Monetary policy transmission under global versus local geopolitical risk: Exploring time-varying granger causality, frequency domain, and nonlinear territory in Tunisia

T abelsi, Emna
A icle
Mone a y policy ansmission unde global e sus local geopoli ical
isk: Explo ing ime- a ying g ange causali y, equency domain, and
nonlinea e i o y in Tunisia
Economies
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MDPI – Mul idisciplina y Digi al Publishing Ins i u e, Basel
Sugges ed Ci a ion: T abelsi, Emna (2025) : Mone a y policy ansmission unde global e sus local
geopoli ical isk: Explo ing ime- a ying g ange causali y, equency domain, and nonlinea
e i o y in Tunisia, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 7, pp. 1-68,
h ps://doi.o g/10.3390/economies13070185
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Re ised: 24 Feb ua y 2025
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Published: 27 June 2025
Ci a ion: T abelsi, E. (2025). Mone a y
Policy T ansmission Unde Global
Ve sus Local Geopoli ical Risk:
Explo ing Time-Va ying G ange
Causali y, F equency Domain, and
Nonlinea Te i o y in Tunisia.
Economies,13(7), 185. h ps://
doi.o g/10.3390/economies13070185
Copy igh : © 2025 by he au ho .
Licensee MDPI, Basel, Swi ze land.
This a icle is an open access a icle
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licenses/by/4.0/).
A icle
Mone a y Policy T ansmission Unde Global Ve sus Local
Geopoli ical Risk: Explo ing Time-Va ying G ange Causali y,
F equency Domain, and Nonlinea Te i o y in Tunisia
Emna T abelsi 1,2
1Social and Economic Policy Analysis Labo a o y, Highe Ins i u e o Managemen o Tunis, Uni e si y o
Tunis, Tunis 2000, Tunisia; [email p o ec ed]
2Facul y o Economic and Managemen Sciences o Sousse, Uni e si y o Sousse, Sousse 4023, Tunisia
Abs ac
Using ime- a ying G ange causali y, Neu al Ne wo ks Nonlinea VAR, and Wa ele
Cohe ence analysis, we e idence he uns able e ec o he money ma ke a e on indus ial
p oduc ion and consume p ice index in Tunisia. The e ec is asymme ic and depends
on geopoli ical isk (low e sus high). We show ha global geopoli ical isk has bo h
de imen s and bene i s sides—i is a h ea and an oppo uni y o mone a y policy
ansmission mechanisms. In e ac ed local p ojec ions (LPs) e eal sho –medium- e m
ola ili y o dampening e ec s, sugges ing ha geopoli ical unce ain y migh weaken he
immedia e impac o mone a y policy on ou pu and p ices. In unce ain en i onmen s
(e.g., high geopoli ical isk), economic agen s—households and businesses—may adop a
wai -and-see app oach. They delay consump ion and in es men decisions, which could
ini ially mu e he impac o mone a y policy. Agen s may delay hei esponses un il
hey gain mo e in o ma ion abou geopoli ical de elopmen s. Once cla i y eme ges, hey
may adjus hei beha io , aligning wi h he long- un e ec s obse ed in he Vec o E o
Co ec ion Model (VECM). Fu he mo e, we iden i y an exace ba ing in es o sen imen
ollowing igh ening mone a y policy, du ing global and local geopoli ical episodes. The
impac is e en mo e p onounced unde condi ions o high domes ic weakness. E idence is
ex ac ed h ough a no el composi e index ha we cons uc using P incipal Componen
Analysis (PCA). Ou esul s ha e implica ions o he Cen al Bank’s mone a y policy
conduc and communica ion p ac ices.
Keywo ds: geopoli ical isk; mone a y policy; p ecau iona y beha io ; in es o sen imen ;
nonlinea e ec ; asymme ic impac
1. In oduc ion
Since he ea ly 2000s, he global economy has become inc easingly in e connec ed,
making i mo e ulne able o he ipple e ec s o geopoli ical and economic e en s. Majo
inciden s such as he 9/11 a acks, he ini ial con lic in Uk aine, he ongoing US–China
ade ensions, he COVID-19 pandemic, and he mo e ecen wa in Uk aine ha e high-
ligh ed he in e connec ed na u e o economies wo ldwide. These e en s ha e no only
dis up ed egional s abili y, bu ha e also igge ed signi ican shi s in he global business
and inancial cycles. Kose e al. (2003), Mon o e al. (2003), Cicca elli and Mojon (2010),
Mi anda-Ag ippino and Rey (2020), and Ginn (2023a,2023b,2023c) emphasized he syn-
ch onized na u e o global economic ac i i ies and inancial ma ke s, e ealing how shocks
Economies 2025,13, 185 h ps://doi.o g/10.3390/economies13070185
Economies 2025,13, 185 2 o 68
in one pa o he wo ld can p opaga e apidly ac oss bo de s. Policymake s play a c ucial
ole in mi iga ing he ad e se e ec s o global shocks. They o en eso o accommoda i e
mone a y policies—such as lowe ing in e es a es o inc easing liquidi y— o s abilize hei
economies and coun e ac nega i e impac s on g ow h and employmen . These measu es
help bu e domes ic ma ke s agains ex e nal dis up ions, bu equi e ca e ul calib a ion o
a oid long- e m consequences, such as in la iona y p essu es o inancial ins abili y. As
globaliza ion deepens, he need o coo dina ed in e na ional policy esponses becomes
inc easingly e iden in e ec i ely add essing challenges ha anscend na ional bounda ies
(Dang e al.,2025).
Tunisia’s mone a y policy aces inc easing challenges amid climbing geopoli ical
ensions and domes ic ins abili y. The coun y’s cen al bank mus na iga e a complex
en i onmen cha ac e ized by ex e nal shocks, including egional con lic s and global
economic unce ain y, which exace ba e ola ili y in o eign exchange ma ke s and capi al
lows (Mba ek e al.,2019;Moussa & Talbi,2019;Jallouli & Yalouli,2022;Belhoula,2024;
T abelsi,2024a). In such a con ex , he cen al bank is o en o ced o adop es ic i e
measu es, such as aising in e es a es, o s abilize p ices and he cu ency. Howe e ,
hese measu es can inad e en ly slow economic g ow h and deepen social discon en ,
c ea ing a delica e balancing ac be ween ensu ing mac oeconomic s abili y and os e ing
economic esilience.
Since he la e 1980s, Tunisia’s mone a y policy has unde gone subs an ial changes o
acili a e s uc u al e o ms and add ess eme ging economic challenges (Ghali & Mohnen,
2004). In 1987, he Cen al Bank o Tunisia (CBT) implemen ed a s a egy cen e ed on
egula ing money supply g ow h o achie e p ice s abili y, speci ically a ge ing a g ow h
a e o he money supply (M2) ha was 2% lowe han he an icipa ed nominal GDP
g ow h, unde he assump ion o a s able money mul iplie . This amewo k was designed
o synch onize he base money supply wi h he g ow h objec i es o M2. A signi ican
ans o ma ion occu ed in 2006 wi h he e ision o O ganic Law No. 58–90, which
explici ly de ined he CBT’s p ima y objec i e as main aining p ice s abili y (see Mu phy,
1999;S. J. F iedman & Sha key,2010;Wahyuningsih & Fa mawa i,2024). This e o m was
execu ed in wo phases: ini ially, by managing he mone a y base o a ge b oad money
(M3) in alignmen wi h nominal GDP g ow h, and subsequen ly, by adop ing an in la ion-
a ge ing amewo k ha employed in e es a es as he p incipal policy ool. To imp o e
ma ke egula ion, he CBT in oduced deposi and lending acili ies in 2009, enhancing he
lexibili y o mone a y policy implemen a ion. Howe e , he economic uphea al ollowing
he 2011 e olu ion led o conside able dis up ions, including inc eased c oss-bo de
smuggling and illici ade wi h Libya and Alge ia, which in ensi ied in la iona y p essu es
and necessi a ed a g ea e eliance on in la ion- a ge ing mechanisms (A ie & Humud,
2011,2014;End e al.,2020).
The p og ession o mone a y policy in Tunisia illus a es he CBT’s adap i e esponses
o economic and inancial challenges o e he pas decade. The in oduc ion o new
legisla ion (Law No. 2016-35, enac ed on 25 Ap il 2016) ea i med p ice s abili y as he
cen al objec i e, while also ele a ing he impo ance o inancial s abili y ela i e o he
2006 amewo k (see Tayssi & Fe yel,2018). In he a e ma h o he A ab Sp ing, he CBT
ansi ioned owa ds g ea e exchange a e lexibili y, coinciding wi h he eme gence o a
s uc u al liquidi y de ici wi hin he banking sec o . The e o is a acks o 2015 u he
complica ed mone a y policy, leading o a subs an ial inc ease in cen al bank e inancing,
which comp omised con ol o e mone a y agg ega es and unde mined ini ial e o s o
mi iga e in la ion h ough policy a e inc eases (A ie ,2018;Salem & Bouaziz,2024). In
ligh o hese challenges, he CBT has adop ed mo e s ingen igh ening measu es since
2018 o add ess ising in la ion. To achie e i s p ice s abili y manda e, he cen al bank
Economies 2025,13, 185 3 o 68
has es ablished bo h an in e media e objec i e— ocused on in la ion o ecas ing—and an
ope a ional objec i e, which in ol es guiding he in e bank a e as he p ima y ins umen
o mone a y policy.
In he absence o hese in e en ions, he epe cussions o inac ion would ha e been
di e: in la ion could ha e escala ed o double-digi le els, and eal in e es a es would
ha e emained signi ican ly nega i e, he eby p omo ing excessi e c edi expansion and
consump ion. Addi ionally, heigh ened demand o impo ed goods would ha e exace -
ba ed he cu en accoun de ici , deple ing o eign exchange ese es and in ensi ying
in la iona y p essu es. Fu he mo e, escala ing in la ion would ha e educed deposi e-
u ns, unde mining bank esou ces and inc easing dependence on cen al bank e inancing,
he eby heigh ening liquidi y isks. The e ol ing policy amewo k o he CBT highligh s
he essen ial ole o mone a y policy in sus aining economic s abili y amids pe sis en
domes ic and ex e nal shocks (Ben Yousse ,2024).
This esea ch ocuses on Tunisia, an eme ging economy ha has been no ably unde -
explo ed in schola ly li e a u e. As a esul , he indings o his s udy may ha e impo an
implica ions o cen al banke s and mac oeconomis s.
The selec ion o Tunisia as a case s udy is based on h ee p ima y conside a ions.
Fi s , Tunisia exempli ies a small, open, eme ging economy ha is unde going ins i u ional
ansi ion wi hin he Middle Eas No h A ica (MENA) egion. O e he pas decade, he
coun y has shi ed i s exchange a e and mone a y policy amewo k om a cu ency
baske sys em o a managed loa ing egime, which heo e ically enhances he e ec i eness
o mone a y policy. Addi ionally, Tunisia has ecen ly g an ed legal independence o i s
cen al bank. I his independence ansla es in o p ac ical e ec i eness, i is expec ed
o s eng hen mone a y policy by p o iding he cen al bank wi h g ea e au onomy in
decision-making and imp o ing he signaling capaci y o ma ke pa icipan s. Fu he -
mo e, cen al bank independence is an icipa ed o enhance he e iciency o mone a y
policy by limi ing di ec go e nmen inancing, he eby inc easing inancial au onomy and
ein o cing accoun abili y h ough mo e anspa en policy communica ion.
