white john s. the limiting distribution of the serial correlation coefficient in the explosive case
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8/3/2019 White John S. the Limiting Distribution of the Serial Correlation Coefficient in the Explosive Case
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The Limiting Distribution of the Serial Correlation Coefficient in the Explosive Case
Author(s): John S. WhiteReviewed work(s):Source: The Annals of Mathematical Statistics, Vol. 29, No. 4 (Dec., 1958), pp. 1188-1197Published by: Institute of Mathematical StatisticsStable URL: http://www.jstor.org/stable/2236955 .
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THE LIMITINGDISTRIBUTION OF THE SERIAL CORRELATIONCOEFFICIENT IN THE EXPLOSIVE CASE
BY JOHN . WHITE
Aero ivision, inneapolis oneywellegulatorompany, inneapolisMinnesota
1. Introductionnd summary. everalauthorshave studied he discretestochasticrocessxt) nwhich hex's arerelated ythe stochasticifferenceequation
(1.1) Xt= axt,- + u, tX 1, 2, * Ty
whereheu's are unobservableisturbances,ndependentndidenticallyis-tributed ithmean ero nd variance2, nda is anunknownarameter.
The statisticalroblems tofind ome ppropriateunctionfthex's as anestimatoror andexaminetsproperties.
Wemay ewrite1.1)as
(1.2) Xt=Ut + aUt_ + * +a+Ct lU + &XO.
From 1.2) we see that the distributionf the successive 's is notuniquelydeterminedythat f heu'salone.The distributionfxomust lsobespecified.Three istributionshich avebeenproposedor o re thefollowing:
(A) xo= a constantwith robabilityne),(B) xo s normally istributed ithmean zeroand varianceo2/(l - a2),
(C) Xo= XT.
DistributionB) is perhapshemost ppealing rom physical oint fview,sincefxohas thisdistributionnd ftheu's arenormallyistributed,hen heprocesssstationarye.g., eeKoopmans4]).However,here re everalnalyticdifficultieshich rise nthe tatisticalreatmentfthisprocess. istribution(C), the o-calledircularistribution,asbeenproposeds an approximationto (B) and s much asier oanalyze e.g., ee Dixon 2]).DistributionA) has
beenstudied xtensivelyy Mann and Wald [5].An interestingeature fdistributionA) is that may ssume nyfinitealue,while or istributions(B) and (C) a mustbe between 1 and 1. From 1.2) weseethat processsatisfying1.1)and(A) has
(1.3) var xt) = 2(1 + a2 + * + a2"'1).
If a I > 1, imt-.var xt) = ooand theprocesss said to be "explosive."Mann and Wald [5] considered nlythe case Ia I < 1. They showedthat the
least quares stimatoror isthe erial orrelationoefficient'
(1.4)a
22xsx_iE X9_1
Received ecember0,1955; evisedMay 27,1958.1 Inthis aper, he ummationignE will lways efero ummationrom= 1tot= T.
1188
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LIMITING DISTRIBUTIONS 1189
and that for aI< 1) thisestimators asymptotically ormally istributed ith
mean a and variance (1 - a2)/T. Rubin [6] showed that the estimatora isconsistent i.e., plim a = a) for all a.
In this paper the asymptotic istribution f& will be studied under the as-sumption hat the u's are normally istributed. or Ia I > 1, t is shown hat heasymptotic istributionfa is the Cauchy distribution.or Ia I = 1, a momentgenerating unction s found, he inversion f whichwillyield the asymptoticdistribution.
2. The distributionf & - a. From equation (1.1) and condition A) thejoint distributionf
x =(X1, X2X*** X)
is easily found o be
(2.1) f(x') = exp [(- 1/20o2)? (Xt-aXt_1)2](2.1) AX')
~~~~~~~~~(2ircr2)/2
The maximumikelihood stimator or is then the least-squares stimator '.
Sincewe shall be considering nlythedistributionf
, E Xt Xt-ia 2 '
Xt-1
we may,without oss ofgenerality,ake 2 = 1. For the timebeingwe shallalso setxo= 0.
