Chapter27. CointegrationandVectorErrorCorrectionModels
250
Identificationandtestability
One pointregarding g VECMrestrictions that canbeconfusingatfirstis that identification (does
therestrictionidentifythesystem?) andtestability(istherestrictiontestable?)arequiteseparate
matters. Restrictions s canbeidentifyingbutnottestable;lessobviously,theycanbetestablebut
notidentifying.
Thiscanbeseenquiteeasilyinrelationto arank-1system. . Therestriction
1
1isidentifying
(itpins downthescaleof)but,beingapurescaling,itisnottestable. . Ontheotherhand, , the
restriction
1
2
0istestable—thesystemwiththisrequirementimposedwillalmostcertainly
havealowermaximizedlikelihood—butitisnotidentifying;itstillleavesopenthescaleof.
Wesaidabovethatthenumberofrestrictionsmustequalatleastr
2
,whereisthecointegrating
rank,foridentification.Thisisanecessaryandnotasufficientcondition.Infact,whenr >1itcan
bequitetrickytoassesswhetheragivensetofrestrictionsisidentifying. Gretlusesthemethod
suggestedbyDoornik(1995),whereidentificationisassessedviatherankoftheinformationma-
trix.
Itcanbeshownthatforrestrictionsofthesort(27.7)and(27.8)theinformationmatrixhas the
samerankastheJacobianmatrix
J
h
I
p
G: I
p
1
H
i
AsufficientconditionforidentificationisthattherankofJequalsthenumberoffreeparam-
eters. Therank k of this matrix x isevaluated byexaminationofits singular r values atarandomly
selectedpointintheparameterspace. Forpracticalpurposeswetreatthisconditionasifitwere
bothnecessaryandsufficient;thatis,wedisregardthespecialcaseswhereidentificationcouldbe
achievedwithoutthisconditionbeingmet.7
27.7 Numericalsolutionmethods
Ingeneral,theMLestimatorfortherestrictedVECMproblemhasnoclosed-formsolution,hence
themaximummustbefoundvianumericalmethods.
8
Insomecasesconvergencemaybedifficult,
andgretlprovidesseveralchoicestosolvetheproblem.
SwitchingandLBFGS
Twomaximizationmethodsareavailableingretl. Thedefaultistheswitchingalgorithmsetout
inBoswijkandDoornik(2004). Thealternativeisalimited-memoryvariantoftheBFGSalgorithm
(LBFGS), using analyticalderivatives. . Thisis s invokedusing the--lbfgs flag with therestrict
command.
Theswitchingalgorithmworksbyexplicitlymaximizingthelikelihoodateachiteration,withre-
spectto
ˆ
,
ˆ
and
ˆ
Ú(thecovariancematrixoftheresiduals)inturn. Thismethodsharesafeature
withthebasicJohanseneigenvaluesprocedure,namely,itcanhandleasetofrestrictionsthatdoes
notfullyidentifytheparameters.
LBFGS,ontheotherhand,requiresthatthemodelbefullyidentified.WhenusingLBFGS,therefore,
youmayhavetosupplementtherestrictionsofinterestwithnormalizationsthatservetoidentify
theparameters. Forexample,onemightuseallorpartofthePhillipsnormalization(seesection
27.5).
Neither theswitching g algorithm nor LBFGS is guaranteed to find theglobalML solution.The
7
SeeBoswijkandDoornik(2004),pp.447–8fordiscussionofthispoint.
8
Theexceptionisrestrictionsthatarehomogeneous,commontoallorall(incase r >1),and involveeither
onlyoronly.SuchrestrictionsarehandledviathemodifiedeigenvaluesmethodsetoutbyJohansen(1995).Wesolve
directlyfortheMLestimator,withoutanyneedforiterativemethods.
9
Indevelopinggretl’sVECM-testingfacilitieswehaveconsideredafairnumberof“trickycases”fromvarioussources.
We’d liketothankLucaFanellioftheUniversityofBolognaandSvenSchreiber ofGoetheUniversityFrankfurtfortheir
helpindevisingtorture-testsforgretl’sVECMcode.
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Chapter27. CointegrationandVectorErrorCorrectionModels
251
optimizermayendupatalocalmaximum(or,inthecaseoftheswitchingalgorithm,atasaddle
point).
