmaywishtoincludeatrendin
theanalysis.Thecommandgenr timecreatesavariablenamedtimewhichrunsfrom1tofor
eachunit,whereisthelengthofthetime-seriesdimensionofthepanel.Ifyouwanttocreatean
indexthatrunsconsecutivelyfrom1tomT,wheremisthenumberofunitsinthepanel,use
genr index.
Basicstatisticsbyunit
gretlcontainsfunctionswhichcanbeusedtogeneratebasicdescriptivestatisticsforagivenvari-
able,onaper-unitbasis;thesearepnobs()(numberofvalidcases),pmin()andpmax()(minimum
andmaximum)andpmean()andpsd()(meanandstandarddeviation).
Asabriefillustration,supposewehaveapaneldatasetcomprising8time-seriesobservationson
eachofNunitsorgroups.Thenthecommand
genr pmx x = = pmean(x)
createsaseriesofthisform: thefirst8values(correspondingtounit1)containthemeanofxfor
unit1,thenext8valuescontainthemeanforunit2, andsoon. . Thepsd()functionworksina
similarmanner.Thesamplestandarddeviationforgroupiiscomputedas
s
i
s
P
x ¯x
i
2
T
i
1
whereT
i
denotesthenumberofvalidobservationsonxforthegivenunit, ¯x
i
denotesthegroup
mean, and d thesummation is across valid observations for the group. . If T
i
2, however, the
standarddeviationisrecordedas0.
Oneparticularuseofpsd()maybeworthnoting.Ifyouwanttoformasub-sampleofapanelthat
containsonlythoseunitsforwhichthevariablexistime-varying,youcaneitheruse
smpl (pmin(x) < pmax(x)) --restrict
or
smpl (psd(x) > 0) --restrict
4.6 Missingdatavalues
Representationandhandling
MissingvaluesarerepresentedinternallyasDBL_MAX,thelargestfloating-pointnumberthatcanbe
representedonthesystem(whichis likelyto beatleast10tothepower300, andsoshouldnot
beconfusedwithlegitimatedatavalues). Inanative-formatdatafiletheyshouldberepresented
asNA.WhenimportingCSVdatagretlacceptsseveralcommonrepresentationsofmissingvalues
including 999,thestringNA(inupperorlowercase),asingledot,orsimplyablankcell.Blankcells
should,ofcourse,beproperlydelimited,e.g.120.6,,5.38,inwhichthemiddlevalueispresumed
missing.
Asforhandlingofmissingvaluesinthecourseofstatisticalanalysis,gretldoesthefollowing:
 Incalculatingdescriptivestatistics(mean,standarddeviation,etc.) ) underthesummarycom-
mand,missingvaluesaresimplyskippedandthesamplesizeadjustedappropriately.
Chapter4. Datafiles
27
 Inrunning g regressionsgretl firstadjusts thebeginningandendofthesamplerange, trun-
catingthesampleifneedbe. Missingvaluesatthebeginningofthesamplearecommonin
timeseriesworkduetotheinclusionoflags,firstdifferencesandsoon;missingvaluesatthe
endoftherangearenotuncommondueto differentialupdatingofseriesandpossiblythe
inclusionofleads.
Ifgretldetectsanymissingvalues“inside”the(possiblytruncated)samplerangeforaregression,
theresultdependsonthecharacter ofthedatasetandtheestimator chosen. . Inmanycases, , the
programwillautomaticallyskipthemissingobservationswhencalculatingtheregressionresults.
Inthissituationamessageisprintedstatinghowmanyobservationsweredropped. Ontheother
hand,theskippingofmissingobservationsisnotsupportedforallprocedures:exceptionsinclude
allautoregressiveestimators,systemestimatorssuchasSUR,andnonlinearleastsquares. Inthe
caseofpaneldata,theskippingofmissingobservationsissupportedonlyiftheiromissionleaves
abalancedpanel. Ifmissingobservationsarefoundincaseswheretheyarenotsupported,gretl
givesanerrormessageandrefusestoproduceestimates.