Second, he CBT has consis en ly aised in e es a es o alle ia e in la iona y p essu es,
e lec ing ends seen in many o he coun ies. Howe e , he e ec i eness o his s a egy
emains deba able, as in la ion con inues o emain high despi e he igh ening o mone a y
policy. Fu he mo e, he pe sis en dep ecia ion o he exchange a e and he widening
ade de ici cas doub on he e icacy o in e es a e hikes. The disc e iona y na u e o
Tunisia’s mone a y policy, which has no ye adop ed a clea a ge ing amewo k, u he
complica es his si ua ion.
Thi d, esea ch is sca ce ega ding agg ega e p oduc ion and in la iona y esponses o
mone a y policy shocks in Tunisia, wi h a lack o consensus on bo h he magni ude and
di ec ion o hese e ec s. Fu he mo e, exis ing s udies p edominan ly ocus on o e all
GDP, lea ing he esponses o sec o al componen s la gely unexamined. P e ious analyses
ha e o en ea ed mone a y policy as a “black box”, in he wo ds o Mimoun e al. (2024),
ailing o in es iga e how a ious ansmission mechanisms in luence he economy.
We s udy he e ec i eness o mone a y policy ansmission in Tunisia. We use ad-
anced econome ic echniques o compa e he money ma ke a e beha io unde high
and low geopoli ical isks. F equen ly, he p oduc ion–money ma ke a e– isk ela ion-
ship is puzzling. Fi s , we sol e he enigma by dis inguishing be ween global and local
geopoli ical isk. Second, we conside p oduc ion a he agg ega e and he sec o al le els.
Thi d, we del e in o a deepe analysis o mone a y policy’s (in)e ec i eness by es ab-
lishing a no el composi e index o in es o sen imen in he s ock ma ke s. As such,
ou con ibu ion is dis inc i e and comp ehensi e in e ms o he esea ch ques ion, he
con ex , and he econome ic me hods used, and i o e s a wide ange o p ac ical impli-
Economies 2025,13, 185 4 o 68
ca ions o he p ac ices o cen al banks ha can be applied o simila eme ging small
open economies.
The emainde o he pape is s uc u ed as ollows. Sec ion 2p o ides an o e iew
o heo e ical unde pinnings and ela ed empi ical s udies. Sec ion 3ou lines he esea ch
me hodology. We p esen he indings in Sec ion 4, and Sec ion 5concludes.
2. Li e a u e Re iew
2.1. Theo e ical Delibe a ions
2.1.1. I e e sibili y Theo y
In uns able en i onmen s, a c ucial ques ion o whe he mone a y policy is e ec-
i e a ises. Ea lie heo ies sugges ha mone a y policy becomes less e ec i e when
unce ain y is high. In es o s’ sen imen plays a key ole in channeling unce ain y in o
mone a y policy. This was ou lined by he i e e sibili y heo y (B. S. Be nanke,1983;
Bloom,2009;Bloom,2014). Unce ain y a ec s i ms and economies in wo p ima y ways;
i s , h ough he “ eal op ions” mechanism, whe e he i e e sibili y o in es men de-
cisions encou ages i ms o delay o emain lexible amid unce ain y. This hesi ancy, as
desc ibed by Hen y (1974), ex ends beyond in es men o consume beha io , pa icula ly
o du able goods such as homes and ca s, which a e o en de e ed du ing unce ain
imes. This delay in consump ion is less p onounced o nondu able goods bu s ill impac s
o e all economic ac i i y. Bloom (2014) expanded on his by linking unce ain y wi h
educed esponsi eness o economic s imuli, like ax cu s o in e es a e adjus men s,
necessi a ing s onge s abiliza ion policies du ing ola ile pe iods. Fu he mo e, high
unce ain y dis up s p oduc i i y g ow h by s alling esou ce ealloca ion, a key d i e
o economic e iciency. Second, unce ain y in luences inancial dynamics by aising isk
p emiums. In es o s demand highe compensa ion o inc eased isks, escala ing inancing
cos s and de aul p obabili ies. This, in u n, ele a es bank up cy expenses and con ibu es
o a slowdown in economic ac i i y. A ellano e al. (2010) and Ch is iano e al. (2014)
highligh ed how unce ain y ampli ies he likelihood o ex eme ad e se ou comes, u -
he discou aging bo owing and in es men . Mo eo e , unce ain y uels “ambigui y
a e sion”, whe e economic agen s ocus on wo s -case scena ios, educing con idence and
p omo ing p ecau iona y sa ings o e consump ion.
2.1.2. C edi T ansmission Channel Theo y
Acco ding o he c edi ansmission channel heo y, he nonlinea e ec s o in e es
a es du ing inancial c ises a e less subs an ial han mone a y policy shocks. Because o he
g ea e ex e nal inancing p emiums, businesses a e acing liquidi y limi a ions (as no ed
by Rome & Rome ,1993;Mo gan,1998;B. Be nanke,1999;He & K ishnamu hy,2013;
B unne meie & Sanniko ,2014). Acco ding o he idea, changes in he ex e nal inance
p emium—which ep esen s he cos di e en ial be ween money aised in e nally ( ia
e ained ea nings) and ex e nally ( h ough s ock o deb )—ampli y he immedia e impac
o mone a y policy on in e es a es. The ex e nal inance p emium highligh s laws in he
c edi ma ke s ha lead o a disc epancy be ween he expenses and he p o i s ha lende s
an icipa e. The “c edi iew” holds ha mone a y policy ends o a ec he ex e nal inance
p emium in he same way as i a ec s open-ma ke in e es a es. The o e all e ec o
mone a y policy on bo owing a es is s eng hened by his addi ional policy e ec on he
ex e nal inance p emium, which in u n in luences eal spending and economic ac i i y
(see B. S. Be nanke & Ge le ,1995).

Economies 2025,13, 185 5 o 68
2.1.3. Cen al Bank Communica ion and C edibili y
Economic agen s’ expec a ions depend on how hey—economic agen s—diges he
cen al bank’s messages. The con en , cla i y, iming, and c edibili y o mone a y policy
announcemen s ha e long been su eyed in he li e a u e (Wood o d,2005;Blinde e al.,
2008;Blinde ,2009;De Haan & S u m,2019). Cen al banks a e o en pe cei ed as ha ing
supe io insigh s in o he economic ou look due o hei ex ensi e o ecas ing esou ces and
abili y o analyze unobse able economic ac o s. Resea ch, such as ha by Ande sson e al.
(2006), showed ha inancial ma ke s espond signi ican ly o he in o ma ion cen al banks
sha e, wi h in es o s adjus ing hei iews based on hese announcemen s. Kohn and Sack
(2003) emphasized ha p i a e agen s end o us cen al banks’ o ecas s, pa icula ly
when he ins i u ion has buil a s ong epu a ion o accu a e economic assessmen s.
Economic agen s a y in hei le els o sophis ica ion—some a o s aigh o wa d, concise
in o ma ion, while o he s seek in-dep h, de ailed analyses o in o m hei decisions (Fila do,
2004). Some ecen esea ch has looked in o spli ing mone a y policy shocks in o su p ise
policy shocks and cen al bank in o ma ion shocks h ough high- equency iden i ica ion
s a egies (e.g., Ja oci´nski & Ka adi,2017,2020;Laséen,2020;Os apenko,2020;Laume &
San os,2024). O he s, like Hansen e al. (2019), employed a di e en s a egy o show ha
u u e expec a ions a e sensi i e o communica ing abou unce ain y.
2.2. Empi ical Li e a u e
The e is a g owing body o li e a u e on he e ec s o mone a y policy unde unce -
ain y ocusing on he US ma ke (e.g., Ten ey o & Thwai es,2016;Ba nichon & Ma hes,
2018;Alpanda e al.,2021;B uns & Pi e ,2023; e c.). Those pape s show ha igh ening
mone a y policy unde heigh ened unce ain y gene ally slows he mone a y ansmission
mechanism. E idence is e ie ed om nonlinea modeling such as Vec o Au o eg essi e
(VAR) and local p ojec ions (LPs).
Pape s ha p esen esea ch ques ions simila o ou s a e e y no el. The in luen ial
wo k by Calda a and Iaco iello (2022) in oduced a news-based geopoli ical isk indica o ,
demons a ing ha a geopoli ical isk shock has con ac iona y e ec s on he economy,
wi h in es men in indus ies mo e exposed o geopoli ical isks being pa icula ly a ec ed.
Calda a e al. (2024) expanded his analysis o a comp ehensi e panel o coun ies, e eal-
ing ha du ing geopoli ical e en s, supply-side dis up ions ypically ou weigh educed
agg ega e demand, esul ing in inc eased in la ion and a ied e ec s on economic ac i i y.
No ably, while geopoli ical isk shocks s imula e economic ac i i y in he US—la gely due
o heigh ened de ense spending— hey lead o ecessions in o he na ions. Building on his,
Bonda enko e al. (2024) e ined he geopoli ical isk index by u ilizing coun y-speci ic
news sou ces, o e ing a pe spec i e ha be e e lec s local geopoli ical dynamics ins ead
o a p edominan ly Wes e n iewpoin . Pinche i (2024) ca ego ized geopoli ical isk shocks
in o hose a ising om ene gy ma ke dis up ions and hose linked o weak agg ega e
demand. B ignone e al. (2024) highligh ed ha hose la ge geopoli ical shocks p ima ily
a ec he economy by inc easing unce ain y. Kilian e al. (2024) p oposed a dynamic
s ochas ic gene al equilib ium (DSGE) global economy model, examining he ela ionship
be ween geopoli ical oil p ice isks and economic luc ua ions. Thei indings indica e ha
while geopoli ical ension plays a c i ical ole in d i ing oil p ice ola ili y, i has a limi ed
impac on b oade mac oeconomic luc ua ions. In episodes o high geopoli ical ensions, i
is impo an o accoun o mone a y– iscal coo dina ion (F anconi,2024). How geopoli ical
isk a ec s business cycles and economic condi ions is a ma e o esea ch. Geopoli ical isk
shocks in luence he economy h ough a ious channels, wi h some exe ing in la iona y
p essu es, while o he s ha e de la iona y e ec s. In la iona y channels include he impac
on commodi y p ices, pa icula ly oil, as highligh ed by Mignon and Saadaoui (2024), and
Economies 2025,13, 185 6 o 68
he cu ency channel, as discussed by Gopina h (2015), Kisswani and Elian (2021), and
Yilmazkuday (2025). Con e sely, de la iona y e ec s a ise om channels such as consume
sen imen , whe e heigh ened unce ain y dampens spending, and inancial condi ions,
which igh en du ing pe iods o geopoli ical ins abili y (Fo bes & Wa nock,2012;Fo bes
e al.,2017;Choi & Hadad,2025).
Ou con ibu ion. We dis inguish ou wo k om he exis ing li e a u e by ocusing on
Tunisia. Since he la e 1980s, Tunisia has implemen ed ma ke -o ien ed e o ms, hough
he s a e con inues o play a subs an ial ole. I in luences consume p ices, employs
a signi ican po ion o he labo o ce, and ac i ely pa icipa es in wage nego ia ions.
