Hae el, Thomas; Lucke, Be nd
Wo king Pape
New E idence on News-D i en Business Cycles
Economics Discussion Pape s, No. 2007-27
P o ided in Coope a ion wi h:
Kiel Ins i u e o he Wo ld Economy – Leibniz Cen e o Resea ch on Global Economic Challenges
Sugges ed Ci a ion: Hae el, Thomas; Lucke, Be nd (2007) : New E idence on News-D i en Business
Cycles, Economics Discussion Pape s, No. 2007-27, Kiel Ins i u e o he Wo ld Economy (I W), Kiel
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discussion Pape s
Discussion Pape 2007-27
July 9, 2007
New E idence on News-D i en Business Cycles
Thomas Hae el and Be nd Lucke
Uni e si y o Hambu g
Abs ac :
We s udy he Beaud y and Po ie (2006)-hypo hesis o delayed- echnology di usion and news-
d i en business cycles. Fo Ge man da a on TFP and s ock p ices we ind quali a i ely simila
empi ical e idence. Quan i a i ely, howe e , an impulse esponse analysis sugges s ha a sub-
s an ial pa o he o al TFP esponse is immedia e a he han delayed. We ela e his o disem-
bodied echnological change and noisy da a on TFP. Ne e heless, we con i m he echnology
in e p e a ion o s uc u al shocks by showing ha hey a e G ange -causal o da a on pa en s
g an ed by he Ge man pa en agency.
JEL: E32
Keywo ds: news, business cycles, TFP, s uc u al VAR
Co espondence:
Uni e si y o Hambu g, on-Melle-Pa k 5, D-20146 Hambu g, email: Thomas.Hae [email protected] g.de
Uni e si y o Hambu g, on-Melle-Pa k 5, D-20146 Hambu g, email: luck[email p o ec ed].de
h p://www.economics-ejou nal.o g/economics/discussionpape s
© Au ho (s) 2007. This wo k is licensed unde a C ea i e Commons License - A ibu ion-NonComme cial 2.0 Ge many
1 In oduc ion
In a ecen pape , Beaud y and Po ie (2006) ha e emphasized ha s ock p ices may ha e
ele an in o ma ional con en o unde s anding mac oeconomic luc ua ions. New
in o ma ion, hei a gumen goes, may al e expec a ions abou u u e undamen als. Fo wa d
looking a iables such as s ock p ices will eac o changes in expec a ions much ea lie han
he o eseen changes in undamen als a ec o he mac oeconomic ime se ies. In pa icula ,
news abou echnological inno a ions may a ec s ock p ices ins an aneously, bu due o an
implemen a ion lag, i may ake some ime un il hey ac ually al e o al ac o p oduc i i y
(TFP). Thus, ou unde s anding o expec a ions-d i en mac oeconomic luc ua ions could be
enhanced i news abou expec ed changes in undamen als could be p ope ly iden i ied om,
among o he s, s ock ma ke da a.
Fo his pu pose, Beaud y and Po ie (BP) sugges o use s uc u al ec o au o eg essions
(SVAR). Imposing di e en iden i ying es ic ions on he es ima ed lag polynomial o a
mo ing a e age (MA) ep esen a ion (c . Blancha d and Quah (1989)) yields a se o
s uc u al shocks o eady compa ison. I simila shocks a e ound unde di e en iden i ying
assump ions, hen he ype o iden i ying assump ion e eals impo an in o ma ion abou he
way a speci ic shock hi s he economy. This, in u n, allows in e ence on he alidi y o
compe ing models and hei assump ions.
Fo ins ance, in a bi a ia e ec o au o eg ession o US TFP and s ock p ices, BP ind wo
almos co-linea shocks unde wo pola iden i ying assump ions. The i s iden i ying
assump ion imposes ha he e exis s a shock which does no al e TFP in he sho un, he
al e na i e iden i ying assump ion imposes ha he e exis s a shock which does no al e TFP
in he e y long- un. The wo co-linea shocks a e consis en wi h he o me bu inconsis en
wi h he la e . Hence, BP conjec u e ha hey ep esen a echnological inno a ion which
a ec s TFP wi h conside able delay. Howe e , his echnological inno a ion a ec s s ock
p ices immedia ely and may he e o e cause expec a ions-d i en luc ua ions in consump ion
and in es men .
The idea o a p ominen ole o echnology- ela ed news in mac oeconomic luc ua ions has
ecen ly gi en ise o qui e a ew o he pape s, e. g. Lo enzoni (2006) and Jaimo ich and
Rebelo (2006). I is he e o e e y in e es ing o in es iga e i he empi ical inding o BP in
a o o news-d i en business cycles is a obus business cycle ac which can be documen ed
o o he coun ies and samples as well. Mo eo e , one would like o know i he e is any kind
o di ec e idence which suppo s he in e p e a ion o he iden i ied shocks as being
echnology shocks.
In he i s line o esea ch, Beaud y and Po ie (2005) ook he lead by epea ing hei
analysis wi h Japanese da a. He e hey came up wi h essen ially he same inding as o he