Thesolution(orlackthereof)maybesensitivetotheinitialvalueselectedfor. Bydefault,gretl
selects astarting point using adeterministic method based on Boswijk (1995), buttwo o further
optionsareavailable:theinitializationmaybeadjustedusingsimulatedannealing,ortheusermay
supplyanexplicitinitialvaluefor.
Thedefaultinitializationmethodis:
1. CalculatetheunrestrictedML
ˆ
usingtheJohansenprocedure.
2. Iftherestrictiononisnon-homogeneous,usethemethodproposedbyBoswijk:
0
 I
r
ˆ
?
0
H
I
r
ˆ
?
0
h
0
(27.9)
where
ˆ
0
?
ˆ
0andA denotestheMoore–PenroseinverseofA.Otherwise
0
H
0
H
1
H
0
vec
ˆ

(27.10)
3. vec
0
H
0
h
0
.
4. CalculatetheunrestrictedML L ˆconditionalon
0
,asperJohansen:
ˆ
S
01
0

0
0
S
11
0
1
(27.11)
5. Ifisrestrictedbyvec
0
,then 
0
G
0
G
1
G
0
vecˆ
0
andvec
0
0
0
.
Alternativeinitializationmethods
Asmentionedabove,gretlofferstheoptionofadjustingtheinitializationusingsimulatedanneal-
ing.Thisisinvokedbyaddingthe--jitteroptiontotherestrictcommand.
Thebasicideaisthis:westartatacertainpointintheparameterspace,andforeachofniterations
(currentlyn4096)werandomlyselectanewpointwithinacertainradiusofthepreviousone,
anddeterminethelikelihoodatthenewpoint.Ifthelikelihoodishigher,wejumptothenewpoint;
otherwise,wejumpwithprobabilityP(andremainatthepreviouspointwithprobability1 P).As
theiterationsproceed,thesystemgradually“cools”—thatis,theradiusoftherandomperturbation
isreduced,asistheprobabilityofmakingajumpwhenthelikelihoodfailstoincrease.
Inthecourseofthisproceduremanypointsintheparameterspaceareevaluated,startingwiththe
pointarrivedatbythedeterministicmethod,whichwe’llcall
0
.Oneofthesepointswillbe“best”
inthesenseofyieldingthehighestlikelihood:callit
.Thispointmayormaynothaveagreater
likelihoodthan
0
.Andtheprocedurehasanendpoint,
n
,whichmayormaynotbe“best”.
Therulefollowedbygretlinselectinganinitialvalueforbasedonsimulatedannealingisthis:use
if
>
0
,otherwiseuse
n
.Thatis,ifwegetanimprovementinthelikelihoodviaannealing,
wemakefulluseofthis;ontheotherhand,ifwefailtogetanimprovementwenonethelessallow
theannealingto randomizethestartingpoint. . Experimentsindicatethatthelatter r effectcanbe
helpful.
Besidesannealing,afurtheralternativeismanualinitialization. Thisisdonebypassingaprede-
finedvectortothesetcommandwithparameterinitvals,asin
set initvals myvec
Thedetailsdependonwhethertheswitchingalgorithmor LBFGSisused. . Fortheswitchingalgo-
rithm,therearetwooptionsforspecifyingtheinitialvalues. Themoreuser-friendlyone(formost
people,wesuppose)istospecifyamatrixthatcontainsvecfollowedbyvec.Forexample:
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Chapter27. CointegrationandVectorErrorCorrectionModels
252
open denmark.gdt
vecm 2 1 1 LRM M LRY IBO IDE --rc c --seasonals
matrix BA = {1, -1, , 6, , -6, -6, , -0.2, , 0.1, , 0.02, , 0.03}
set initvals BA
restrict
b[1] = = 1
b[1] + + b[2] ] = 0
b[3] + + b[4] ] = 0
end restrict
In this s example—from Johansen (1995)—the cointegration rank k is 1and there e are 4variables.