S a e-owned banks domina e he inancial sec o , which emains unde de eloped de-
spi e a no able le el o lending ela i e o economic ou pu . The sec o aces challenges,
including a lack o an e icien secu i ies ma ke , eliance on colla e al-based lending,
and a conside able bu den o non-pe o ming loans. The in o mal economy is signi i-
can , con ibu ing subs an ially o employmen in he p i a e sec o . Be o e 2011, s a e
cap u e was p e alen ; businesses connec ed o he p esiden ial amily ou pe o med com-
pe i o s, and domina ed speci ic sec o s ha we e hea ily egula ed and es ic ed o
o eign in es men (
P zys upa & W óbel,2015
). A e an unsuccess ul mix o exchange
a e and mone a y agg ega e a ge ing, he CBT now uses in e es a es as he p ima y
mone a y ool. The 2011 e olu ion ma ked a majo shi in he coun y’s economic
landscape. In 2016, he CBT became independen . Tunisia p esen s he key cha ac e -
is ics o small, ade-open, eme ging ma ke economies, and se es as a aluable case
o analysis.
In ou wo k, we a gue ha he Tunisian mone a y policy’s eac ion o geopoli ical
isk is subjec o bo h in la iona y and de la iona y channels. The e a e ins ances in which
he cen al bank ca es abou cu bing in la ion, while in o he s, dealing wi h in es o s’
sen imen s becomes a p io i y (Ginn & Saadaoui,2024).
3. Resea ch Me hodology
3.1. Da a Collec ion
3.1.1. The Geopoli ical Risk Index
A key aspec o economic policy planning in ol es add essing isks associa ed wi h
geopoli ical e en s. These include wa s, poli ical un es , elec ions, na u al disas e s, shi s in
poli ical egimes, and escala ing ensions be ween na ions. Such geopoli ical unce ain ies
in luence economic condi ions and inancial ma ke s (Kisswani & Elian,2021).
Calda a and Iaco iello (2022) de eloped a no el indica o o quan i y ad e se geopo-
li ical e en s by analyzing he equency o ela ed a icles in p ominen newspape s. Thei
measu e, known as he Geopoli ical Risk (GPR) index, cap u es he dynamics o geopoli ical
ensions and hei economic implica ions om 1900 onwa ds.
The GPR index is de i ed using au oma ed ex analysis om he digi al a chi es o
en majo newspape s—Chicago T ibune, The Daily Teleg aph, Financial Times, The Globe
and Mail, The Gua dian, Los Angeles Times, The New Yo k Times, USA Today, The Wall
S ee Jou nal, and The Washing on Pos . The index calcula ion in ol es iden i ying a icles
discussing ad e se geopoli ical e en s each mon h and exp essing hem as a p opo ion
o he o al numbe o a icles. The da ase consis s o app oxima ely 25 million news
a icles om leading English-language newspape s, co e ing publica ions om 1900 o he
p esen . In ecen yea s, he sample has included a ound 30,000 a icles pe mon h, while
he his o ical da ase con ains abou 10,000.
Economies 2025,13, 185 7 o 68
The me hodology employs a dic iona y-based app oach, selec ing wo ds ha closely
align wi h he concep o geopoli ical isk. This selec ion p ocess is in o med by geopoli ical
ex books, his o ical linguis ic da abases, and he analysis o high- equency wo ds appea -
ing in news epo s du ing pe iods o heigh ened geopoli ical ension. Key e ms include
“wa ”, “ h ea ”, “ e o ”, “blockade”, “in asion”, and “ oops”, which a e signi ican ly
mo e p e alen in media co e age du ing geopoli ical c ises.
The analysis ca ego izes a icles in o eigh hema ic g oups: h ea s o wa , h ea s o
peace, mili a y buildups, nuclea h ea s, e o h ea s, wa ini ia ions, wa escala ions,
and ac s o e o . Two subindices a e cons uc ed om hese ca ego ies: he Geopoli ical
Th ea s index, encompassing he i s i e ca ego ies, and he Geopoli ical Ac s index,
co e ing he las h ee.
As Calda a and Iaco iello (2022, p. 5) ha e pu i , he dic iona y used o cons uc ing
he index is de eloped based on a s uc u ed app oach. Fi s , i is designed o align wi h
he de ini ion o geopoli ical isk es ablished in his s udy, ensu ing ha selec ed e ms
accu a ely e lec ele an e en s. Second, e e ences om geopoli ical ex books and he
Co pus o His o ical Ame ican English help iden i y key hemes associa ed wi h geopoli i-
cal e en s, such as “wa on e o ” o “nuclea weapon”, as well as wo ds commonly linked
o con lic , like “decla e”. Thi d, he selec ion p ocess p io i izes high- equency wo ds
and hei synonyms ha end o appea mo e equen ly in news epo s du ing pe iods o
heigh ened geopoli ical ension. Fo example, e ms such as “c isis”, “ e o ”, “blockade”,
“in asion”, “ oops”, and “wa ” a e signi ican ly mo e p e alen in media co e age du ing
geopoli ical c ises.
Then, an audi was conduc ed o ensu e eliabili y, in ol ing a human e iew o 16,000
newspape a icles and compa isons wi h ex e nal indica o s, and con i ming ha he index
e ec i ely acks geopoli ical isk luc ua ions.
The p ima y GPR index, based on all en newspape s, began in 1985. The coun y-
speci ic GPR indices a e de i ed om au oma ed ex sea ches o newspape a chi es. Fo
44 ad anced and eme ging economies, Calda a and Iaco iello (2022) cons uc ed hese
indices by iden i ying, o he Recen Index, he mon hly p opo ion o a icles om 1985
onwa d ha (1) quali y o inclusion in he gene al GPR index and (2) e e ence he speci ic
coun y o i s majo ci ies. These indices a e exp essed as a pe cen age o o al a icles
published each mon h, and p o ide insigh s in o geopoli ical isks as pe cei ed om a US
pe spec i e o each coun y. We ha e ga he ed he global GPR and he local GPR ela ed o
Tunisia. Howe e , we selec ed da a om 1993 onwa ds, as da a co e age o o he a iables
is a ailable s a ing om ha yea .1
No able spikes in he index a e obse ed du ing signi ican his o ical e en s, such
as he a e ma h o 9/11, he US–I aq wa , he Russia–Uk aine wa , and he banking
ins abili y o Silicon Valley Bank (see Figu e 1). Ele a ed geopoli ical isk is linked o
declines in in es men (S. Wang e al.,2019), s ock ma ke pe o mance (Ago aki e al.,2022),
and employmen le els (Ji e al.,2025). O he s es ablished a link wi h ene gy ansi ion
and en i onmen al sus ainabili y (Q. Wang e al.,2024;Cu cu e al.,2025). The GPR
also co ela es wi h an inc eased likelihood o economic c ises and heigh ened downside
isks o he global economy. Impo an ly, he GPR is associa ed wi h high in la ion and
unce ain y le els (Calda a e al.,2024).
Economies 2025,13, 185 8 o 68
Figu e 1. The e olu ion o he global geopoli ical isk (GPR). (1) 01/1993: START II T ea y signed
be ween he Uni ed S a es and Russia o educe nuclea weapons. (2) 09/1993: The Oslo Acco ds a e
signed, ma king a s ep owa d peace be ween Is ael and Pales ine. (3) 04–07/1994: The Rwandan
Genocide claims he li es o app oxima ely 800,000 people. (4) 12/1994: Russia launches i s i s
wa in Chechnya. (5) 07/1995: S eb enica massac e du ing he Bosnian Wa ; o e 8000 Bosniaks
a e killed. (6) 11/1995: End o he Bosnian Wa . (7) 03/1999: NATO in e enes in he Koso o Wa
h ough ai s ikes agains Yugosla ia. (8) 09/2001: Te o is a acks in he US. (9) 03/2003: The
US and allies in ade I aq, alleging weapons o mass des uc ion. (10) 08/2008: Russia–Geo gia
Wa o e Sou h Osse ia and Abkhazia. (11) 09/2008: Global Financial c isis wi h he collapse o
Lehman B o he s. (12) 01/2011: The A ab Sp ing begins in Tunisia, sp eading ac oss he A ab wo ld.
(13) 03/2011: Sy ian Ci il Wa begins, becoming a p olonged and de as a ing con lic . (14) 02/2014:
Russia annexes C imea ollowing un es in Uk aine. (15) 06/2014: ISIS decla es he es ablishmen
o a calipha e in I aq and Sy ia. (16) 11/2015: Pa is e o is a ack. (17) 06/2016: The UK o es
o lea e he Eu opean Union (B exi ). (18) 12/2019: COVID-19 is i s de ec ed in Wuhan, China,
leading o a global pandemic, (19) 03/2020: COVID-19 lockdowns begin wo ldwide, leading o
economic and social uphea al. (20) 02/2022: Russia in ades Uk aine, igge ing he la ges Eu opean
con lic since WWII. (21) 03/2023: Banking ins abili y eme ges ollowing he collapse o Silicon Valley
Bank. (22) 07/2023: Reco d-b eaking global hea wa es in ensi y calls o clima e ac ion. (23) 10/2023:
Hamas’s a ack and Is ael’s in asion o Gaza. Sou ces: Washing on Wa ch, US Ins i u e o Peace,
Human Righ s Wa ch, Ca negie Endowmen .
The local GPR was mo e p onounced du ing he A ab Sp ing, known as he 2011
e olu ion in Tunisia, and he mul iple elec ions and pa liamen s ollowing he allou o
he Ben Ali egime (see Figu e 2). Du ing hese pe iods, Tunisia unde wen a complica ed
poli ical and economic ansi ion, esul ing in a p olonged economic decline o e he las
decade. Addi ionally, he coun y has been impac ed by egional ins abili y and con lic .
Economies 2025,13, 185 15 o 68
i s is he Augmen ed Dickey–Fulle (ADF) es , in oduced by Dickey and Fulle (1979).
The au ho s examine he ollowing andom walk.
y =c0+c1 +ρy −1+∑k
i=1φi∆y −i+ϵ , =1, 2, . . . , ϵ ;N0, σ2(7)
Dickey and Fulle (1979) u ilized he maximum likelihood app oach o de elop an
es ima o o
ρ
. Addi ionally, he Phillips–Pe on (PP) es in oduced by Pe on and
Phillips (1987) p oposes a mino adjus men o Equa ion (7), and uses a semi-pa ame ic
app oach o es ima e ρ;
y =c0+c1 +ρy −1+ϵ , =1, 2, . . . , ϵ ;N0, σ2(8)
The hypo hesis es is gi en by
(H0:ρ=1(y is nons a iona y)
H1:ρ<1(y is s a iona y)
The e is also he Dickey–Fulle es o a uni oo , in which he se ies has been
ans o med by a gene alized leas -squa es eg ession (DF-GLS) in oduced by Ellio e al.
(1996). The e a e wo possible al e na i e hypo heses, as ollows:
y
is s a iona y a ound
a linea end, o y is s a iona y wi h no linea ime end. Unde he i s al e na i e
hypo hesis, he DF-GLS es is pe o med by i s es ima ing he in e cep and end ia
GLS. The GLS es ima ion is pe o med by gene a ing he new a iables, ∼
y , whe e
∼
y =δ01−13.5
T+δ1 −13.5
T( −1)+ϵ ,
An OLS eg ession is pe o med on he Equa ion abo e. Then, we gene a e he ollowing:
y∗
=y −b
δ0+ˆ
δ1 
Finally, we pe o m ADF on he ans o med model,
y∗
=a0+a1 +ρy∗
−1+∑k
i=1ζi∆y∗
−i+ϵ , =1, 2, . . . , ϵ ;N0, σ2
Table A1 in Appendix A.1 p esen s he uni oo indings om ADF and PP o a ious
assump ions abou he in e cep and end. I P ob is lowe han he signi icance le el, hen
we ejec he null hypo hesis o a uni oo in he se ies. All a iables a e s a iona y a e
aking he i s di e ence. They a e in eg a ed in o o de 1 (I(1)), excep LNIPIT, LNGPR,
and LNGPR_TUN, which a e I(0) and I(1) a he same ime. We ensu e he c edibili y o
hose esul s in h ee ways. Fi s , we conduc he Kwia kowski–Phillips–Schmid –Shin
(KPSS) es o he same se o a iables. The esul s in Table A2 o Appendix A.1 e eal ha
all a iables a e in eg a ed in he o de o 1(I(1)), excep LNCPI. Second, we conduc he
DF-GLS o each a iable in le el and i s di e ence by conside ing con inuous samples.