US: Two almos co-linea “ echnology” shocks unde al e na i e iden i ying assump ions. In
his pape , we look a Ge many as a hi d coun y and p esen a simila , bu sligh ly weake
piece o e idence: The e is e idence o a g adually inc easing esponse o TFP o ce ain
shocks in excess o a clea ly posi i e e ec on impac . The e o e, i he iden i ied shocks a e
indeed echnological, he o e all e idence is qui e suppo i e o a s ylized business cycle ac
o delayed TFP esponse o echnology shocks.
Going u he , we es whe he he iden i ied shocks a e igh ly conside ed as echnology
shocks by con on ing hem wi h da a on pa en s g an ed by o applied o a he Ge man
pa en agency. Fo a ious measu es o TFP and pa en s, we ha e e y obus e idence ha
2
he iden i ied shocks G ange -cause pa en s. Con e sely, he iden i ied non-pe sis en shock
in he SVAR-app oach is no G ange -causal in any o he speci ica ions we es . This seems
o be ai ly s ong e idence o he hypo hesis ha he iden i ied shock, which a ec s TFP on
impac and wi h a delay, is indeed a echnology shock.
The sequel o he pape is o ganized as ollows: In sec ion 2, we illus a e ha he BP
app oach can be used o in e key model p ope ies. We p esen a modi ied Long and Plosse
(1983) model which allows o mul i-pe iod usage o capi al goods. We compu e s ock p ices
as he discoun ed sum o expec ed e u ns o capi al. We hen show ha a s anda d
speci ica ion o TFP shocks has e y di e en implica ions o he shocks iden i ied in he
Blancha d-Quah app oach han a delayed-implemen a ion speci ica ion. In sec ion 3, we
b ie ly illus a e he econome ic app oach and apply i o Ge man da a. We use h ee di e en
measu es o TFP in o de o check he obus ness o ou esul s. Sec ion 4 analyzes G ange -
causali y be ween he iden i ied shocks and di e en measu es o pa en s. Sec ion 5
concludes.
2 The Model
We will illus a e he po en ial o he BP app oach by conside ing wo e sions o he same
model: One wi h a s anda d, ins an aneous eac ion o TFP o a echnological inno a ion, he
o he wi h a delayed esponse. This is simila o BP (2005). Howe e , hei model assumes
100% dep ecia ion on physical capi al, which makes i di icul o model s ock p ices, since,
essen ially, i ms shu down each pe iod. Hence, BP (2005) do no conside s ock p ices bu
a he ocus on bonds whose p ice is in e sely ela ed o he e u n on he (one-pe iod) capi al
goods. By con as , we use a model whe e he p oduc i e use o capi al goods ex ends o e
many pe iods and s ock p ices a e compu ed as he discoun ed sum o expec ed e u ns o
capi al.
The model is aken om Long and Plosse (1983). We agg ega e hei model o jus a single
sec o , bu ex end i s p oduc ion echnology o a mul i-pe iod se ing. Speci ically, in es men
goods
I
can be used o p+1 pe iods un il hey a e comple ely wo n ou . The p oduc ion
elas ici ies o in es men o pe iod -
τ
is gi en by a
τ
and we can allow o any kind o
dep ecia ion schedule by secu ing 1
aa p
ττ
τ
+
>∀<. Labo inpu is L wi h p oduc ion
elas ici y b>0, so ha cons an e u ns imply 01
pab
τ
τ
=
+
=
∑
. 1
+
Λ
is TFP o pe iod +1 and
p oduc ion is gi en by
11
0
p
a
b
YLI
τ
τ
τ
+
+
=
=Λ −
∏
(1)
o (using small le e s o deno e logs)
1
0
p
ybl ai
ττ
τ
1
λ
+
−
=
=+ +
∑+
1
. (2)
The ep esen a i e agen has a s anda d in e empo al u ili y unc ion wi h subjec i e discoun
ac o 0
β
<<
and isk a e sion cap u ed by 1
σ
>. 2
e
η
θ
= is a s a iona y p e e ence shock
and he inno a ion 2
η
is, o simplici y, whi e noise wi h uni a iance.
3
00
ln
L
UE C
σ
βθ
σ
∞
=
⎡
⎤
⎛⎞
=−
⎢
⎥
⎜⎟
⎝⎠
⎣
⎦
∑
Maximizing u ili y unde he budge cons ain
CIY
+
= (3)
yields i s o de condi ions
1
1
00
11
1:
pp
II
aE aE
CC
ττ
τ
τ
ττ
ττ
βγβ
++
++ ++
==
++ ++
1
1
I
C
τ
τ
⎡
⎤⎡
=+=+ ⎤
⎢
⎥⎢⎥
⎣
⎦⎣
∑∑
⎦
(4)
and
1
1
1
I
LbEC
σ
θβ
+
+
⎡
⎤
=+
⎢
⎥
⎣
⎦. (5)
Imposing s abili y, sol ing (4) o wa d and using (3) yields
(
)
1,
CYI
Y
γ
γ
=− = . (6)
This is he policy unc ion, since is a s a e a iable. Fo labo , we compu e
Y
2
1
ln ln1
b
lL
β
η
σγ
⎛
== −
⎜
−
⎝⎠
⎞
⎟
Y
. (7)
Inse ing (6) in o (1) we ge
11 0
p
a
ab
YL
τ
τ
τ
γ
+
+
=
=Λ −
∏
, (8)
whe e
0
:p
aa
τ
τ
=
=∑. Se ing
()
:ln ln
1
bb
a
β
κγ
σ
γ
=+
−
and aking logs we ge :
()
1121
0
:
p
b
yay aLy
ττ
τ
12
b
κ
ληκ λ
σ
+−+ ++
=
=+ + − =+ + −
∑
η
σ
(9)
() ()
21
21
:
11 1
b
b
yA
aaL a
λη
κκ
σλη
σ
−
−
⎛⎞
−
⎜⎟ ⎛
⎝⎠
=+ =+ −
⎜
−− − ⎝⎠
L
⎞
⎟
, (10)
whe e a e polynomials in he lag ope a o L and
() ()
,aL AL
(
)()
00,0aA1
=
=.
Ne p o i s a e ou pu minus labo and in es men cos s:
()
1
11 1
:1
Y
YLIb
L
γ
+
++ + 1
Y
+
∂
Π= − − =−−
∂ (11)
4