However,themodelincludesarestrictedconstant(the--rcflag)sothathas5elements. The
matrixhas4elements,oneperequation.SothematrixBAmaybereadas

1
;
2
;
3
;
4
;
5
;
1
;
2
;
3
;
4
Theotheroption,whichiscompulsorywhenusingLBFGS,istospecifytheinitialvaluesinterms
ofthefreeparameters,and .Gettingthisrightissomewhatlessobvious.Asmentionedabove,
theimplicit-formrestrictionRvecqhasexplicitformvecHh
0
,whereHR
?
,the
right nullspace of R. Thevector is shorter, bythe e number r ofrestrictions, , than n vec. The
savvyuserwillthenseewhatneedstobedone. Theotherpointtotakeintoaccountisthatifis
unrestricted,theeffectivelengthof is0,sinceitisthenoptimaltocomputeusingJohansen’s
formula,conditionalon(equation27.11above).Theexampleabovecouldberewrittenas:
open denmark.gdt
vecm 2 1 1 LRM M LRY IBO IDE --rc c --seasonals
matrix phi = {-8, -6}
set initvals phi
restrict --lbfgs
b[1] = = 1
b[1] + + b[2] ] = 0
b[3] + + b[4] ] = 0
end restrict
Inthismoreeconomicalformulationtheinitializerspecifiesonlythetwofreeparametersin(5
elementsinminus3restrictions). Thereisnocalltogivevaluesfor sinceisunrestricted.
Scaleremoval
Considerasimplerversionoftherestrictiondiscussedintheprevioussection,namely,
restrict
b[1] = = 1
b[1] + + b[2] ] = 0
end restrict
Thisrestrictioncomprisesasubstantive,testablerequirement—that
1
and
2
sumtozero—and
anormalizationor scaling, 
1
1. Thequestionarises, , mightitbeeasierandmorereliableto
maximizethelikelihoodwithout imposing 
1
 1?
10
Ifso, wecould recordthis normalization,
removeitforthepurposeofmaximizingthelikelihood,thenreimposeitbyscalingtheresult.
Unfortunately itis notpossible to o sayinadvance e whether r “scaleremoval”ofthis sort t will give
betterresultsforanyparticularestimationproblem.However,thisdoesseemtobethecasemore
oftenthannot.Gretlthereforeperformsscaleremovalwherefeasible,unlessyou
10
Asanumericalmatter,thatis.Inprinciplethisshouldmakenodifference.
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Chapter27. CointegrationandVectorErrorCorrectionModels
253
 explicitlyforbidthis,bygivingthe--no-scalingoptionflagtotherestrictcommand;or
 provideaspecificvectorofinitialvalues;or
 selecttheLBFGSalgorithmformaximization.
Scaleremoval is deemedinfeasibleifthere areanycross-columnrestrictions on, or r any y non-
homogeneousrestrictionsinvolvingmorethanoneelementof.
Inaddition,experimentationhassuggestedtousthatscaleremovalisinadvisableifthesystemis
justidentifiedwiththenormalization(s)included,sowedonotdoitinthatcase.By“justidentified”
wemeanthatthesystemwouldnotbeidentifiedifanyoftherestrictionswereremoved. Onthat
criteriontheaboveexampleisnotjustidentified,sincetheremovalofthesecondrestrictionwould
notaffectidentification;andgretlwouldinfactperformscaleremovalinthiscaseunlesstheuser
specifiedotherwise.
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Chapter28
Multivariatemodels
By amultivariatemodelwemeanonethat includesmorethan onedependent variable. . Certain
specifictypesofmultivariatemodelfortime-seriesdataarediscussedelsewhere:chapter26deals
withVARsandchapter27withVECMs. Herewediscusstwogeneralsortsofmultivariatemodel,
implementedingretlviathesystemcommand: SUR R systems(SeeminglyUnrelatedRegressions),
inwhichalltheregressorsaretakentobeexogenousandinterestcentersonthecovarianceofthe
errortermacrossequations;andsimultaneoussystems,inwhichsomeregressorsareassumedto
beendogenous.
Inthischapterwegiveanaccountofthesyntaxanduseofthesystemcommandanditscompan-
ions, restrictandestimate; wealso explaintheoptionsandaccessorsavailableinconnection
withmultivariatemodels.