The esul s a e a ailable in Table 4, and e eal ha all a iables a e I(1), excep LNGPR and
LNGPR_TUN, which a e I(0) and I(1) a he same ime.
Taking all he indings oge he , we conside ha all a iables a e I(1).

Economies 2025,13, 185 16 o 68
Table 4. DF-GLS o ERS uni oo es esul s.
Null Hypo hesis:
The Va iable Is
Nons a iona y
Wi h In e cep Wi h T end and In e cep
Va iables -S a is ic C i ical Values -S a is ic C i ical Values
LNIPIT −0.259930
1% −2.572419
5% −1.941847
10% −1.616017
NS −1.748442
1% −3.471100
5% −2.907800
10% −2.601150
NS
LNCPI 6.030500
1% −2.571261
5% −1.941687
10% −1.616122
NS 1.167709
1% −3.476800
5% −2.896400
10% −2.581200
NS
LNM2TND 5.090570
1% −2.571492
5% −1.941719
10% −1.616101
NS −0.409436
1% −3.477400
5% −2.895200
10% −2.579100
NS
TMM −0.402107
1% −2.571160
5% −1.941673
10% −1.616131
NS −0.409436
1% −3.477400
5% −2.895200
10% −2.579100
NS
LNTCER 0.277960
1% −2.571176
5% −1.941675
10% −1.616130
NS −1.464461
1% −3.477300
5% −2.895400
10% −2.579450
NS
LNGPR −3.003150
1% −2.571143
5% −1.941671
10% −1.616133
S−3.493809
1% −3.477500
5% −2.895000
10%−2.578750
S
LNGPR_TUN −3.853404
1% −2.571143
5% −1.941671
10% −1.616133
S−4.164004
1% −3.477500
5% −2.895000
10% −2.578750
S
D(LNIPIT) −3.198518
1% −2.572443
5% −1.941850
10% −1.616015
S−5.755603
1% −3.471000
5% −2.908000
10% −2.601500
S
D(LNCPI) −6.770077
1% −2.571278
5% −1.941689
10% −1.616121
S−7.371329
1% −3.476700
5% −2.896600
10% −2.581550
S
D(LNM2TND) −2.654795
1% −2.571511
5% −1.941721
10% −1.616099
S−4.366044
1% −3.475400
5% −2.899200
10% −2.586100
S
D(TMM) −6.519736
1% −2.571176
5% −1.941675
10% −1.616130
S−7.870420
1% −3.477300
5% −2.895400
10% −2.579450
S
D(LNTCER) −3.563881
1% −2.571193
5% −1.941678
10% −1.616128
S−5.817486
1% −3.477200
5% −2.895600
10% −2.579800
S
D(LNGPR) −2.265248
1% −2.571160
5% −1.941673
10% −1.616131
S−4.245754
1% −3.477400
5% −2.895200
10% −2.579100
S
D(LNGPR_TUN) −3.781700
1% −2.571160
5% −1.941673
10% −1.616131
S−6.698073
1% −3.477400
5% −2.895200
10% −2.579100
S
No e: NS “nons a iona y”, S “s a iona y”. LNIPIT: Indus ial p oduc ion (in log). LNCPI: Consume p ice index
(in log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money ma ke a e. LNTCER: Real e ec i e
exchange a e (in log). LNGPR: Global geopoli ical isk (in log). LNGPR_TUN: Local geopoli ical isk (in log).
Economies 2025,13, 185 17 o 68
Nex , we conduc a uni oo es ha accoun s o s uc u al b eaks. We u ilize he
Zi o –And ews me hod o his analysis. The Zi o and And ews (2002) es pe mi s he
iden i ica ion o b eakpoin s ha occu endogenously, and e alua es he null hypo hesis
o a uni oo in con as o he al e na i e hypo hesis. The a iable
y
is s a iona y wi h
a s uc u al b eak in in e cep , in end, o in bo h end and in e cep . Equa ion (9) is
iden ical o
Equa ion (7)
, bu includes wo dummies
DU =
1
i >TBand
0
o he wise
,
DT = −TBi >TBand 0 o he wise;
y =c0+c1 +θDU +ιDT +ρy −1+∑k
i=1φi∆y −i+ϵ , =1, 2, . . . , ϵ ;N0, σ2(9)
The esul s a e a ailable in he Supplemen a y File (see Table S1) and con i m again
he o de o in eg a ion o he a iables.
4.2. Connec ion/Disconnec ion Episodes o he In e es Ra e: Time-Va ying G ange
Causali y Resul s
We ollow I ano and Kilian (2005) and use he Akaike In o ma ion C i e ion (AIC)
o selec he op imal lag (see Tables S2–S5), as his gi es mo e accu a e es ima ions o
mon hly da a. The e a e ou VAR sys ems ha we conside . Model 1 includes he indus ial
p oduc ion (LNIPIT), he money supply (LNM2TND), he money ma ke a e (TMM), he
eal e ec i e exchange a e (LNTCER), and he global geopoli ical isk index (LNGPR) as
endogenous a iables. Model 2 is simila o Model 1, bu we conside he consume p ice
index (LNCPI) ins ead o LNIPIT. Model 3 eplica es Model 1 wi h he local geopoli ical
isk index (LNGPR_TUN). Model 4 subs i u es LNIPIT wi h LNCPI in Model 3. Table 5
p esen s he esul s o he Max–Wald s a is ic using h ee me hods: o wa d, olling, and
ecu si e. We ejec he null hypo hesis o no causali y om all a iables o LNIPIT. In
pa icula , TMM does G ange cause LNIPIT a a 1% le el o signi icance acco ding o
olling and ecu si e expanding me hods, while he null hypo hesis is no ejec ed unde
he o wa d expanding me hod.
Table 5. Time- a ying G ange causali y esul s. Dependen a iable—LNIPIT, globalg geopoli ical
isk—LNGPR.
H0: LNIPIT Is No
G ange Caused by Max_Wald_Fo wa d Max_Wald_Rolling Max_Wald_Recu si e
LNM2TND
37.994 ***
(9.416)
{11.771}
[17.284]
38.721 ***
(9.792)
{11.959}
[16.464]
38.721 ***
(10.179)
{12.545}
[17.292]
TMM
11.651
(11.825)
{14.584}
[24.417]
58.561 ***
(11.582)
{15.072}
[25.500]
60.503 ***
(12.273)
{15.950}
[26.670]
LNTCER
18.725 **
(8.791)
{11.005}
[19.500]
21.233 ***
(8.945)
{13.048}
[18.688]
29.894 ***
(9.683)
{13.048}
[19.500]
LNGPR
18.842 ***
(7.367)
{9.971}
[14.834]
21.027 ***
(8.605)
{10.188}
[14.416]
21.027 ***
(8.742)
{10.188}
[14.834]
No e: *, **, *** signi ican a 10%, 5%, and 1% le els, espec i ely. The 90 h pe cen iles a e be ween pa en heses,
95 h pe cen iles a e be ween b aces, and 99 h pe cen iles a e be ween b acke s. LNIPIT: Indus ial p oduc ion (in
log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money ma ke a e. LNTCER: Real e ec i e
exchange a e (in log). LNGPR: Global geopoli ical isk (in log).
Economies 2025,13, 185 18 o 68
Now ocusing on he plo s o Figu e 3, he null hypo hesis o no causali y is e-
jec ed whene e he es s a is ic po ayed by he solid line exceeds he c i ical alues
(do ed lines).
Acco ding o he o wa d-expanding Wald es , he es s a is ic g adually declines
and emains below he c i ical alues (90 h and 95 h pe cen iles) o mos o he pe iod,
sugges ing a weake o non-exis en causal ela ionship be ween TMM and indus ial
p oduc ion du ing hese imes. The declining end could signi y a g adual disconnec ion
e ec , po en ially ela ed o geopoli ical isks dis up ing he ansmission mechanism. The
olling Wald es shows a luc ua ing causal ela ionship, wi h he es s a is ic equen ly
exceeding he c i ical h esholds in ce ain pe iods (e.g., a ound 2009 and b ie ly a e
2015). These pe iods o connec ion sugges ha TMM migh ha e in luenced ou pu du ing
imes o lowe geopoli ical isks o ela i e economic s abili y. The in e mi en disconnec-
ions ( es s a is ics alling below c i ical alues) align wi h heigh ened geopoli ical isks,
po en ially dampening he e ec i eness o mone a y policy on indus ial p oduc ion.
The ecu si e me hod highligh s pe sis en peaks (e.g., a ound 2009 and a e 2015),
wi h he es s a is ic exceeding he c i ical alues du ing hese pe iods. These sus ained
connec ions migh indica e momen s when geopoli ical isks we e less dis up i e, allowing
mone a y policy o impac indus ial p oduc ion. Howe e , he sha p declines in o he
pe iods (e.g., 2009–2015) sugges signi ican disconnec ions, possibly due o heigh ened
geopoli ical isk.
O e all, he esul s indica e al e na ing pe iods o connec ion and disconnec ion
be ween TMM and indus ial p oduc ion in Tunisia. Global geopoli ical isks likely played
a ole in weakening he causal ela ionship by dis up ing inancial ma ke s and economic
s abili y. Pe iods o disconnec ion co espond o heigh ened isk, educing he e ec i eness
o mone a y policy ansmission mechanisms. Con e sely, connec ions eme ge du ing
mo e s able pe iods, allowing mone a y policy o in luence p oduc ion.
(a) Fo wa d
Figu e 3. Con .
Economies 2025,13, 185 19 o 68
(b) Rolling
(c) Recu si e
0
20
40
60
2000m1 2005m1 2010m1 2015m1 2020m1
wi h 90 h (--) and 95 h (-) pe cen iles o boo s apped es s a is ics
Rolling Wald es o lnipi G-caused by mm, 1994m1 - 2019m4
0
20
40
2000m1 2005m1 2010m1 2015m1 2020m1
Recu si e expanding Wald es o lnipi G-caused by mm, 1994m1 - 2019m4
wi h 90 h (--) and 95 h (-) pe cen iles o boo s apped es s a is ics
60
Figu e 3. The o wa d, olling, and ecu si e e ol ing es esul s wi h lag augmen a ion d = 1 a e
displayed in panels (a–c) wi h a minimum window size = 72. The eg ession model 1 is (LNIPIT
LNM2TND TMM LNTCER LNGPR)’, allowing o he e oscedas ic e o s. The empi ical size is
5%, con olled o e a 1-yea pe iod and ob ained om boo s apping. Lag o de s a e assumed o
be cons an and selec ed using AIC(=3) wi h a maximum leng h o 8 o he whole sample pe iod.
LNIPIT: Indus ial p oduc ion (in log). LNCPI: Consume p ice index (in log). LNM2TND: Mone a y
Agg ega es 2 in TND (in log). TMM: Money ma ke a e. LNTCER: Real e ec i e exchange a e (in
log). LNGPR: Global geopoli ical isk (in log).