Le
1
:
SP E
τ
τ
τ
β
∞
+
=
⎡
=Π
⎢
⎣⎦
∑⎤
⎥
]
1+
be he s ock p ice, i. e. he discoun ed sum o p o i s expec ed in
pe iod . By de ini ion, we ha e
[] [] [
111
11
:
SP E E E E E SP
ττ
ττ
ττ
ββ ββ β β
∞∞
++ +++
==
⎡⎤ ⎡ ⎤
= Π=Π+ Π=Π+
⎢⎥ ⎢ ⎥
⎣⎦ ⎣ ⎦
∑∑ .
Log-linea izing his equa ion we ge :
()
()
()
() ()
() ( ) ()
()
11
1
0
11
0
12 1
0
1
1
1
:1
:1
sp cns E E sp
cns E
b
cns E A L
cns L E A L
τ
τ
τ
τ
ττ
τ
τ
τ
τ
βπ β
ββπ
β
ββ λ η
σ
δη β β λ
++
∞
++
=
∞
++ +
=
∞
++
=
≈+− +
⎛⎞
=+−
⎜⎟
−⎝⎠
⎛
⎛
=+− −
⎜⎟
⎜⎟
⎝⎠
⎝⎠
=− +−
∑
∑
∑
2
,
⎞
⎞
(12)
whe e .
()
00
δ
≠
Le us now conside wo di e en speci ica ions o he s ochas ic p ocesses d i ing he
model. The s anda d speci ica ion would be a andom walk o log TFP:
11
λ
λη
−
=
+ (13)
He e, 1
η
is assumed o be whi e noise wi h uni a iance.
The al e na i e speci ica ion would speci y a delayed esponse o log TFP o pe manen
echnological inno a ions. Assume ha log TFP is he sum o a andom walk componen
ζ
and a s a iona y p ocess . The e a e o hogonal, uni - a iance-whi e noise inno a ions 1
η
and
3
η
o
ζ
and , espec i ely. The 3
η
inno a ion a ec s log TFP in he same pe iod in which i
becomes known, while we assume ha he 1
η
inno a ion a ec s TFP wi h a delay o one
pe iod. Thus, he s ochas ics a e desc ibed by
1
11
13
,1
λζ
ζζ η
ρηρ
−
−
−
=
+
=+
=
+<
(14)
To p ese e he same numbe o shocks as unde he s anda d speci ica ion, we assume ha
he e is no p e e ence shock in he delayed- esponse speci ica ion, 20
η
=
∀.
Unde he s anda d speci ica ion we de i e om (12)
5
(
)
(
)
()( ) ()( )
() ()( )
12
2
12
11
1,
sp cns L A L
sp A L L L L
AL L L
δη λ
λ
δη
ηδ η
≈− +
Δ≈ − − −
=−−