We maynow write 2.1) in matrix orm s follows:
(2.2) f(x') - ~~exp-I x'Px)(2.2) AX -
(2r) T/2
whereP is the T X T matrix
-1 a 2 -a - 0
-a 1 + at2 _cot 0
(2.3) P [ O -+a 1 + a2 -a .IC + ay2 xIa -+a 1
o -a Ii
Sincea is a consistent stimator or , we shallconsider he distributionf - arather han thatofa alone. We have
A E XtXt.ia-a -C% a
(2.4) = E XtXt_i_ a EXt_
EXt-l
x'Axx'Bx'
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1190 JOHN S. WHITE
whereA andB are theT X T matrices2 a - 1 0-1 22a -10 - 1 2a
-1 2ca -1
(2.5)0 -1 0
-1 0 010 1 01
B O 0 1
o 1 ?1
Let m(u,v) be the jointmomentgenerating unction f x'Ax and x'Bx. Wehave
m(u,v) = E (exp {x'Axu + x'Bxv})
(2.6) = (27r)-T12
fxp (x'Axu+ x'Bxv x'Px/2)dx
= (27r)fT12f xp (-x'Dx/2) dx,
whereD is the T X T matrix
p q Oq P q
(2.7) D=P-2Au-2Bv= 0 q P p
q P q
0 q 1p= 1+ a2- 2v+ 2au, q= -(a + u).
By a well-knownntegrationormulaCramer[1],Eq. (11.12.2.), p. 120) we
have
(2.8) m(u,v) = (27r)T12 exp ( x2Dx) dx = (detD)-1.
If wenowwrite etD D(T), we note thatexpanding2.7) bytheelements f
thefirst olumn ivesthedifferencequation
(2.9) D(T) = pD(T - 1) - q2D(T - 2).
From the nitialvaluesD(1) = 1 andD(2) = p - q2,we obtain
1 _ s T 1 - r T(2.10) D r)=-s s-r~~
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LIMITING DISTRIBUTIONS 1191
where ands areroots f he quation - px + q2 = 0, that s(2.11) r,s = (p 4 p / 4q2)/2.
The nversionfm(u,v) =(T)-- seems utof he uestion or inite . Theinversionf certainimitingormfm(u,v)willbe discussednSection .
3. The standardizingunction(T). Since&i s consistenthe imiting istri-butionf& - a is theunitaryistribution.he firstroblemhenstofind omefunctionfT, sayg(T), such hat he imitingistributionfg(T) (& - a) isnon-degenerate.enote hat heresultsfMannandWald Eq. (1.4) above)
giveg(T) = (T/I{1 - a2})l for ja j < 1, since (T/{1 -a2})* (c> ) has alimitingormal istribution.he function2(T) correspondsoughlyo thereciprocalf he symptoticariancef & - a), or nFisher'serminologyhe"information"n supplied ythe ample.
The "information"n a maybe obtainedxplicitlys follows. etf be thedensityunction2.1)with O= 0 and o,2 = 1. The"information,"ay (a), isthen efineds
I(a) = (E d( 2)
= E ( xt_j)(3.1) (T 1a2r) if
T(T-1) if a I=1.2
If thex'shadbeen ndependentandomariables,hen(a) (a& a) would easymptotically (0, 1) (Cramer1],Eq.(33.3.4), p. 503). This,ofcourse,snot he ase.This pproach oes,however,ive nheuristic ethodor inding
function(T) such hat (T) (& - a) has a non-degenerateimitingistribution.Wemightow akeg(T) = [1(a)]*;however,twill implifyhe omputationsto useslightmodificationshich reasymptoticallyquivalento [1(a)]1. Wechoose
g T) = <for a I < 1,
T(3.2) fora I = 1,
- aTa a-1 forjal > 1.
In the next ectiontwillbe shown hatg(T) (ca a) has a non-degeneratedistributionor ll values f .
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1192 JOHN S. WHITE
4. The limiting istributionf g(T) (& - a). We shallfirst onsider he ointdistribution fx'Ax/g(T) and x'Bx/g2(T).et M(U, V) be the jointmomentgenerating unctionf thesetwo statistics.We thenhave
M(U, V) = E[expx'AxU/g(T) + x'BxV/g2(T)]
= m[U/g(T),V/g'(T)j,
wherem(u,v) is the ointmoment eneratingunction2.6).From 2.10) and (2.11) withg = g(T), u = U/g and v = Vlg2,we have
M(U,V)
=D(T)-1(4.2) 1-srT + 1-rTr-s s-r
r S 2[1 + a2 + 2aU/g - 2V/g2? {(1 - a2)2 - 4a(1 -a2)U/g
- 4(1 - a2)U2/g2 4(1 + a2)V/l2 8aUV/g3+ 4V2/g}"1/2]
ForsufficientlyargeT and Ia I $ 1,wemayfactor1 - a2) out oftheradical n(4.3) and expandtheremainingadicalbythebinomial heorem.We thenhave,up to terms f orderO(g-3)
r, = 1[ + a2 + 2aU/g - 2V/92(4.4)
2- 2 - 2aU/g 2(1 + a2)V _ 2U2 + g,fJ]
(1 - oil)g2 (1- a.2) lTakingrwiththeplus sign and s withthe minus ignwe have
r = 1 U2+ 2V+ 0(9-3)
(4.5)~ r= -(1 - al)g2+Og)
(4.5) ~ ~ ~ 2U2 + 2a2Vs = a + 2aU/g+ (1 - 2)g2 + O(g )
Substitutingheappropriate alues ofg(T) from3.2), we have
U2 +2Vr = 1 T +0 (T-') for a I < 1,
(4.6) ___ TS- a2 + 2a - a2 U+ U + 2a2V+ 0(T-').