Manipulatingmissingvalues
Some special functions s are e availablefor r the e handling of missing values. . Theboolean n function
missing()takesthenameofavariableasitssingleargument;itreturnsaserieswithvalue1for
eachobservationatwhichthegivenvariablehasamissingvalue,andvalue0otherwise(thatis,if
thegivenvariablehas avalidvalueatthatobservation). . Thefunctionok()iscomplementaryto
missing;itisjustashorthandfor!missing(where!isthebooleanNOToperator). Forexample,
onecancountthemissingvaluesforvariablexusing
scalar nmiss_x = sum(missing(x))
Thefunctionzeromiss(),whichagaintakesasingleseriesasitsargument,returnsaserieswhere
allzerovaluesaresettothemissingcode. Thisshouldbeusedwithcaution—onedoesnotwant
toconfusemissingvaluesandzeros—butitcanbeusefulinsomecontexts.Forexample,onecan
determinethefirstvalidobservationforavariablexusing
genr time
scalar x0 = min(zeromiss(time e * * ok(x)))
Thefunctionmisszero()doestheoppositeofzeromiss,thatis,itconvertsallmissingvaluesto
zero.
Itmay be worthcommenting on thepropagationofmissing values withingenr r formulae. . The
generalruleisthatinarithmeticaloperationsinvolvingtwovariables,ifeitherofthevariableshas
amissingvalueatobservationthentheresultingserieswillalsohaveamissingvalueatt. The
oneexceptiontothisruleismultiplicationbyzero:zerotimesamissingvalueproduceszero(since
thisismathematicallyvalidregardlessoftheunknownvalue).
4.7 Maximumsizeofdatasets
Basically,thesizeofdatasets(boththenumberofvariablesandthenumberofobservationsper
variable)is limitedonlybythecharacteristicsofyourcomputer. . gretlallocatesmemorydynami-
cally,andwillasktheoperatingsystemforasmuchmemoryasyourdatarequire.Obviously,then,
youareultimatelylimitedbythesizeofRAM.
Asidefromthemultiple-precisionOLSoption,gretlusesdouble-precisionfloating-pointnumbers
throughout.Thesizeofsuchnumbersinbytesdependsonthecomputerplatform,butistypically
eight. Togivearoughnotionofmagnitudes,supposewehaveadatasetwith10,000observations
on 500variables. . That’s s 5millionfloating-pointnumbers or 40million bytes. . Ifwe e definethe
Chapter4. Datafiles
28
megabyte(MB)as10241024bytes,asisstandardintalkingaboutRAM,it’sslightlyover38MB.
Theprogramneedsadditionalmemoryforworkspace,butevenso,handlingadatasetofthissize
shouldbequitefeasibleonacurrentPC,whichatthetimeofwritingislikelytohaveatleast256
MBofRAM.
IfRAMis notanissue, thereis onefurther limitation on datasize(thoughit’s veryunlikely to
beabindingconstraint). Thatis,variablesandobservationsareindexedbysignedintegers,and
onatypicalPCthesewillbe32-bitvalues, capableofrepresentingamaximumpositivevalueof
231 12;147;483;647.
Thelimitsmentionedaboveapply togretl’s “native”functionality. . Therearetighter r limits with
regardtotwothird-partyprogramsthatareavailableasadd-onstogretlforcertainsortsoftime-
seriesanalysisincludingseasonaladjustment,namelyTRAMO/SEATSandX-12-ARIMA.Thesepro-
gramsemployafixed-sizememoryallocation,andcan’thandleseriesofmorethan600observa-
tions.
4.8 Datafilecollections
Ifyou’reusinggretlinateachingcontextyoumaybeinterestedinaddingacollectionofdatafiles
and/orscriptsthatrelatespecificallytoyourcourse,insuchawaythatstudentscanbrowseand
accessthemeasily.
Therearethreewaystoaccesssuchcollectionsoffiles:
 Fordatafiles: : selectthemenuitem“File,Opendata,Samplefile”,orclickonthefoldericon
onthegretltoolbar.
 Forscriptfiles:selectthemenuitem“File,Scriptfiles,Practicefile”.
Whenauserselectsoneoftheitems:
 Thedataorscriptfilesincludedinthegretldistributionareautomaticallyshown(thisincludes
filesrelatingtoRamanathan’sIntroductoryEconometricsandGreene’sEconometricAnalysis).
 Theprogram m looks for r certain n known collections ofdatafiles availableas optional extras,
for instance e the datafiles fromvarious s econometrics textbooks (Davidson and MacKinnon,
Gujarati, StockandWatson,Verbeek,Wooldridge)andthePennWorldTable(PWT5.6). . (See
thedatapageatthegretlwebsiteforinformationonthesecollections.)Iftheadditionalfiles
arefound,theyareaddedtotheselectionwindows.