whe e
()
A
L
 is a lag polynomial wi h
(
)
(
)
00 1
A
A≠≠

.
Hence, he mo ing a e age ep esen a ion is gi en by
() ()( ) ()
1
1
22
10 :
1
CL
AL L L
sp
1
λ
ηη
δ
η
η
Δ⎛⎞
⎛ ⎞ ⎛⎞ ⎛⎞
=
⎜⎟
⎜ ⎟ ⎜⎟ ⎜
−−
Δ
⎝ ⎠ ⎝⎠ ⎝⎠
⎝⎠
=
⎟
. (15)
Unde he delayed- esponse speci ica ion we know
(
)
(
)
() ()
2
23
1
sp cns A L B L
BL
cns A L
L
ζ
ζη
ρ
≈+ +
=+ +
−



wi h . The mo ing a e age ep esen a ion o he i s di e ences is hen
gi en by
() ()
00 1B≠≠

B

(
)
() ()
()
()
1
2
33
1
1:
1
1
L
LLCL
sp BL
AL L
L
1
λ
ηη
ρ
η
η
ρ
⎛−⎞
⎜⎟
Δ−
⎛ ⎞ ⎛⎞ ⎛⎞
⎜⎟
=
⎜ ⎟ ⎜⎟ ⎜
⎜⎟
Δ
⎝ ⎠ ⎝⎠ ⎝⎠
⎜⎟
−
⎜⎟
−
⎝⎠


=
⎟
. (16)
F om (15) and (16) we in e ha he i s ow o bo h
(
)
11C and
(
)
21C is
(
, while he
i s ow o and a e e y di e en :
)
1, 0
()
10C
()
20C
(
)
1, 0 and
(
)
0,1 , espec i ely. This implies
ha he iden i ica ion o he s uc u al shocks gi es ise o di e en pa e ns, oo, and hese
pa e ns can be used o in e wha he ue unde lying model is. We will discuss his in de ail
in he nex sec ion.
3 The Econome ic App oach
Conside empi ical ime se ies o log TFP and log s ock p ices, deno ed
λ
and as be o e.
We assume hese a e in eg a ed o o de one and coin eg a ed wi h each o he , i. e.
is I(0). Using Wold’s decomposi ion heo em,
sp
(
,
sp
λ
ΔΔ
)
'
(
)
,
sp
λ
ΔΔ '
⎞
⎟
⎠
can be w i en in
educed o m
wi h
()
1
2
u
CL
sp u
λ
Δ
⎛⎞ ⎛
=
⎜⎟ ⎜
Δ
⎝⎠ ⎝ 1
(): i
i
i
CL I CL
∞
=
=+
∑
(17)
and in s uc u al o m
6
()
1
2
DL
sp
λ
ε
ε
Δ
⎛⎞ ⎛
=
⎜⎟ ⎜
Δ
⎝⎠ ⎝
⎞
⎟
⎠
wi h . (18)
0
(): i
i
i
DL DL
∞
=
=∑
Iden i ying he s uc u al shocks
ε
equi es knowledge o D0. This ma ix can be eco e ed
om he es ima ed
(
)
CL ma ices o he educed o m (17), i one es ic ion is imposed on
he pa ame e s o
(
)
D
L. We ollow Beaud y and Po ie (2005, 2006) by using wo
al e na i e assump ions, which we call he sho - un and he long- un es ic ion. The o me
pos ula es ha he (1,2) elemen o
(
)
0D is ze o, i. e. he s ock ma ke shock 2
ε
has no
e ec on TFP on impac . The la e pos ula es ha he (1,2) elemen o is ze o, i. e. he
s ock ma ke shock
()
1D
2
ε
has no long- un e ec on TFP. Le us hink o (18) as he
ep esen a ion ob ained unde he sho - un es ic ion and le (19) be he ep esen a ion
ob ained unde he long- un es ic ion:
1,
2,
()
DL
sp
ε
λ
ε
Δ⎛⎞
⎛⎞
=⋅
⎜
⎜⎟
Δ
⎝⎠ ⎝⎠


⎟
wi h
0
(): i
i
i
DL D L
∞
=
=
⋅
∑

(19)
I he empi ical da a we e gene a ed by he model o sec ion 2 wi h s anda d speci ica ion, i. e.
by equa ion (15), he impac ma ix
() () ()
1
10
000
CA
δ
⎛⎞
=⎜⎟
−
⎝⎠

would al eady ul ill he sho - un iden i ying assump ion, hence, as s uc u al shocks we
would iden i y 11 2
,2
ε
ηεη
==. On he o he hand, he long- un ma ix is
() ()
1
10
110
CA
⎛⎞
=⎜⎟
⎝⎠
,
hus, we would immedia ely ha e 11 2
,2
ε
ηεη
=
=

. The impo an poin is ha unde bo h
iden i ying assump ions we would ind he same esul o 1
ε
and 1
ε
.
I , con e sely, he empi ical da a we e gene a ed by he delayed echnology speci ica ion, he
impac ma ix would be
() () ()
2
01
000
CAB
⎛⎞
=⎜⎟
⎝⎠
.
In his case, he iden i ying assump ions imply ha 13 2
,
1
ε
ηεη
=
=, whe eas unde he
long- un es ic ion we ha e
() ()
2
10
110
CA
⎛⎞
=⎜⎟
⎝⎠