+ - +0(I ) forlal > 1,
(4.7)2_ U 2V a2 2aU(a - 1) (U2 2a2V) 1) +2 a -3T).= i + Ia |I a2T
II
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LIMITING DISTRIBUTIONS 1193
If lal=
1, theexpansionn (4.4) is notvalid; however, rom4.3), wehave1 4 TaU + 2iV + O(T2) for I =1,
(4.8) V VS = 1 + Val_2i-\V O(T-2)=1+ T T-
Substitutinghese esultsn 4.2),wehave
limM(U, V) =exp (V + U2/2) for al < 1,
(1- u2 - 2V)-1/2 for(aj > 11
(4.9)= exp VaU (cos 2V-VV-2U
2\in
for ax = 1.
The nextproblems toobtain he imitingistributionifg(T)(a& a) fromlimM(U, V). Sinceg(T)(&$* a) = g(T)x'Ax/x'bX,heproblems one offindinghedistributionfthe ratiooftwo random ariables. nemethod fsolutionasbeen roposedyGurland3].LetX andYbetwo andomariables,Prob Y > 0) = 1. Wewish odeterminehedistributionf Z = X/Y. Let
W = = X - zY. ThenwehaveProb Z < z) = Prob X/Y < z)
(4.10) = Prob X - zY < 0)
= Prob W. < 0).
If the distributionfW can be found,hedistributionfZ will mmediatelyfollow. requentlyhedistributionfWcanbefoundrom hat fX and Y bymeans fmomenteneratingunctions.et
(4.11) m(w)= E(exp w} , m*(u, ) = E(exp Xu + Yv} ,
then
m(w) E(exp{X - zY}w) = E(exp{Xw Yzw}) - m*(w, zw).
To apply his echniqueo theproblemt hand,we set W = x'Ax/gzx'Bx/g2.rom4.1), 4.2) and 4.9)wehave
m(w)= M(w,-zw),
limm(w)= exp -zw + w2/2) for a j < 1,
(4.12) = (1 + 2zw- w2)-' for a > 1,= {exp (V2caw) cos 2V-zw - ___ sin2-\/-Zw)}
forlal =a
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1194 JOHN S. WHITE
The inversion f imm(w) s trivial or a I < 1. The moment eneratingunctionexp (-zw+w2/2) is immediately ecognized s that of a randomvariablewhich s normally istributedwithmean -z and variance 1. Hence we have
lim rob (W < 0) - (27r)-L xp (-{t + z}2/2)dt
00(4.13) = (21rr-112fLxp -t2/2)dt
=limProb {g(T)(8' - a) <z},
i.e., g(T) (' - a) is asymptoticallyormalwithmean 0 and variance1.For Ia I > 1, the nverse f im m(w) mightbe obtaineddirectlyn termsof
Besselfunctions; owever,t is more ppealingfrom statistical ointofviewtoproceed s follows. et X and Y be independent hi-squared ariableswithonedegreeoffreedom. hen E(exp {Xw ) = E(exp { Yw ) = (1 - 2w)112 is theircommonmomentgenerating unction.Now set R = aX - bY, the momentgeneratingunction f R willbe
mR(w) E(exp{Rw}) = E(exp{aX - bYiw)
= ({1 - 2aw} 11+ 2bw)-1/2
In particularfwe set
(4.15) 2a = V/1 z2-z, 2b=V/1 + Z2+ Z,
wehave
(4.16) mR(w) = (1 + 2zw W2)112 = lim m(w).
Hence,the imiting istributionf W, for a j > 1, s the ame s thedistributionof R = aX - bY.We thenhave
rimrob (W < 0) = Prob (aX - bY < 0)
= Prob (X < bY/a)
(4.17) 1 fo a exp - x/2- y/2)=- Z I / ~~~~dxy
- limProb {g(T)(a' - a) < z} = say F(z).