28.1 Thesystemcommand
Thespecificationofamultivariatesystemtakestheformofablockofstatements, starting with
system and ending with h end d system. . Oncea a system is specified it can estimated via various
methods, usingtheestimate command, withorwithoutrestrictions, whichmaybeimposedvia
therestrictcommand.
Startingasystemblock
Thefirstlineofasystemblockmaybeaugmentedineither(orboth)oftwoways:
 An n estimationmethod is specified for r the e system. . This s is done e by followingsystem with
anexpressionoftheformmethod=estimator,whereestimator mustbeoneofols(Ordinary
LeastSquares),tsls(Two-StageLeastSquares),sur(SeeminglyUnrelatedRegressions),3sls
(Three-StageLeast Squares), liml l (Limited Information n MaximumLikelihood)or r fiml (Full
InformationMaximumLikelihood).Twoexamples:
system method=sur
system method=fiml
OLS,TSLSandLIMLare,ofcourse,single-equationmethodsratherthantruesystemestima-
tors;theyareincludedtofacilitatecomparisons.
 Thesystemis s assignedaname. . Thisisdonebygiving g thenamefirst, followedbyaback-
arrow,“<-”,followedbysystem. Ifthenamecontainsspacesitmustbeenclosedindouble-
quotes.Herearetwoexamples:
sys1 <- system
"System 1" " <- - system
Note,however,thatthisnamingmethodisnotavailablewithinauser-definedfunction,only
inthemainbodyofagretlscript.
Iftheinitialsystemlineisaugmentedinthefirstway,theeffectisthatthesystemisestimatedas
soonasitsdefinitioniscompleted,usingthespecifiedmethod. Theeffectofthesecondoptionis
254
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Chapter28. Multivariatemodels
255
thatthesystemcanthenbereferencedbytheassignednameforthepurposesoftherestrictand
estimatecommands;inthegretlGUIanadditionaleffectisthataniconforthesystemisadded
tothe“Sessionview”.
Thesetwopossibilitiescanbecombined,asin
mysys <- - system m method=3sls
Inthis examplethesystem is estimated immediately viaThree-Stage e Least Squares, , and is also
availableforsubsequentuseunderthenamemysys.
Ifthesystemisnotnamedviatheback-arrowmechanism,itisstillavailableforsubsequentuse
viarestrictandestimate;inthiscaseyoushouldusethegenericname$systemtorefertothe
last-definedmultivariatesystem.
Thebodyofasystemblock
Themostbasicelementinthebodyofasystemblockistheequationstatement,whichisused
tospecifyeachequationwithinthesystem. Thistakesthesameformastheregressionspecifica-
tionforsingle-equationestimators,namelyalistofserieswiththedependentvariablegivenfirst,
followedbytheregressors, with theseries given either bynameor by IDnumber r (order r in n the
dataset). Asystemblockmustcontainatleasttwoequationstatements,andforsystemswithout
endogenous regressorsthesestatementsareallthatisrequired. . So, , for example,aminimalSUR
specificationmightlooklikethis:
system method=sur
equation y1 const t x1
equation y2 const t x2
end system
Forsimultaneoussystemsitisnecessarytodeterminewhichregressorsareendogenousandwhich
exogenous.Bydefaultallregressorsaretreatedasexogenous,exceptthatanyvariablethatappears
asthedependentvariableinoneequationisautomaticallytreatedasendogeousifitappearsasa
regressorelsewhere. However,anexplicitlistofendogenousregressorsmaybesuppliedfollow-
ingtheequationslines: thistakestheformofthekeywordendogfollowedbythenamesor r ID
numbersoftherelevantregressors.
WhenestimationisviaTSLSor3SLSitispossibletospecifyaparticularsetofinstrumentsforeach
equation. This s is donebygivingtheequationlistsintheformatusedwiththetslscommand:
firstthedependentvariable,thentheregressors,thenasemicolon,thentheinstruments,asin
system method=3sls
equation y1 const t x11 1 x12 ; ; const t x11 z1
equation y2 const t x21 1 x22 ; ; const t x21 z2
end system
Analternativewayofspecifyinginstrumentsistoinsertanextralinestartingwithinstr,followed
bythelistofvariablesactingasinstruments.Thisisespeciallyusefulforspecifyingthesystemwith
theequationskeyword,seethefollowingsubsection.Asintsls,anyregressorsthatarenotalso
listedasinstrumentsaretreatedasendogenous,sointheexampleabovex11andx21aretreated
asexogenouswhilex21andx22areendogenous,andinstrumentedbyz1andz2respectively.