7
and hence 11 2
,
3
ε
ηεη
==

. Unde his model, we would hus no ind he same esul o
1
ε
and 1
ε
, bu a he we would ind ha 21
ε
ε
=
. The e o e, i he empi ical analysis sugges s
ha 21
ε
ε
≈ we may in e ha a model wi h delayed echnology esponse is mo e app op ia e
han a s anda d speci ica ion. We now u n o an in es iga ion o his issue o Ge man da a.
3.1 Da a desc ip ion
As in Beaud y and Po ie (2006), h ee di e en TFP a iables a e calcula ed: he s anda d
Solow esidual, he Solow esidual adjus ed o a iable capi al u iliza ion and a TFP measu e
ollowing he me hodology o G o h e al. (2004) and Oul on (2001).
We ha e qua e ly da a om 1970(1) o 2005(2). The simple TFP measu e (wi hou capi al
u iliza ion) is compu ed om da a on GDP, hou s wo ked and annual capi al s ock da a
in e pola ed wi h cons an wi hin yea qua e ly g ow h a es. Unde he assump ion o
cons an e u ns o scale, he obse ed qua e ly labo sha e is used as he p oduc ion elas ici y
o labo . The log o his measu e is deno ed TFP_D1. Modi ying he capi al s ock da a by
mul iplying wi h he capaci y u iliza ion a e in manu ac u ing gi es a second measu e o TFP
whose log is deno ed TFP_D2.
Compu a ion o he hi d TFP measu e (TFP_D3) akes se e al c i icisms o he s anda d
Solow esidual in o accoun . Quali y aspec s a e conside ed when measu ing labo inpu .
Unde he assump ion o pe ec compe i ion, he quali y o wo k is e lec ed by wages.
The e o e, quali y adjus ed labo inpu L can be cons uc ed as
1
1
ˆ
ˆ2
n
i i
i
ss
i
L
h
−
=
+
⎛⎞
=
⋅
⎜⎟
⎝⎠
∑, (20)
whe e ha ed a iables a e g ow h a es, n cons i u es he numbe o employee ca ego ies, si
desc ibes ou pu con ibu ion and hi wo king hou s o g oup i in pe iod . In he case o
Ge many, ele an da a in e ms o g oss ea nings exis o sala ied employees o he se ice
and manu ac u ing sec o s, and in e ms o g oss wages o wage ea ne s o manu ac u ing
and ag icul u e. F om mic oda a, ou ca ego ies o labo inpu can be dis inguished.
The concep o capi al inpu does no e e o he capi al s ock bu uses a measu e o capi al
se ices. Di e en ypes o asse s a e weighed by hei en al p ices o ep esen he alue o
se ices which can be ealized a pe ec compe i ion. Ren al p ices MP o asse ype j in
pe iod a e, in p inciple, compu ed as
,, , ,
,
ˆ
j j j j j
j
M
PT p p
δ
⎡
⎤
⎛⎞
=⋅ +− ⋅
⎜⎟
⎢
⎥
⎝⎠
⎣
⎦, (21)
whe e j, a e oppo uni y cos s, δj is dep ecia ion and Tj, exp esses axa ion and in es men
allowances1.
1 As in Oul on (2001) dwellings a e excluded om buildings because dwellings may no con o m wi h s ic
p o i maximizing beha io . Dep ecia ion a es o machine y and buildings a e assumed o be 13% and
2.5%. Ma ke p ices o bo h asse s esul om he a io o nominal and eal alues o he espec i e g oss
ixed capi al o ma ion. Ra es o e u n a e compu ed by he a io o g oss ope a ing su plus and capi al s ock
alue. The ax ac o is dis ega ded due o lack o adequa e da a.
8
hence many o he applica ions do appa en ly no ep esen in en ions. Also, PAG is e y
s ongly skewed due o a ew obse a ions in he la e 1990s, when pa en applica ions we e
inc easing emendously. The e o e, any kind o in e ence is e y di icul o PAG and we
ha e ocused on PAT. We do no epo he esul s in de ail, bu hey a e a ailable upon
eques . Su ice i o say ha no G ange -causali y can be ound be ween PAT and any o he
iden i ied shocks. This is wha one would expec i in en o s y o keep hei in en ions
sec e as long a possible, bu he in en ions a e disclosed a e submi ing he pa en
applica ion. (Recall ha he ime lag be ween submission and disclosu e is a mos 18 mon hs,
i i is sho e , i may well all wi hin he same yea .)
One migh c i icize ou p ocedu e o simply agg ega ing qua e ly shocks o yield annual
equency. In o de o check o a possible empo al agg ega ion bias, we es ima e he
bi a ia e VARs also wi h annual da a. Mo eo e , he equency con e sion may se e as a es
o he obus ness o ou esul s. We hus ecalcula e he ime se ies TFP_D1b, TFP_D2b,
TFP_D3b and DAX1b wi h annual da a.
Looking a he esul s o he Johansen ace es , we now do no ind e idence o
coin eg a ion, in ma ked con as o ou esul s wi h qua e ly da a. This is an immedia e
con adic ion, since coin eg a ion o he qua e ly se ies would imply ha he annual se ies
a e also coin eg a ed. In iew o he ela i ely low p- alues o he Johansen es s a is ics o
annual da a one may hus suspec ha he Johansen es s o annual da a su e om low
powe . We he e o e e e se he es p ocedu e by es ing o he null hypo hesis o
coin eg a ion using he Shin (1994) es . This es is also no able o ejec he null hypo hesis
and his inding is obus o a ious choices o he unca ion lag. Consequen ly, he e is s ill
no con incing e idence agains he iew ha TFP and s ock ma ke p ices a e coin eg a ed.
We he e o e edo he analysis in comple e analogy o ou handling o qua e ly da a. All
ele an esul s o VECM es ima ion and impulse esponse analysis a e p esen ed in he
appendix, as hey a e quali a i ely e y simila o he qua e ly esul s. The esul ing annual
s uc u al esiduals can hen be used in G ange causali y es s which a e no subjec o
empo al agg ega ion bias. The esul s a e gi en in Tables 3 and 4. Basically, he conclusions
om he ea lie es s a e unal e ed. In mos cases, he iden i ied echnology shocks a e
G ange -causal o g an ed pa en s and in he ew cases whe e his is no he case, he p- alues
a e e y close o 5%. The con e se is no ue, i. e. nowhe e a e g an ed pa en s G ange -
causal o he echnology shocks. Mo eo e , he e is no causali y in ei he di ec ion be ween
g an ed pa en s and he non- echnology shocks. Hence he a ailable indica es ha he SVAR
analysis co ec ly iden i ies echnology and non- echnology inno a ions o he Ge man
economy.
15