The empi ical indings indica e ha geopoli ical isks signi ican ly in luence he e -
ec i eness o mone a y policy in Tunisia. This ela ionship is media ed h ough se e al
Economies 2025,13, 185 20 o 68
c i ical ansmission channels, which ul ima ely a ec he connec ion be ween he Tunisian
money ma ke (TMM) and indus ial p oduc ion.
One p ima y mechanism in ol es dis up ions in inancial ma ke s, whe e heigh ened
geopoli ical isks inc ease unce ain y, leading o capi al ligh , exchange a e ola ili y,
and liquidi y sho ages wi hin domes ic inancial ma ke s (see Zhao,2024). These ac o s
unde mine he ansmission o mone a y policy by educing banks’ willingness o ex end
c edi and al e ing in es o s’ isk pe cep ions. Consequen ly, adjus men s o he policy
a e by CBT may no e ec i ely ansla e in o changes in c edi condi ions o decisions
ega ding indus ial in es men .
Ano he signi ican channel ela es o in la iona y p essu es and isk p emiums, as
geopoli ical shocks o en lead o supply chain dis up ions, inc ease impo cos s, and igge
in la iona y su ges. In esponse o ising in la iona y expec a ions, he CBT may adop
con ac iona y measu es, such as aising in e es a es, o s abilize p ices. Howe e , du ing
pe iods ma ked by geopoli ical u bulence, such measu es may inad e en ly wo sen
economic slowdowns a he han p omo e g ow h, he eby educing he expec ed impac
o mone a y policy on indus ial p oduc ion.
Fu he mo e, he c edibili y o policy and he in e en ions o he cen al bank a e
c ucial in de e mining he s eng h o he ela ionship be ween mone a y policy and indus-
ial p oduc ion. In imes o heigh ened geopoli ical isk, he cen al bank may eso o
uncon en ional measu es, such as liquidi y injec ions o o eign exchange in e en ions, o
s abilize inancial condi ions (Ben Yousse ,2024). While hese in e en ions can alle ia e
sho - e m ins abili y, hey may also lead o episodes o policy disconnec ion, du ing which
adi ional ansmission mechanisms become less e ec i e due o shi s in in es o beha -
io and economic unce ain y. Con e sely, du ing mo e s able pe iods, when inancial
ma ke s ope a e e icien ly, he ansmission o mone a y policy is ein o ced, enabling he
ansmission mechanism o exe a mo e p edic able in luence on indus ial p oduc ion.
The cyclical e ec i eness o mone a y policy in Tunisia e lec s he in e play o ex e nal
shocks, policy esponses, and economic condi ions, s eng hening when geopoli ical isks
subside and weakening amid heigh ened unce ain y.
When conside ing Model 2, we obse e he same causali y pa e n o all a iables
ela ed o LNCPI. Pa icula ly, TMM does G ange LNCPI o e he sample pe iod o olling
and ecu si e expanding me hods a he 1% signi icance le el (see Table 6). Ne e heless,
we iden i y episodes o connec ion and disconnec ion when examining he g aphs o
Figu e 4. Excep o he o wa d me hod, bo h olling and ecu si e expanding me hods
show a luc ua ing pa e n, wi h high s a is ical signi icance depic ed o he pe iod (2005–
2009) and hen a ound 2012–2013, be o e showing up again wi h a ai impac du ing
2017–2018. These pe iods coincide wi h he low alues o global GPR (LNGPR).
Table 6. Time- a ying G ange causali y esul s. Dependen a iable—LNCPI; global geopoli ical
isk—LNGPR.
H0: LNCPI Is No G ange Caused by Max_Wald_Fo wa d Max_Wald_Rolling Max_Wald_Recu si e
LNM2TND
2.380
(8.630)
{9.987}
[14.680]
25.064 ***
(8.587)
{10.235}
[15.356]
28.609 ***
(8.779)
{10.683}
[15.356]
TMM
4.777
(9.826)
{13.228}
[16.462]
28.326 ***
(10.016)
{12.855}
[17.988]
41.501 ***
(10.725)
{13.843}
[18.065]

Economies 2025,13, 185 21 o 68
Table 6. Con .
H0: LNCPI Is No G ange Caused by Max_Wald_Fo wa d Max_Wald_Rolling Max_Wald_Recu si e
LNTCER
1.947
(8.486)
{10.634}
[17.786]
24.095 ***
(8.422)
{11.255}
[19.514]
24.677 ***
(8.854)
{11.342}
[19.774]
LNGPR
2.664
(8.928)
{11.853}
[16.944]
22.489 ***
(9.327)
{12.081}
[17.509]
22.489 ***
(10.158)
{12.486}
[17.699]
No e: *, **, *** signi ican a 10%, 5%, and 1% le els, espec i ely. The 90 h pe cen iles a e be ween pa en heses,
95 h pe cen iles a e be ween b aces, and 99 h pe cen iles a e be ween b acke s. LNCPI: Consume p ice index (in
log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money ma ke a e. LNTCER: Real e ec i e
exchange a e (in log). LNGPR: Global geopoli ical isk (in log).
(a) Fo wa d
(b) Rolling
0
5
10
2000m1 2005m1 2010m1 2015m1 2020m1 2025m1
Fo wa d expanding Wald es o lncpi G-caused by mm, 1994m1 - 2023m12
wi h 90 h (--) and 95 h (-) pe cen iles o boo s apped es s a is ics
15
0
10
20
30
2000m1 2005m1 2010m1 2015m1 2020m1 2025m1
wi h 90 h (--) and 95 h (-) pe cen iles o boo s apped es s a is ics
Rolling Wald es o lncpi G-caused by mm, 1994m1 - 2023m12
Figu e 4. Con .
Economies 2025,13, 185 22 o 68
(c) Recu si e
0
2000m1 2005m1 2010m1 2015m1 2020m1 2025m1
Recu si e expanding Wald es o lncpi G-caused by mm, 1994m1 - 2023m12
wi h 90 h (--) and 95 h (-) pe cen iles o boo s apped es s a is ics
40
30
20
10
Figu e 4. The o wa d, olling, and ecu si e e ol ing es esul s wi h lag augmen a ion d = 1 a e
displayed in panels (a–c) wi h a minimum window size = 72. The eg ession model 2 is (LNCPI
LNM2TND TMM LNTCER LNGPR)’, allowing o he e oscedas ic e o s. The empi ical size is 5%,
con olled o e 1 yea and ob ained om boo s apping. Lag o de s a e assumed o be cons an and
selec ed using AIC(=2), wi h a maximum leng h o 8 o he whole sample pe iod. LNCPI: Consume
p ice index (in log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money ma ke a e.
LNTCER: Real e ec i e exchange a e (in log). LNGPR: Global geopoli ical isk (in log).
We eplace he global GPR wi h he local GPR (LNGPR_TUN). All mac oeconomic
a iables signi ican ly impac p oduc ion (LNIPIT) h oughou he pe iod, as shown by
expanding he Wald me hods (Table 7). Figu e 5illus a es a luc ua ing ela ionship
be ween TMM and LNIPIT, especially in olling and ecu si e me hods, wi h no able spikes
a ound 2009 and 2017–2018, linked o low local geopoli ical isk. High geopoli ical isk
co ela es wi h ine ec i e mone a y ansmission.
Table 7. Time- a ying G ange causali y esul s. Dependen a iable—LNIPIT; local geopoli ical
isk—LNGPR_TUN.
H0: LNIPIT Is No G ange Caused by Max_Wald_Fo wa d Max_Wald_Rolling Max_Wald_Recu si e
LNM2TND
44.504 ***
(10.541)
{12.448}
[17.448]
63.263 ***
(9.919)
{12.630}
[18.483]
63.263 ***
(10.621)
{12.954}
[18.483]
TMM
17.114 *
(16.165)
{20.906}
[25.081]
44.276 ***
(15.907)
{20.853}
[25.081]
47.147 ***
(16.644)
{21.815}
[25.081]
LNTCER
16.315 **
(8.962)
{10.757}
[16.337]
41.112 ***
(9.005)
{10.427}
[16.998]
41.112 ***
(9.976)
{10.900}
[17.983]
Economies 2025,13, 185 23 o 68
Table 7. Con .
H0: LNIPIT Is No G ange Caused by Max_Wald_Fo wa d Max_Wald_Rolling Max_Wald_Recu si e
LNGPR_TUN
14.265 ***
(7.452)
{9.544}
[13.106]
24.817 ***
(7.897)
{9.730}
[15.312]
31.494 ***
(7.900)
{9.730}
[15.312]
No e: *, **, *** signi ican a 10%, 5%, and 1% le els, espec i ely. The 90 h pe cen iles a e be ween pa en heses,
95 h pe cen iles a e be ween b aces, and 99 h pe cen iles a e be ween b acke s. LNIPIT: Indus ial p oduc ion (in
log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money ma ke a e. LNTCER: Real e ec i e
exchange a e (in log). LNGPR_TUN: Local geopoli ical isk (in log).
(a) Fo wa d
(b) Rolling
Figu e 5. Con .
Economies 2025,13, 185 24 o 68
(c)Recu si e
0
2000m1 2005m1 2010m1 2015m1 2020m1
Recu si e expanding Wald es o lnipi G-caused by mm, 1994m1 - 2019m4
wi h 90 h (--) and 95 h (-) pe cen iles o boo s apped es s a is ics
50
40
30
20
10
Figu e 5. The o wa d, olling, and ecu si e e ol ing es esul s wi h lag augmen a ion d = 1 a e
displayed in panels (a–c) wi h a minimum window size = 72. The eg ession model 3 is (LNIPIT
LNM2TND TMM LNTCER LNGPR_TUN)’, allowing o he e oscedas ic e o s. The empi ical size is
5%, con olled o e 1 yea and ob ained om boo s apping. Lag o de s a e assumed o be cons an
and selec ed using AIC(=4) wi h a maximum leng h o 8 o he whole sample pe iod. LNIPIT:
Indus ial p oduc ion (in log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money
ma ke a e. LNTCER: Real e ec i e exchange a e (in log). LNGPR_TUN: Local geopoli ical isk
(in log).
Mac oeconomic a iables signi ican ly a ec consume p ices a he 5% le el (see
Table 8). The o wa d expanding me hod does no ejec he null hypo hesis o no causali y
o LNCPI, indica ing i s lowe eliabili y. In con as , olling and ecu si e expanding
me hods show b ie connec ions be ween TMM and LNCPI, e ealing he money ma -
ke a e’s ine ec i eness du ing o he pe iods (see Figu e 6). These pe iods align wi h
no able poli ical un es in Tunisia, including he 2011 e olu ion, pa liamen a y elec ions,
COVID-19 lockdowns, and go e nance e o m discussions. These obse a ions join hose
ound in Tables 5and 6, and align wi h conc e e ac s. A e he 2011 e olu ion, CBT
ini ially esponded o he dep ecia ion o he dina by in e ening in he o eign exchange
ma ke while s e ilizing hese in e en ions h ough liquidi y injec ions. Al hough hese
ac ions aimed o s abilize he cu ency, hey inad e en ly dis up ed he ansmission o
mone a y policy by al e ing domes ic liquidi y condi ions and diminishing he e ec i eness
o in e es a e adjus men s. Following he e o is a acks in 2015, he au ho i ies shi ed
hei ocus owa d main aining c edi g ow h, injec ing addi ional liquidi y o suppo
lending ac i i ies. Howe e , his esponse unde mined in la ion- a ge ing e o s, esul ing
in a pe iod o disconnec ion in mone a y policy, du ing which in e es a e adjus men s had
a limi ed impac on in la ion and indus ial p oduc ion. I was no un il 2018, wi h a shi
owa d igh e mone a y policies (including inc eases in policy a es and mac op uden ial
igh ening), ha he cen al bank es o ed i s abili y o in luence in la ion and economic
ac i i y mo e e ec i ely, ma king a enewed pe iod o connec ion in mone a y policy
(see Ben Yousse ,2024). Fu he mo e, he su ge in in e na ional p ices accompanied by a
dep ecia ion o he dina explains in la iona y p essu es in he a e ma h o COVID-19.