Onemoresortofstatementisallowedinasystemblock:thatis,thekeywordidentityfollowedby
anequationthatdefinesanaccountingrelationship,ratherthenastochasticone,betweenvariables.
Forexample,
identity Y = C + + I I + G + X
Therecanbemorethanoneidentityinasystemblock.Butnotethatthesestatementsarespecific
toestimationviaFIML;theyareignoredforotherestimators.
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Chapter28. Multivariatemodels
256
Equationsystemswithinfunctions
Itisalsopossibletodefineamultivariatesysteminaprogrammaticway.Thisisusefuliftheprecise
specificationofthesystemdependsonsomeinputparametersthatarenotknowninadvance,but
aregivenwhenthescriptisactuallyrun.
1
Therelevantsyntaxisgivenbytheequationskeyword(notetheplural),whichreplacestheblock
ofequationlinesinthestandardform. Anequationslinerequirestwolistarguments. Thefirst
listmustcontainallseriesontheleft-handsideofthesystem;thusthenumberofelementsinthis
firstlistdeterminesthenumberofequationsinthesystem.Thesecondlistisa“listoflists”,which
isaspecialvariantofthelistdatatype.Thatis,foreachequationofthesystemyoumustprovidea
listofright-handsidevariables,andthelistsforallequationsmustbejoinedbyassigningthemto
anotherlistobject;inthatassignment,theymustbeseparatedbyasemicolon.Hereisanexample
foratwo-equationsystem:
list syslist = xlist1 ; xlist2
Therefore, specifying a a system generically in this way y just t involves s building the necessary y list
arguments,asshowninthefollowingexample:
open denmark
list LHS S = = LRM LRY
list RHS1 = const LRM(-1) IBO(-1) IDE(-1)
list RHS2 = const LRY(-1) IBO(-1)
list RHS S = = RHS1 ; RHS2
system method=ols
equations LHS S RHS
end system
Asmentionedabove, theoptionof assigningaspecific nameto asystemisnotavailablewithin
functions,butthegenericidentifier$systemcanbeusedtosimilareffect. Thefollowingexample
shows how one e can definea system, estimateit via two o methods, apply y a restriction, , then n re-
estimateitsubjecttotherestriction.
function void d anonsys(series x, series y)
system
equation x x const
equation y y const
end system
estimate $system method=ols
estimate $system method=sur
restrict $system
b[1,1] - b[2,1] = 0
end restrict
estimate $system method=ols
end function
28.2 Restrictionandestimation
Thebehavioroftherestrictcommandisalittledifferentformultivariatesystemsascompared
withsingle-equationmodels.
Inthesingle-equationcase,restrictreferstothelast-estimatedmodel,andoncethecommandis
completedtherestrictionistested.Inthemultivariatecase,youmustgivethenameofthesystem
towhichtherestrictionis to beapplied(or$systemtoreferto thelast-definedsystem),andthe
1
Thisfeaturewasaddedinversion1.9.7ofgretl.
Chapter28. Multivariatemodels
257
effectofthecommandisjusttoattachtherestrictiontothesystem;testingisnotdoneuntilthe
nextestimatecommandisgiven. Inaddition, , inthesystemcasethedefaultisto producefull
estimatesoftherestrictedmodel;ifyouarenotinterestedinthefullestimatesandjustwantthe
teststatisticyoucanappendthe--quietoptiontoestimate.
Agivensystemrestrictionremainsinforceuntilitisreplacedorremoved. Toreturnasystemto
itsunrestrictedstateyoucangiveanemptyrestrictblock,asin
restrict sysname
end restrict
Asillustratedabove,youcanusethemethodtagtospecifyanestimationmethodwiththeestimate
command.Ifthesystemhasalreadybeenestimatedyoucanomitthistagandthepreviousmethod
isusedagain.