Table 3: G ange Causali y Tes s (iden i ied shocks om annual VECM)
Shocks suspec ed o be echnological
PGG PGT
ε
20.060 0.895 0.037 0.313
TFP_D1b
1
ε
 0.051 0.814 0.036 0.632
ε
20.050 0.731 0.029 0.223
TFP_D2b
1
ε
 0.015 0.826 0.016 0.263
ε
20.028 0.662 0.033 0.142
TFP_D3b
1
ε
 0.020 0.372 0.067 0.052
Uppe le co ne : P- alue o null: Row a iable does no G ange -cause column a iable.
Lowe igh co ne : P- alue o null: Column a iable does no G ange -cause ow a iable.
Table 4: G ange Causali y Tes s (iden i ied shocks om annual VECM)
Shocks suspec ed o ha e no pe manen e ec on TFP
PGG PGT
ε
10.397 0.836 0.351 0.446
TFP_D1b
2
ε
 0.417 0.999 0.3800 0.945
ε
10.136 1.000 0.190 0.701
TFP_D2b
2
ε
 0.636 0.762 0.463 0.488
ε
10.221 0.500 0.421 0.236
TFP_D3b
2
ε
 0.357 0.880 0.281 0.687
Uppe le co ne : P- alue o null: Row a iable does no G ange -cause column a iable.
Lowe igh co ne : P- alue o null: Column a iable does no G ange -cause ow a iable.
5 Conclusions
The pu pose o his s udy was o p o ide u he e idence on he BP-hypo hesis o delayed-
echnology di usion and news-d i en business cycles. Fo Ge man da a on TFP and s ock
p ices we ind quali a i ely he same esul as BP do: A high co ela ion be ween a shock wi h
pe manen e ec s on TFP in he long un and – unde a di e en iden i ica ion scheme - a
shock which has an immedia e e ec on s ock p ices bu does no a ec TFP on impac .
The co ela ion is less p onounced, hough, as in BP’s analysis o US and Japanese da a.
Also, he impulse esponse analysis sugges s ha o Ge many a subs an ial pa o he o al
TFP esponse is immedia e a he han delayed. Using a quali y-adjus ed measu e o TFP,
he e is almos no delayed di usion any mo e. This sugges s ha he delayed di usion is
16
con ined o embodied echnological change. Disembodied echnological inno a ions seem o
ha e immedia e e ec s on TFP. Bu since he sha e o disembodied echnological p og ess in
o al echnological p og ess may be small, he ela i ely la ge size o he quali y-adjus ed TFP
measu e sugges s, ha each measu e o Ge man TFP may ac ually con ain a ai ly la ge pa
o unexplained non- echnology in luences, i. e. ou igno ance abou he ue na u e o wha
we measu e as TFP may be ai ly la ge. I may he e o e be he case ha noise in TFP da a is
esponsible o he immedia e eac ion o s anda d TFP measu es in he impulse esponse
analysis.
Gi en possibly noisy da a on TFP, we hen checked how well he iden i ica ion o echnology
shocks in he SVAR app oach wo ked. The answe seems o be: Su p isingly well. Shocks
suspec ed o be echnology shocks a e G ange -causal o he numbe o pa en s g an ed by