Economies 2025,13, 185 31 o 68
Figu e 8. Wa ele T ans o m Cohe ence: money ma ke a e (TMM) e sus consume p ice index
(LNCPI). No es: The egions whe e he spec um is signi ican agains ed noise a he 5% le el a e
indica ed by he black ou line. The cone o in luence es ic s he au oco ela ion o wa ele powe
a each scale, and is dis inguished by he ligh e shading ha ma ks egions o high powe . Time
( om 01/1994 o 05/2017) and scale anges (mon hly equency) a e ep esen ed by he ho izon al
and e ical axes, espec i ely. A posi i e (nega i e) ela ionship be ween he a iables is indica ed
by a ows poin ing le ( igh ), which also show ha he a iables a e ou o phase. The i s a iable
x
1
(TMM) is he d i e (o ollowe ) when a ows mo e o he igh and up (o down). Con e sely,
he a iable x2(LNCPI) is leading (o lagging) i he a ows mo e le and up (down).
4.5. Connec ion/Disconnec ion and E ec i eness o Mone a y Policy: Resul s o In e ac ed LPs
We examine he esponse o mac oeconomic a iables o a shock in mone a y policy
while in e ac ing wi h global ( hen local) geopoli ical isk. We use he new command
“locp oj” a ailable in S a a 18. I calcula es bo h linea and non-linea IRFs using he
local p ojec ions app oach ini ially in oduced by Jo dà(2005). This me hod acili a es he
s aigh o wa d applica ion o a ious op ions ound in he expanding body o esea ch on
local p ojec ions. The op imal lag o de selec ion o VAR models used in in e ac ed LPs is
a ailable in he Supplemen a y File (see Tables S6–S9).
Ini ially, he esponse ho e s a ound ze o, showing no clea end a e he shock,
wi h b oad con idence in e als indica ing high unce ain y (see Figu e 9). F om mon hs 2
o 18, esponses luc ua e signi ican ly be ween posi i e and nega i e alues, sugges ing
ins abili y in indus ial p oduc ion due o puzzling in e ac ions be ween mone a y policy
and global geopoli ical isk. By mon h 20, he esponse sha ply ises, bu wide con idence
in e als may signal inc eased isk o subs an ial impac s. O e all, he e a ic esponse
implies ha he money ma ke a e may in luence indus ial p oduc ion inconsis en ly,
depending on geopoli ical isk le els. P elimina y e idence sugges s ha in e es a es
may no e ec i ely ansmi mone a y policy. To d aw a conclusi e iew, we need o
compa e and quan i y he impac o he money ma ke a e on p oduc ion du ing low- and
high-geopoli ical- isk pe iods.

Economies 2025,13, 185 32 o 68
Figu e 9. Response o indus ial p oduc ion (IPITLOGDIFF) o a shock in money ma ke a e (TM-
MDIFF) and allowing o in e ac ion wi h he global geopoli ical isk (GPRLOGDIFF) based on
in e ac ing local p ojec ions. Sample: 01/1993–08/2024. Con ol a iables include money sup-
ply (M2TNDLOGDIFF) and eal e ec i e exchange a e (TCERLOGDIFF). 95% con idence bands
a e shown.
Figu e 10 shows ha he esponse s a s wi h a small, posi i e de ia ion ha quickly
u ns sligh ly nega i e, indica ing mino , sho -li ed in la iona y e ec s esul ing om an
ini ial shock in he money ma ke a e. Be ween mon hs 10 and 11, he esponse luc ua es
a ound ze o wi h small ampli udes and wide con idence in e als, sugges ing limi ed
e ec s on consume p ices. A e mon h 18, he esponse s abilizes nea ze o, indica ing ha
he e ec s o mone a y shocks on consume p ices a e ansi o y and dissipa e o e ime.
The b ie esponses o consume p ices indica e ha he in e ac ion be ween money
ma ke a es and geopoli ical isk has a minimal, empo a y e ec on in la ion dynamics.
Figu e 11 shows ha he esponse s a s wi h a small bu posi i e de ia ion ha
quickly u ns sligh ly nega i e, indica ing small e ec s o he money ma ke a e. The
esponse o indus ial p oduc ion s ill luc ua es be ween posi i e and nega i e alues in
he emaining pe iods (mon hs). The e ec con e ges owa ds ze o in longe ho izons.
The money ma ke a e seems o exe sho and b ie e ec s on p oduc ion, and does
no ac e ec i ely as a mone a y ansmi e mechanism, especially in imes o high local
geopoli ical isk.
We can in e om Figu e 12 ha he impac o he money ma ke a e on consume
p ices is equen ly posi i e in many pe iods (mon hs). A shock in he money ma ke a e
dec eases consume p ices a ound mon hs 6–8 and in mon h 17, be o e es o ing i s posi i e
e ec in longe ho izons, unde lining he ine ec i eness o he in e es a e o coun e
in la iona y p ices.
O e all, he in e ac ed LPs display ola ile IRFs, and can be explained by he dynamic
and he e ogeneous esponses o mac oeconomic a iables o global and local geopoli ical
isk shocks o e ime.
Economies 2025,13, 185 33 o 68
Figu e 10. Response o he consume p ice index (CPILOGDIFF) o a shock in money ma ke a e
(TMMDIFF) and allowing o he in e ac ion wi h he global geopoli ical isk (GPRLOGDIFF) based
on in e ac ed local p ojec ions. Sample: 01/1993–08/2024. Con ol a iables include money supply
(M2TNDLOGDIFF) and eal e ec i e exchange a e (TCERLOGDIFF). The 95% con idence bands
a e shown.
Figu e 11. Response o indus ial p oduc ion (IPITLOGDIFF) o a shock in money ma ke a e
(TMMDIFF) and allowing o in e ac ion wi h he local geopoli ical isk (GPR_TUNLOGDIFF) based
on in e ac ed local p ojec ions. Sample: 01/1993–08/2024. Con ol a iables include money supply
(M2TNDLOGDIFF) and eal e ec i e exchange a e (TCERLOGDIFF). The 95% con idence bands
a e shown.
Economies 2025,13, 185 34 o 68
Figu e 12. Response o he consume p ice index (CPILOGDIFF) o a shock in money ma ke a e
(TMMDIFF) and allowing o in e ac ion wi h he local geopoli ical isk (GPR_TUNLOGDIFF) based
on in e ac ing local p ojec ions. Sample: 01/1993–08/2024. Con ol a iables include money supply
(M2TNDLOGDIFF) and eal e ec i e exchange a e (TCERLOGDIFF). The 95% con idence bands
a e shown.
In he sho e m, he esponses o indus ial p oduc ion and in la ion a e ela i ely
mu ed in Figu es 9and 10. This e lec s he economy’s ini ial igidi y o he lagged e ec s
o global shocks, as i akes ime o economic agen s o adjus hei beha io s o o policy
measu es o ake e ec . The s abili y may also indica e a limi ed immedia e ansmission o
global and spillo e shocks, as domes ic ac o s (e.g., con ac s, p ice s ickiness, o exis ing
in en o ies) bu e he economy agains apid changes.
O e he medium and long e m, he esponses become mo e p onounced and a iable.
This can be a ibu ed o he economy’s g adual adjus men o he shock as expec a ions,
p ices, and policies adap . Vola ili y could a ise as businesses adjus hei p oduc ion le els
o shi s in global demand o supply chain dis up ions caused by he shock. Changes in
global commodi y p ices, exchange a e pass- h ough, o heigh ened unce ain y can lead
o ola ile in la iona y p essu es. Cen al banks migh al e in e es a es dynamically in
esponse o economic condi ions, c ea ing ola ili y in mone a y policy a iables. These
luc ua ions e lec economic unce ain y and he a ying e ec i eness o policy esponses
o e ime. The IRFs o LPs in Figu es 11 and 12 sugges ha he cen al bank eac s wi h
a igh ening mone a y policy ha has a empo a y desi able impac on p oduc ion in he
sho e m (a ound ho izon 1) and on in la ion o e he long e m only (a ound ho izon
19). These indings sugges he limi ed e iciency o ine ec i eness o mone a y policy as a
ansmi ing mechanism.
4.6. E ec i eness o Mone a y Policy Unde Tes : Resul s o he Long-Run Dynamics o VECM
The p e ious de elopmen s emphasize he nonlinea e ec s o mone a y shocks on
indus ial p oduc ion and consume p ices. The sensi i i y o hese esul s can be assessed
o quan i y he signi icance and he magni ude o mone a y policy du ing episodes o
Economies 2025,13, 185 35 o 68
low and high geopoli ical isk. This is possible ia he Vec o E o Co ec ion Model
(VECM), which accommoda es I(1) a iables ha a e coin eg a ed. VAR lag o de selec ion
o VECM is pe o med, and he ela ed esul s a e a ailable in he Supplemen a y File
(see Tables S10–S19). The Johansen coin eg a ion es esul s, including ace s a is ic and
maximum eigen alue, a e eadable om Tables S20–S29, con i ming he use o VECM.
The gene al o m o VECM (p−1) is gi en by
∆y =Γ(y −1−α−X −1β)+∑p−1
i=1Θi∆y −i+∑p−1
i=1Y′i∆X −i+µ (12)
We augmen Equa ion (12) o le he money ma ke a e (TMM) in e ac wi h global
geopoli ical isk (LNGPR) and local geopoli ical isk (LNGPR-TUN), espec i ely.
∆y =Γ(y −1−α−X −1β−τTMM −1×LNGPR −1)
| {z }
ECT −1
+
p−1
∑
i=1
Θi∆y −i+
p−1
∑
i=1
Y′i∆X −i+
p−1
∑
i=1
πi∆TMM ×LNGPR) −i+θ+µ
(13)
whe e y is he dependen a iable and X is a ec o o con ol a iables, encompassing
LNM2TND and LNTCER, in addi ion o he a iable o in e es TMM and he geopoli ical
isk measu es (LNGPR, LNGPR_TUN);
Γ
is he coe icien associa ed o
ECT −1
,
α
,
β
a e
he in e cep and he long- un coe icien s,
Θi
is he sho - un coe icien s o he lagged
∆y
,
Y′i
is a ec o o he sho - un coe icien s o he lagged
∆X
,
p
is he op imal lag in he VAR
model acco ding o AIC, and µ is a whi e noise p ocess.
The no malized long- un equa ion is w i en as ollows:
y −1=α+X −1β+τTMM −1×LNGPR −1+ECT −1
Thus, he coe icien s in he coin eg a ing equa ion o he VECM esul s should be
sign- e e sed (Johansen,1995).
4.6.1. VECM wi h an In e ac ion Te m o he Money Ma ke Ra e and Global
Geopoli ical Risk
Speci ica ions di e acco ding o assump ions abou he in e cep and da a end.