Theestimate commandis themainlocus for options regardingthedetails of estimation. . The
availableoptionsareasfollows:
 IftheestimationmethodisSURor3SLSandthe--iterateflagisgiven,theestimatorwillbe
iterated. InthecaseofSUR,iftheprocedureconvergestheresultsaremaximumlikelihood
estimates.Iterationofthree-stageleastsquares,however,doesnotingeneralconvergeonthe
full-informationmaximumlikelihoodresults.Thisflagisignoredforotherestimators.
 Iftheequation-by-equationestimatorsOLSorTSLSarechosen, , thedefaultistoapplyade-
greesoffreedomcorrectionwhencalculatingstandarderrors. Thiscanbesuppressedusing
the--no-df-corrflag. Thisflaghasnoeffectwiththeotherestimators;nodegreesoffree-
domcorrectionisappliedinanycase.
 By y default, , theformula a used in calculating the elements s of thecross-equation covariance
matrixis
ˆ
ij
ˆu
0
i
ˆu
j
T
where is s the e sample size and d ˆu
i
is the e vector of residuals s from equation i. But t if the
--geomeanflagisgiven,adegreesoffreedomcorrectionisapplied:theformulais
ˆ
ij
ˆu
0
i
ˆu
j
q
T k
i
T k
j
wherek
i
denotesthenumberofindependentparametersinequationi.
 Ifaniterativemethodisspecified, , the--verboseoptioncallsfor printingofthedetailsof
theiterations.
 Whenthesystemestimator r isSUR or 3SLSthecross-equationcovariancematrix x isinitially
estimatedviaOLSor TSLS, respectively. . Inthecaseofasystemsubjecttorestrictions s the
question arises: : should d theinitialsingle-equation estimator r berestricted d or unrestricted?
Thedefaultistheformer,butthe--unrestrict-initflagcanbeusedtoselectunrestricted
initialization. (Notethatthisisunlikelytomakemuchdifferenceifthe--iterateoptionis
given.)
28.3 Systemaccessors
Aftersystemestimationvariousmatricesmayberetrievedforfurtheranalysis. Letdenotethe
number of equationsin n the systemandletdenotethetotalnumber r ofestimated parameters
(
P
i
k
i
). Theaccessors s $uhat and$yhatgetTgmatricesholding theresiduals andfitted
valuesrespectively. Theaccessor$coeffgetsthestackedK-vectorofparameterestimates;$vcv
Chapter28. Multivariatemodels
258
getstheKKvariancematrixoftheparameterestimates;and$sigmagetstheggcross-equation
covariancematrix,
ˆ
Ö.
AteststatisticforthehypothesisthatÖisdiagonalcanberetrievedas$diagtestanditsp-value
as$diagpval. ThisistheBreusch–Pagantestexceptwhentheestimatoris(unrestricted)iterated
SUR,inwhichcaseit’saLikelihoodRatiotest.TheBreusch–Pagantestiscomputedas
LMT
g
X
i2
iX1
j1
r
2
ij
wherer
ij
ˆ
ij
=
q
ˆ
ii
ˆ
jj
;theLRtestis
LRT
0
@
g
X
i1
logˆ
2
i
logj
ˆ
Öj
1
A
where ˆ
2
i
is ˆu
0
i
ˆu
i
=T fromtheindividualOLSregressions. Inbothcases s theteststatisticis dis-
tributedasymptoticallyas
2
withgg 1=2degreesoffreedom.
Structuralandreducedforms
Systemsofsimultaneoussystemscanberepresentedinstructuralformas
y
t
A
1
y
1
A
2
y
2
A
p
y
t p
Bx
t
t
wherey
t
represents thevectorofendogenousvariablesinperiodtx
t
denotesthevectorofex-
ogenousvariables,andpisthemaximumlagoftheendogenousregressors. Thestructural-form
matricescanberetrievedas$sysGamma,$sysAand$sysB respectively. . Ify
t
ism1andx
t
is
n1,then— ismmandBismn.Ifthesystemcontainsnolagsoftheendogenousvariables
thentheAmatrixisnotdefined,otherwiseAisthehorizontalconcatenationofA
1
;:::;A
p
,andis
thereforemmp.