The density unction orrespondingo F(z) is
f(z) dz 2)_Vlo/5 xp (-by/2a - y/2)4d(b/a dy
(4.18) 1~V7la (ba)d(/a2ir 1 + (bla) dz
1r1 + Z2 (by (4.15)).
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LIMITING DISTRIBUTIONS 1195
Hence the limiting istributionf g(T)(ci -a), for a I > 1, is the Cauchydistribution.We have beenunable to invert imm(w) whenIa = 1. In thenext ection
certain esults oncerninghis imit nd moregeneralproblems f this typewill
be discussed.Ifwenow etxo= c,a non-zeroonstant, heanalysisproceedsmuch s before.
Let A, B, P, and D be theT X T matrices efinedn (2.3), (2.5) and (2.7). We
thenhave, analogousto (2.1) and (2.4),
f(x') = (2)-T2 exp (cx,a - 2c2/2 -x'Px/2),
(4.19) A x'Ax+ cx, - aea a x'Bx+c2
The jointmoment eneratingunctionfx'Ax + cx1 - W22andx'Bx+ C2 is
m(u,v) = E (exp {(xAx + cx - aeC2)u+ (x'Bx + c2)v})
= {exp (c2v - cau - a c2)} (2X)-T2
2 ~ ~ ~ ~(4.20) xfexp([uD+]cxi x2D
- aC ~~~~~ +x [u .JIx7)(r-dxI
=exp (C2V- ae)U-!) p + 2 D (T)JD 1
limm(U/g,V/g2)= limM(U, V)
(4.21) --f(T(a 2C2 Y~-D(T - 1)1,= limD(T)-4 exp (2 1 D(T)]JJ
whereD(T) is as definedn (4.2) whileD'(T - 1) is definedn a similar ashion
butwithg = g(T).
For ja j < 1,itfollows rom4.6) and (4.8) that,sinceg(T) andg(T - 1) areofthesameorder,
limD(T) = limD'(T - 1)and hence
(4.22) lim m(U/g, V/g2) = limM(U, V) = limD(T)-112.
We seethatthis imit s the sameas thatfor o= 0 as given n (4.9) andhence
the limiting istributionfg(T)(& - a) does not dependon the initialvalue
xofor at < 1.For
ja > 1 wehave,from4.7),
lim D(T) = 1- (U + 2V),
(4.23) lim 'T- 1 = (U + 2V)
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1196 JOHN S. WHITE
and nplaceof 4.22)wehave
limM(U, V) = Jim (T) "12 exp 2c [ _ D' (T )])
= (1 - U2- 2Vf112 exp{( -l)c ( Y2+ V)}
Thismomenteneratingunction aybe invertedythemethodsfSectiontogive
(4.25) f(x) = vr(1+X2)
1 ( ) ( q =
as the imitingistributionfg(T)(& - a). Wenote hatfor = 0, f(x) is theCauchy istributions obtainedn 4.18).
6. Finalremarks. heresultsfMannandWald[5]show hat he imitingdistributionfg(T)(a - a), forca < 1, s alsoN(O,1) if, atherhan ssumingthat he"errors"tare normallyistributed,e merelyssume hat ll ofthemomentsf heu's arefinite.his sanotherxamplef ninvariancerinciplewhicheems oholdquitegenerallyor he imitingistributionsffunctionfrandomariables. oughlypeaking,here eems obe an unprovedandun-
stated) heoremhatthe imitingistributionfa functionfa sequence findependentandom ariables, ith uitable estrictionsnthese andom ari-ables, ependsnly nthe ormf he unctionnd sthe ame s thedistributionof relatedunctionalna stochasticrocess.
Ageneralesultf his ormsDonsker's heorem7]which ives he imitingdistributionfany functionf sumsof independentdenticallyistributedrandom ariableswithfinite ariancess thedistributionfa correspondingfunctionalntheWiener rocess.t is conjecturedhat his ype freasoningwill how hat heresultsfMann ndWaldwill tillhold ftheu's aremerelyassumedohavefiniteariances.
Fora = 1, pplicationfDonsker's heoremhowshat he imitingistribu-tion fg(T)(a& a) is the ame s thedistributionf hefunctional
fx t) dx It)
G[x(.)J =2 x1f(t)dt f (t)dt
ontheWienerrocess,ndependentf hedistributionf heu's. Thisdistribu-tionwillbe consideredna future aper.
REFERENCES
[11H. CRAMER, Mathematical ethods fStatistics, rincetonUniversity ress,1946.[21W. J.DIXON, "Further ontributionso theproblem f erial correlation," nn.Math.
Stat.,Vol. 15 1944),pp. 119-144.
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