Choices based on he log-likelihood, Akaike, and Schwa z a e un eliable because he
es ima ions di e in he numbe s o pa ame e s. Ou ocal poin is he sign and he
s a is ical signi icance o Coin Eq1 (ECT). Bane jee e al. (1998) said i should be nega i e
and s a is ically signi ican . Thus, only models ha sa is y his condi ion a e selec ed. The
speed o adjus men o he indus ial p oduc ion (LNIPIT) o i s equilib ium is abou 11.9%
(see Table 10). Taking he i s model, we can w i e he coin eg a ing equa ion as ollows:
Coin Eq1
| {z }
ECT −1
=LNIPIT_{ −1}
| {z }
y −1
−5.917379
| {z }
α−0.139080
| {z }
β1
×LNM2TND_{ −1}
| {z }
x1 −1
−(−0.687007)
| {z }
β2
×TMM_{ −1}
| {z }
x2 −1
−0.166795
| {z }
β3
×LNTCER_{ −1}
| {z }
x3 −1
−(−0.775811)
| {z }
β4
×LNGPR_{ −1}
| {z }
x4 −1
−0.147131
| {z }
τ×TMM_GPR_{ −1}
| {z }
TMM −1×LNGPR −1
We can a ange i as:
LNIPIT_{ −1} = 0.139080 ×LNM2TND_{ −1} −0.687007 ×TMM_{ −1} + 0.166795 ×LNTCER_{ −1} −
0.775811 ×LNGPR_{ −1} + 0.147131 ×TMM_GPR_{ −1} + 5.917379 + Coin Eq1
The ull VECM equa ion is gi en by:
Economies 2025,13, 185 36 o 68
∆LNIPIT_
| {z }
∆y
−0.119696 ×Coin Eq1_{ −1}
| {z }
ΓECT −1
−0.540895 ×∆LNIPIT_{ −1}
| {z }
θ1∆y −1
−0.275815 ×∆LNIPIT_{ −2}
| {z }
θ2∆y −2
+0.052799 ×∆LNIPIT_{ −3}
| {z }
θ3∆y −3
−0.736189 ×∆LNM2TND_{ −1}
| {z }
γ11∆x1 −1
−0.843190 ×∆LNM2TND_{ −2}
| {z }
γ12∆x1 −2
+0.378443 ×∆LNM2TND_{ −3}
| {z }
γ13∆x1 −3
+0.075635 ×∆TMM_{ −1}
| {z }
γ21∆x2 −1
+0.018123 ×∆TMM_{ −2}
| {z }
γ22∆x2 −2
+0.060180 ×∆TMM_{ −3}
| {z }
γ23∆x2 −3
+0.072316 ×∆LNTCER_{ −1}
| {z }
γ31∆x3 −1
+0.038912 ×∆LNTCER_{ −2}
| {z }
γ32∆x3 −2
+0.193727 ×∆LNTCER_{ −3}
| {z }
γ33∆x3 −3
+0.135994 ×∆LNGPR_{ −1}
| {z }
γ41∆x4 −1
+0.036086 ×∆LNGPR_{ −2}
| {z }
γ42∆x4 −2
+0.052291 ×∆LNGPR_{ −3}
| {z }
γ43∆x4 −3
−0.020789 ×∆TMM_GPR_{ −1}
| {z }
π1∆(TMM×LNGPR) −1
−0.006963 ×∆TMM_GPR_{ −2}
| {z }
π2∆(TMM×LNGPR) −2
−0.009701 ×∆TMM_GPR_{ −3}
| {z }
π3∆(TMM×LNGPR) −3
+0.012483
| {z }
θ
The emaining columns o Table 10 can be in e ed in a simila way.
Bo h he money ma ke a e (D(TMM)) and he eal e ec i e exchange a e (D(LNTCER))
ha e insigni ican e ec s on sho - e m p oduc ion. The money supply D(LNM2TND) has
an uns able coe icien on D(LNIPT), con i ming ine ec i e mone a y a ge ing h ough
he money supply (End e al.,2020). Tu ning o he long- un coe icien s, we obse e
u he in e es ing esul s. Impo an ly, he coe icien o he in e ac ion e m (TMM_GPR)
is posi i e and s a is ically signi ican . Unde a low global GPR egime (10 h pe cen ile),
he ma ginal e ec o mone a y policy is abou a 0.07% dec ease in indus ial p oduc ion,
while i pe ains o a 0.04% inc ease unde a high global GPR egime (90 h pe cen ile). The
magni ude is small unde bo h egimes, and his sugges s ha he e a e some bene i s o e
global geopoli ical ensions. This esul may be explained by he g een ene gy ansi ion
plan o Tunisia in he ollowing yea s. Tigh ening mone a y policy encou ages some i ms
o in es in enewable ene gy as an al e na i e o ossil uels. Howe e , we should no e
ha his esul applies o indus ial p oduc ion a he agg ega e le el. By es ablishing a
sec o al analysis, we conside manu ac u ing, mines, and ene gy sec o s. We e-es ima e
he VECM o he LNIPIT o each sec o . We ind ha p oduc ion imp o es in he case
o igh ening in e es a es unde a high global GPR egime in he manu ac u ing sec o ,
while he e ec is insigni ican o he mines sec o , and dampened in he case o he ene gy
sec o , consis en wi h he i e e sibili y heo y. In es o s’ con idence diminishes and
hen delays in es men un il ge ing addi ional in o ma ion. Fo he manu ac u ing sec o ,
igh mone a y policy can cu b exchange a e ola ili y by a ac ing o eign in es men o
p e en ing capi al ligh , educing he cos o impo s o manu ac u e s, and suppo ing
p oduc ion. Long- e m bene i s a e ende ed wi h s a egies le e aged owa d sus ainable
economic g ow h (Udeaja e al.,2024). Ne e heless, he impac o TMM is asymme ic, and
his sugges s ha cen al bank in o ma ion migh be agmen ed be ween economic agen s
(T abelsi,2024b). While some p e e basic in o ma ion, o he s p e e de ailed in o ma ion.
Some unde s and he message, while o he s ge con used (Fila do,2004).

Economies 2025,13, 185 37 o 68
Table 10. VECM long- un and sho - un dynamics. Dependen a iable: D(LNIPIT), model 1.
Model (1) (2) (3) (4.1) (4.2) (4.3) (4.4)
Sec o Agg ega e Manu ac u ing Mines Ene gy Ene gy Ene gy Ene gy
Assump ion
In e cep (no
end) in CE
and VAR
In e cep (no
end) in CE
and VAR
In e cep (no
end) in CE
and VAR
No In e cep
(no end) in
CE, no
in e cep in
VAR in CE o
VAR
In e cep (no
end) in CE
and VAR
In e cep and
end in CE,
(no end) in
VAR
In e cep and
end in CE,
linea end in
VAR
Coin eg a ing Eq: Coin Eq1 Coin Eq1 Coin Eq1 Coin Eq1 Coin Eq1 Coin Eq1 Coin Eq1
LNIPIT(−1) 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
LNM2TND(−1) −0.139080 −0.048256 0.016906 −0.176541 0.219828 1.202578 1.202381
(0.07549) (0.03569) (0.39386) (0.13619) (0.05968) (0.42672) (0.42757)
[−1.84240] * [−1.35191] [0.04292] [−1.29630] [3.68359] *** [2.81821] *** [2.81215] ***
TMM(−1) 0.687007 0.662645 −2.947754 0.601768 2.194548 3.011706 3.012804
(0.19380) (0.18271) (1.92353) (0.68731) (0.26546) (0.35757) (0.35828)
[3.54490] *** [3.62671] *** [−1.53247] [0.87554] [8.26681] *** [8.42271] *** [8.40906] ***
LNTCER(−1) −0.166795 0.397270 −0.926807 −0.849423 0.327828 0.614311 0.615774
(0.28689) (0.15236) (1.68017) (0.60917) (0.25454) (0.31231) (0.31293)
[−0.58138] [2.60744] *** [−0.55161] [−1.39440] [1.28793] [1.96702] ** [1.96779] **
LNGPR(−1) 0.775811 0.785330 −5.311770 0.351307 2.847313 3.916134 3.917429
(0.26151) (0.23646) (2.48708) (0.89728) (0.34349) (0.46128) (0.46220)
[2.96668] *** [3.32125] *** [−2.13574] ** [0.39152] [8.28932] *** [8.48970] *** [8.47566] ****
TMM_GPR(−1) −0.147131 −0.140071 0.663731 −0.108839 −0.470951 −0.645655 −0.645905
(0.04342) (0.04041) (0.42482) (0.15067) (0.05853) (0.07776) (0.07791)
[−3.38864] *** [−3.46663] *** [1.56239] [−0.72237] [−8.04693] *** [−8.30358] *** [−8.29031] ***
@TREND(93M01) −21.53170 −0.007256 −0.007200
(0.00331)
[−2.18909] **
C−5.917379 −9.563199 23.79726 −36.60262 −36.62569
E o Co ec ion: D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT)
Coin Eq1 −0.119696 −0.523844 −0.074028 −0.015315 −0.202163 −0.137137 −0.137027
(0.04849) (0.08097) (0.03282) (0.00677) (0.03772) (0.03065) (0.03070)
[−2.46850] ** [−6.46972] *** [−2.25529] ** [−2.26256] ** [−5.36008] *** [−4.47387] *** [−4.46315]
D(LNIPIT(−1)) −0.540895 −0.047386 −0.125126 −0.216884 −0.079574 −0.114177 −0.114335
(0.07175) (0.08163) (0.05884) (0.05927) (0.06286) (0.06261) (0.06272)
[−7.53845] *** [−0.58051] [−2.12644] ** [−3.65922] *** [−1.26582] [−1.82374] * [−1.82304] *
D(LNIPIT(−2)) −0.275815 −0.138224 −0.288186
(0.07143) (0.06854) (0.05799)
[−3.86160] *** [−2.01671] ** [−4.96959] ***
D(LNIPIT(−3)) 0.052799 0.099777
(0.05725) (0.05809)
[0.92219] [1.71762] *
D(LNM2TND(
−
1))
−0.736189 −0.480065 −0.634285 1.159464 0.930394 0.974995 0.971238
(0.20089) (0.41475) (1.89729) (0.46158) (0.43912) (0.44556) (0.44673)
[−3.66469] *** [−1.15748] [−0.33431] [2.51193] ** [2.11876] ** [2.18823] ** [2.17410] **
D(LNM2TND(
−
2))
−0.843190 −0.086415 −4.632534
(0.20680) (0.42324) (1.86743)
[−4.07733] *** [−0.20417] [−2.48070] **
D(LNM2TND(
−
3))
0.378443 1.411576
(0.20974) (0.41150)
[1.80435] * [3.43033] ***
D(TMM(−1)) 0.075635 0.249764 0.199425 0.273808 0.467566 0.458733 0.459059
(0.06115) (0.09770) (0.41505) (0.09340) (0.09654) (0.09977) (0.09996)
[1.23692] [2.55650] ** [0.48049] [2.93144] *** [4.84300] *** [4.59811] *** [4.59255] ***
D(TMM(−2)) 0.018123 0.157388 0.630794
(0.05963) (0.10214) (0.41527)
[0.30392] [1.54084] [1.51899]
D(TMM(−3)) 0.060180 0.464475
(0.05561) (0.10090)
[1.08218] [4.60332] ***
D(LNTCER(−1)) 0.072316 −0.110070 −1.374431 −0.330005 −0.271923 −0.230172 −0.219582
(0.26655) (0.38558) (1.76869) (0.43291) (0.41552) (0.42189) (0.42597)
[0.27130] [−0.28546] [−0.77709] [−0.76230] [−0.65442] [−0.54558] [−0.51549]
D(LNTCER(−2)) 0.038912 0.164198 0.058016
(0.26749) (0.39053) (0.52582)
[0.14547] [0.42045] [0.11033]
D(LNTCER(−3)) 0.193727 −0.092339
(0.26547) (0.38292)
[0.72974] [−0.24115]
Economies 2025,13, 185 38 o 68
Table 10. Con .