he Ge man pa en agency, while shocks wi hou pe manen e ec on echnology a e no . This
esul is e y obus ac oss di e en speci ica ions, measu es and iden i ica ion schemes. I
may he e o e be he case ha he SVAR app oach is able o sepa a e he ue, pe manen
echnology shocks om ansi o y noise which also a ec s measu ed TFP. Unde his
in e p e a ion, ou esul s a e qui e suppo i e o BP’s news-d i en business cycle
hypo hesis.
17
6 Appendix
Resul s o qua e ly da a:
Table A1
ADF es
Va iable Le el /
1s di . Lags Tes
s a is ics C i ical
alue (5%) p- alue
Le el 0 -2.55 -3.44 0.3041
TFP_D1b 1s di . 0 -14.00 -3.44 0.0000
Le el 1 -2.64 -3.44 0.2643
TFP_D2b 1s di . 0 -15.86 -3.44 0.0000
Le el 1 -1.70 -3.44 0.7483
TFP_D3b 1s di . 0 -15.11 -3.44 0.0000
Le el 0 -2.60 -3.44 0.2810
DAX1b 1s di . 0 -10.34 -3.44 0.0000
Table A2
Johansen ace es
Va iables Lag leng h
(1s di .)
T end in e o
co ec ion
e m (EC) /
O hogonal
T end (OT)
Hypo hesis Tes
s a is ics
C i ical
alue
(5%) p- alue
= 0 16.24 15.49 0.0385
OT = 1 1.08 3.84 0.2991
TFP_D1b
& DAX1b 0 EC = 0 19.69 25.87 0.2420
= 0 19.29 15.49 0.0128
OT = 1 2.08 3.84 0.1491
TFP_D2b
& DAX1b 1 EC = 0 24.10 25.87 0.0817
= 0 15.57 15.49 0.0487
OT = 1 1.58 3.84 0.2089
TFP_D3b
& DAX1b 0 EC = 0 19.78 25.87 0.2372
18
Es ima ion esul s o coe icien s in VECM {TFP_D1b, DAX1b}1
()
11
12
_1 _1
0.021* 0.046*
1.000 0.220*
11
0.203* 0.399*
TFP D b TFP D b u
cns
D
AX b DAX b u
−
−
Δ−
⎛⎞ ⎛⎞
⎛⎞ ⎛⎞
=− +
⎜⎟ ⎜⎟
⎜⎟ ⎜⎟
Δ−
⎝⎠ ⎝⎠
⎝⎠ ⎝⎠
⎛⎞
+
⎜⎟
⎝⎠
Tes ype Tes
s a is ics p- alue
Au oco . 65.91 0.3432
He e osc. 9.99 0.9322
Non-
No mal. 69.54 0.0000
Implied SVAR coe icien s1
sho - un es ic ion: long- un es ic ion:
0
0.0080* 0.0000
0.0003 0.0979*
D⎛⎞
=⎜⎟
−
⎝⎠
i
00.0049* 0.0063*
0.0766* 0.0609*
D−
⎛⎞
=⎜⎟
⎝⎠
1 *=signi ican a he 5% le el.
19
Es ima ion esul s o coe icien s in VECM {TFP_D2b, DAX1b}1
()
1
1
_2 _2
0.023* 1.000 0.187*
11
0.194*
TFP D b TFP D b
DAX b DAX b
−
−
Δ−
⎛⎞ ⎛
⎛⎞
=−
⎜⎟ ⎜
⎜⎟
Δ⎝⎠
⎝⎠ ⎝
⎞
⎟
⎠
11
12
_2
0.287* 0.000 0.046*
1
2.241* 0.141 0.346*
TFP D b u
cns
D
AX b u
−
−
Δ
−⎛⎞
⎛⎞ ⎛⎞
++
⎜⎟
⎜⎟ ⎜⎟
Δ−
⎝⎠ ⎝⎠
⎝⎠
⎛⎞
+
⎜⎟
⎝⎠
Tes ype Tes
s a is ics p- alue
Au oco . 55.33 0.5753
He e osc. 16.34 0.5690
Non-
No mal. 58.09 0.0000
Implied SVAR coe icien s1
sho - un es ic ion: long- un es ic ion:
0
0.0081* 0.0000
0.0075 0.0947*
D⎛⎞
=⎜⎟
⎝⎠
i
00.0051* 0.0063*
0.0783* 0.0537*
D−
⎛⎞
=⎜⎟
⎝⎠
Impulse- esponse unc ions2
a) Sho - un es ic ion ( esponse o ε2):
b) Long- un es ic ion ( esponse o 1
ε
):
1 *=signi ican a he 5% le el.
2 Con idence in e als a e ob ained by 2500 eplica ions wi h he boo s apping p ocedu e o Hall (1992). They
ep esen he 95% quan iles.
20