 Theprogram m then searches for r validfile e collections s (notnecessarilyknown inadvance) in
theseplaces: the“system” ” data directory, the system script directory, , the e user r directory,
andallfirst-levelsubdirectoriesofthese. Forreference,typicalvaluesforthesedirectories
areshowninTable4.1. (NotethatPERSONALisaplaceholderthatisexpandedbyWindows,
correspondingto“MyDocuments”onEnglish-languagesystems.)
Linux
MSWindows
systemdatadir
/usr/share/gretl/data
c:\Program Files\gretl\data
systemscriptdir
/usr/share/gretl/scripts
c:\Program Files\gretl\scripts
userdir
$HOME/gretl
PERSONAL\gretl
Table4.1:Typicallocationsforfilecollections
Anyvalidcollectionswillbeaddedtotheselectionwindows.Sowhatconstitutesavalidfilecollec-
tion?ThiscompriseseitherasetofdatafilesingretlXMLformat(withthe.gdtsuffix)orasetof
Chapter4. Datafiles
29
scriptfilescontaininggretlcommands(with.inpsuffix),ineachcaseaccompaniedbya“master
file”orcatalog. Thegretldistributioncontains s severalexamplecatalogfiles,forinstancethefile
descriptionsinthemiscsub-directoryofthegretldatadirectoryandps_descriptionsinthe
miscsub-directoryofthescriptsdirectory.
Ifyouareaddingyour owncollection, datacatalogs shouldbenameddescriptions andscript
catalogsshouldbebenamedps_descriptions. Ineachcasethecatalogshouldbeplaced(along
withtheassociateddataorscriptfiles)initsownspecificsub-directory(e.g./usr/share/gretl/
data/mydataorc:\userdata\gretl\data\mydata).
Thecatalogfilesareplaintext;iftheycontainnon-ASCIIcharacterstheymustbeencodedasUTF-
8. Thesyntaxofsuchfilesisstraightforward. . Here,forexample,arethefirstfewlinesofgretl’s
“misc”datacatalog:
# Gretl: : various s illustrative e datafiles
"arma","artificial data a for ARMA script example"
"ects_nls","Nonlinear least squares example"
"hamilton","Prices and d exchange rate, U.S. and Italy"
The first line, which must start with a hash mark, contains s a a short name, , here “Gretl”, which
willappearasthelabelforthiscollection’stabinthedatabrowserwindow,followedbyacolon,
followedbyanoptionalshortdescriptionofthecollection.
Subsequentlinescontaintwo elements,separatedbyacommaandwrappedindoublequotation
marks. Thefirstis s adatafilename(leaveoff the.gdt suffix here)andthesecondisashortde-
scription of thecontentof thatdatafile. . Thereshould d be onesuchlinefor eachdatafilein the
collection.
Ascriptcatalogfilelooksverysimilar,exceptthattherearethreefieldsinthefilelines:afilename
(withoutits.inpsuffix),abriefdescriptionoftheeconometricpointillustratedinthescript,and
abriefindicationofthenatureofthedataused.Again,herearethefirstfewlinesofthesupplied
“misc”scriptcatalog:
# Gretl: : various s sample scripts
"arma","ARMA modeling","artificial data"
"ects_nls","Nonlinear least squares (Davidson)","artificial data"
"leverage","Influential observations","artificial data"
"longley","Multicollinearity","US employment"
Ifyouwanttomakeyourowndatacollectionavailabletousers,thesearethesteps:
1. Assemblethedata,inwhateverformatisconvenient.
2. Convertthedatatogretlformatandsaveasgdtfiles.Itisprobablyeasiesttoconvertthedata
byimportingthemintotheprogramfromplaintext,CSV,oraspreadsheetformat(MSExcel
orGnumeric)thensavingthem. Youmaywishtoadddescriptionsoftheindividualvariables
(the“Variable,Editattributes”menuitem),andaddinformationonthesourceofthedata(the
“Data,Editinfo”menuitem).
3. Writeadescriptionsfileforthecollectionusingatexteditor.
4. Putthedatafilesplusthedescriptionsfileinasubdirectoryofthegretldatadirectory(oruser
directory).
5. Ifthecollectionistobedistributedtootherpeople,packagethedatafilesandcataloginsome
suitablemanner,e.g.asazipfile.
Ifyouassemblesuchacollection, andthedataarenot proprietary, wewouldencourageyouto
submitthecollectionforpackagingasagretloptionalextra.
Documents you may be interested
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