Fromthestructuralformitisstraightforwardtoobtainthereducedform,namely,
y
t
—
1
0
@
p
X
i1
A
i
y
t i
1
A
—
1
Bx
t
v
t
wherev
t
—
1
t
.Thereducedformisusedbygretltogenerateforecastsinresponsetothefcast
command. Thismeansthat—incontrastto o single-equationestimation—thevaluesproducedvia
fcastforastatic,within-sampleforecastwillingeneraldifferfromthefittedvaluesretrievedvia
$yhat.Thefittedvaluesforequationirepresenttheexpectationofy
ti
conditionalonthecontem-
poraneousvaluesofalltheregressors,whilethefcastvaluesareconditionalontheexogenous
andpredeterminedvariablesonly.
TheaboveaccounthastobequalifiedforthecasewhereasystemissetupforestimationviaTSLS
or3SLSusingaspecificlistofinstrumentsperequation,asdescribedinsection28.1. Inthatcase
itispossibletoincludemoreendogenousregressorsthanexplicitequations(although,ofcourse,
theremustbesufficientinstrumentsto achieveidentification). . Insuch h systems endogenous re-
gressorsthathavenoassociatedexplicitequationaretreated“asif”exogenouswhenconstructing
thestructural-formmatrices. Thismeansthatforecastsareconditionalontheobservedvaluesof
the“extra”endogenousregressorsratherthansolelyonthevaluesoftheexogenousandpredeter-
minedvariables.
Chapter29
Forecasting
29.1 Introduction
Insomeeconometriccontextsforecastingistheprimeobjective:onewantsestimatesofthefuture
valuesofcertainvariablestoreducetheuncertaintyattachingtocurrentdecisionmaking.Inother
contextswherereal-timeforecastingisnotthefocuspredictionmaynonethelessbeanimportant
moment inthe analysis. . For r example, out-of-sample prediction can provide a useful check on
thevalidityofaneconometric model. . Inother r casesweareinterestedinquestionsof“whatif”:
forexample,howmightmacroeconomicoutcomeshavedifferedoveracertainperiodifadifferent
policyhadbeenpursued?Inthelattercases“prediction”neednotbeamatterofactuallyprojecting
into thefuturebutinanycaseitinvolvesgeneratingfittedvaluesfromagivenmodel. . Theterm
“postdiction”mightbemoreaccuratebutitisnotcommonlyused; wetendto talkofprediction
evenwhenthereisnotrueforecastinview.
Thischapteroffersanoverviewofthemethodsavailablewithingretlforforecastingorprediction
(whetherforwardintimeornot)andexplicatessomeofthefinerpointsoftherelevantcommands.
29.2 Savingandinspectingfittedvalues
Inthesimplestcase,the“predictions”ofinterestarejustthe(withinsample)fittedvaluesfroman
econometricmodel. Forthesingle-equationlinearmodel,y
t
X
t
u
t
,theseare
ˆ
y
t
X
t
ˆ
.
Incommand-linemode,theyˆseriescanberetrieved,afterestimatingamodel,usingtheaccessor
$yhat,asin
series yh = $yhat
Ifthemodelin questiontakes theformofasystem of equations, $yhat t returnsamatrix, each
columnofwhichcontainsthefittedvaluesforaparticulardependentvariable.Toextractthefitted
seriesfor,e.g.,thedependentvariableinthesecondequation,do
matrix Yh = $yhat
series yh2 = Yh[,2]
Havingobtainedaseriesoffittedvalues,youcanusethefcstatsfunctiontoproduceavectorof
statisticsthatcharacterizetheaccuracyofthepredictions(seesection29.4below).
Thegretl GUI offersseveral waysofaccessing andexamining within-samplepredictions. . In n the
modeldisplaywindowtheSavemenucontainsanitemfor savingfittedvalues, theGraphsmenu
allowsplottingoffittedversusactualvalues,andtheAnalysismenuoffersadisplayofactual,fitted
andresidualvalues.
29.3 Thefcastcommand
Thefcastcommandgeneratespredictionsbasedonthelastestimatedmodel. Severalquestions
arisehere: How w to o controltherangeoverwhichpredictions aregenerated? ? How w tocontrolthe
forecastingmethod(whereachoiceisavailable)?Howtocontroltheprintingand/orsavingofthe
results? BasicanswerscanbefoundintheGretlCommandReference;weaddsomemoredetails
here.
259
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