Es ima ion esul s o coe icien s in VECM {TFP_D3b, DAX1b}1
()
11
12
_3 _3
0.033* 0.060*
1.000 0.186*
11
0.258* 0.436*
TFP D b TFP D b u
cns
D
AX b DAX b u
−
−
Δ−
⎛⎞ ⎛⎞
⎛⎞ ⎛⎞
=− +
⎜⎟ ⎜⎟
⎜⎟ ⎜⎟
Δ−
⎝⎠ ⎝⎠
⎝⎠ ⎝⎠
⎛⎞
+
⎜⎟
⎝⎠
Tes ype Tes
s a is ics p- alue
Au oco . 74.70 0.1292
He e osc. 14.12 0.7215
Non-
No mal. 58.89 0.0000
Implied SVAR coe icien s1
sho - un es ic ion: long- un es ic ion:
0
0.0129* 0.0000
0.0021 0.0974*
D⎛⎞
=⎜⎟
⎝⎠
i
00.0094* 0.0088*
0.0681* 0.0697*
D−
⎛⎞
=⎜⎟
⎝⎠
1 *=signi ican a he 5% le el.
21
Resul s o annual da a:
Table A3
ADF es
Va iable Le el /
1s di . Lags Tes
s a is ics C i ical
alue (5%) p- alue
Le el 1 -2.16 -3.55 0.4970
TFP_D1b 1s di . 0 -3.12 -2.95 0.0344
Le el 1 -2.38 -3.55 0.3847
TFP_D2b 1s di . 0 -2.75 -2.95 0.0763
Le el 0 -2.04 -3.55 0.5578
TFP_D3b 1s di . 0 -5.01 -2.95 0.0003
Le el 1 -3.01 -3.55 0.1437
DAX1b 1s di . 0 -3.94 -2.95 0.0048
Table A4
Johansen ace es
Va iables Lag leng h
(1s di .)
T end in e o
co ec ion
e m (EC) /
O hogonal
T end (OT)
Hypo hesis Tes
s a is ics
C i ical
alue
(5%) p- alue
OT = 0 12.00 15.49 0.1570 TFP_D1b
& DAX1b 1 EC = 0 18.32 25.87 0.3227
OT = 0 12.29 15.49 0.1435
TFP_D2b
& DAX1b 1 EC = 0 17.52 25.87 0.3769
OT = 0 14.39 15.49 0.0729 TFP_D3b
& DAX1b 0 EC = 0 17.93 25.87 0.3485
22
Table A5
Shin es (LM es s a is ics o TFP_D1b & DAX1b)
Lagged di e ences
2 4 6
2 0.140 0.116 0.054
4 0.112 0.103 0.061
Lag
unca ions 8 0.127 0.124 0.160
C i ical alue a he 5% le el: 0.314
No linea end assump ion unde null hypo hesis
Table A6
Shin es (LM es s a is ics o TFP_D2b & DAX1b)
Lagged di e ences
2 4 6
2 0.138 0.109 0.049
4 0.112 0.096 0.072
Lag
unca ions 8 0.127 0.125 0.113
C i ical alue a he 5% le el: 0.314
No linea end assump ion unde null hypo hesis
Table A7
Shin es (LM es s a is ics o TFP_D3b & DAX1b)
Lagged di e ences
2 4 6
2 0.127 0.062 0.042
4 0.103 0.057 0.043
Lag
unca ions 8 0.131 0.117 0.165
C i ical alue a he 5% le el: 0.314
No linea end assump ion unde null hypo hesis
23
Es ima ion esul s o coe icien s in VECM {TFP_D1b, DAX1b}1
()
1
1
_1 _1
0.035 1.000 0.226*
11
0.700*
TFP D b TFP D b
DAX b DAX b
−
−
Δ−
⎛⎞ ⎛
⎛⎞
=−
⎜⎟ ⎜
⎜⎟
Δ⎝⎠
⎝⎠ ⎝
⎞
⎟
⎠
11
12
_1
0.418* 0.011 0.079*
1
2.601 0.346* 1.336*
TFP D b u
cns
D
AX b u
−
−
Δ
⎛⎞
⎛⎞ ⎛⎞
++
⎜⎟
⎜⎟ ⎜⎟
Δ
−−
⎝⎠ ⎝⎠
⎝⎠
⎛⎞
+
⎜⎟
⎝⎠
Tes ype Tes
s a is ics p- alue
Au oco . 8.40 0.5894
He e osc. 6.75 0.6636
Non-
No mal. 2.37 0.6680
Implied SVAR coe icien s1 Iden i ied shocks
sho - un es ic ion:
0
0.0084* 0.0000
0.0296 0.1492*
D⎛⎞
=⎜⎟
⎝⎠
long- un es ic ion:
i
00.0067* 0.0051*
0.1134* 0.1013*
D−
⎛⎞
=⎜⎟
⎝⎠
Impulse- esponse unc ions2
a) Sho - un es ic ion ( esponse o ε2):
b) Long- un es ic ion ( esponse o 1
ε
):
1 *=signi ican a he 5% le el.
2 Con idence in e als a e ob ained by 2500 eplica ions wi h he boo s apping p ocedu e o Hall (1992). They
ep esen he 95% quan iles.
24