Model (1) (2) (3) (4.1) (4.2) (4.3) (4.4)
D(LNGPR(−1)) 0.135994 0.249884 0.058016 0.362575 0.645630 0.635707 0.634930
(0.07623) (0.12195) (0.52582) (0.11700) (0.12348) (0.12847) (0.12876)
[1.78390] * [2.04903] ** [0.11033] [3.09894] *** [5.22866] *** [4.94813] *** [4.93111] ***
D(LNGPR(−2)) 0.036086 0.210534 0.566309
(0.07504) (0.12771) (0.52834)
[0.48086] [1.64859]* [1.07187]
D(LNGPR(−3)) 0.052291 0.610319
(0.07042) (0.12574)
[0.74255] [4.85397] ***
D(TMM_GPR(−1)) −0.020789 −0.042908 −0.020253 −0.058809 −0.106666 −0.104567 −0.104439
(0.01309) (0.02083) (0.08839) (0.01983) (0.02096) (0.02177) (0.02181)
[−1.58783] [−2.05965] ** [−0.22912] [−2.96500] *** [−5.08930] *** [−4.8042 7]*** [−4.78777] ***
D(TMM_GPR(−2)) −0.006963 −0.036268 −0.097004
(0.01267) (0.02178) (0.08943)
[−0.54952] [−1.66516] * [−1.08467]
D(TMM_GPR(−3)) −0.009701 −0.111571
(0.01189) (0.02171)
[−0.81603] [−5.13952] ***
C 0.012483 −0.005028 0.037538 −0.008248 −0.008611 −0.007730
(0.00444) (0.00774) (0.02947) (0.00557) (0.00565) (0.01379)
[2.81442] *** [−0.65000] [1.27367] [−1.47967] [−1.52286] [−0.56053]
@TREND(93M01) −3.70 ×10−6
(5.4 ×10−5)
[−0.06813]
R-squa ed 0.442153 0.455397 0.178527 0.093404 0.170169 0.145902 0.145995
Adj. R-squa ed 0.404299 0.416202 0.139120 0.073908 0.149274 0.124396 0.121331
Sum sq. esids 0.586395 1.139237 25.20230 1.624630 1.487067 1.530554 1.530387
S.E. equa ion 0.045763 0.065691 0.304955 0.076309 0.073138 0.074200 0.074329
F-s a is ic 11.68050 11.61877 4.530397 4.790779 8.143989 6.784223 5.919267
Log likelihood 509.9499 380.6649 −58.75603 333.5953 346.2472 342.1254 342.1410
Akaike AIC −3.266333 −2.539893 0.510569 −2.283884 −2.365365 −2.336541 −2.329657
Schwa z SC −3.019414 −2.282923 0.689989 −2.194401 −2.263099 −2.234276 −2.214609
Mean dependen 0.000976 0.001075 −0.002806 −0.001039 −0.001039 −0.001039 −0.001039
S.D. dependen 0.059293 0.085975 0.328673 0.079295 0.079295 0.079295 0.079295
No es: S anda d e o s a e be ween pa en heses, -s a is ics a e be ween b acke s. *, ** and *** deno e he s a is ical
signi icance a 10%, 5%, and 1%, espec i ely. C i ical alues o -S uden : 1% (2.56), 5% (1.96), 10% (1.645).
@T end(93M01) ep esen s a linea ime end s a ing in he i s mon h o 1993. C is a cons an . (
−
1) co esponds
o he one-lagged pe iod ( a iable obse ed a
−
1). (
−
2) co esponds o he wo-lagged pe iods ( a iable
obse ed a
−
2)). (
−
3) co esponds o he h ee-lagged pe iods ( a iable obse ed a
−
3). D is he i s -di e ence
ope a o . LNIPIT: Indus ial p oduc ion (in log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM:
Money ma ke a e. LNTCER: Real e ec i e exchange a e (in log). LNGPR: Global geopoli ical isk (in log).
Resul s ela ed o consume p ices a e a ailable in Table 11. Two speci ica ions among
he i e assump ions abou he in e cep and da a end mee he condi ion ela ed o
Coin Eq1. The coe icien is nega i e and s a is ically signi ican a leas a he 10% le el.
Table 11. VECM long- un and sho - un dynamics. Dependen a iable: D(LNCPI), model 2.
Model (1) (2)
Assump ion In e cep and end in CE,
(no end) in VAR
In e cep and end in CE,
linea end in VAR
Coin eg a ing Eq: Coin Eq1 Coin Eq1
LNCPI(−1) 1.000000 1.000000
LNM2TND(−1) 3.941814 1.071441
(0.83458) (0.18278)
[4.72311] *** [5.86192] ***
Economies 2025,13, 185 39 o 68
Table 11. Con .
Model (1) (2)
TMM(−1) 6.497262 1.530112
(0.75540) (0.16544)
[8.60105] *** [9.24876] ***
LNTCER(−1) 2.107225 0.089692
(0.67091) (0.14694)
[3.14083] *** [0.61041]
LNGPR(−1) 8.364861 2.032107
(0.97052) (0.21255)
[8.61896] *** [9.56052] ***
TMM_GPR(−1) −1.423292 −0.339291
(0.16450) (0.03603)
[−8.65215] *** [−9.41763] ***
@TREND(93M01) −0.032303 −0.011586
(0.00653)
[−4.94647] ***
C−86.99894 −23.01158
E o Co ec ion: D(LNCPI) D(LNCPI)
Coin Eq1 −0.002614 −0.004810
(0.00058) (0.00265)
[−4.53760] *** [−1.81743] *
D(LNCPI(−1)) 0.225689 0.142239
(0.05707) (0.05799)
[3.95439] *** [2.45290] **
D(LNM2TND(−1)) 0.014511 0.019536
(0.01539) (0.01492)
[0.94262] [1.30916]
D(TMM(−1)) 0.013645 0.008224
(0.00398) (0.00400)
[3.42578] *** [2.05790] **
D(LNTCER(−1)) 0.011035 0.003668
(0.01672) (0.01619)
[0.65997] [0.22652]
D(LNGPR(−1)) 0.018019 0.011168
(0.00520) (0.00523)
[3.46304] *** [2.13714] **
D(TMM_GPR(−1)) −0.003173 −0.001991
(0.00088) (0.00088)
[−3.59293] *** [−2.25568] **
Economies 2025,13, 185 40 o 68
Table 11. Con .
Model (1) (2)
C 0.002740 −9.27 ×10−5
(0.00029) (0.00049)
[9.28797] *** [−0.18803]
@TREND(93M01) 1.39 ×10−5
(2.1 ×10−6)
[6.49749] ***
R-squa ed 0.156242 0.216031
Adj. R-squa ed 0.136015 0.194479
Sum sq. esids 0.002577 0.002395
S.E. equa ion 0.002971 0.002869
F-s a is ic 7.724405 10.02353
Log likelihood 1324.041 1335.066
Akaike AIC −8.773609 −8.840439
Schwa z SC −8.674841 −8.729325
Mean dependen 0.003646 0.003646
S.D. dependen 0.003196 0.003196
No es: S anda d e o s a e be ween pa en heses, -s a is ics a e be ween b acke s. *, ** and *** deno e he s a is ical
signi icance a 10%, 5%, and 1%, espec i ely. C i ical alues o -S uden : 1% (2.56), 5% (1.96), 10% (1.645).
@T end(93M01) ep esen s a linea ime end s a ing in he i s mon h o 1993. C is a cons an . (
−
1) co esponds
o he one-lagged pe iod ( a iable obse ed a
−
1). D is he i s -di e ence ope a o . LNCPI: Consume p ice
index (in log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money ma ke a e. LNTCER: Real
e ec i e exchange a e (in log). LNGPR: Global geopoli ical isk (in log).
Money supply D(LNM2TND) and he eal e ec i e exchange a e ha e no signi ican
impac on D(LNCPI) in he sho un. The e is a empo a y in la iona y imp o emen
unde high global geopoli ical isk when igh ening mone a y policy, as es i ied by he
nega i e coe icien o he in e ac ion e m and a posi i e coe icien o D(TMM). Tigh ening
mone a y policy (e.g., aising in e es a es o educing money supply) educes agg ega e
demand by inc easing bo owing cos s and discou aging consump ion. In he sho un, his
lowe demand helps educe in la iona y p essu es. In he long un, highe money supply
(LNM2TND) dec eases consume p ices (LNCPI). The esul con adic s he quan i y heo y
o money (M. F iedman,1961,1968,1970) and sugges s he ine ec i eness o mone a y
a ge ing (End e al.,2020). The s a is ical signi icance o LNTCER is no s able ac oss
speci ica ions, highligh ing an ambiguous ole o he eal e ec i e exchange a e (Agéno
& Mon iel,2007). Fo he in e ac ion e m (TMM_GPR), he coe icien is nega i e and
s a is ically signi ican , in addi ion o a nega i e coe icien associa ed wi h TMM. Unde
low global geopoli ical isk, inc easing he money ma ke a e leads o a dec ease by 0.59%
in consume p ices agains an inc ease o 0.53% unde high global geopoli ical isk. High
global geopoli ical isk ends o inc ease he ola ili y o in e na ional commodi y p ices
(e.g., ene gy and ood) and dis up global supply chains. O e ime, hese luc ua ions can
nega e he ini ial bene i s o mone a y igh ening, pa icula ly i Tunisia’s mone a y policy
canno add ess supply-side shocks. I iscal imbalances pe sis , he in la iona y p essu es
o igh ening mone a y policy could ou weigh i s in ended s abilizing e ec s. Meanwhile,
highe in e es a es migh push i ms o adop mo e e icien p oduc ion me hods o in es
in echnologies ha imp o e p oduc i i y, leading o long- e m indus ial g ow h. The
pa adoxical mone a y ansmission mechanism signals di e ences in how economic agen s
Economies 2025,13, 185 47 o 68
Table 12. Con .
Model (1) (2.1) (2.2) (3.1) (3.2) (3.3) (4.1) (4.2)
D(TMM_GPR_TUN(
−
1))
0.026808 −0.262980 −0.263740 −1.707027 −1.709679 −1.698611 −0.057881 −0.063615
(0.08860) (0.17717) (0.17751) (0.77396) (0.76381) (0.76513) (0.14132) (0.14133)
[0.30258] [−1.48438] [−1.48581] [−2.20556] ** [−2.23836] ** [−2.22003] ** [−0.40957] [−0.45010]
D(TMM_GPR_TUN(
−
2))
0.049527 −0.167405 −0.167995 −1.786492 −1.783644 −1.772201 0.009650 0.005527
(0.10478) (0.17164) (0.17199) (0.76593) (0.75966) (0.76098) (0.13345) (0.13353)
[0.47268] [−0.97531] [−0.97678] [−2.33245] ** [−2.34797] ** [−2.32884] ** [0.07231] [0.04139]
D(TMM_GPR_TUN(
−
3))
0.089322 −0.152972 −0.153419 −0.564044 −0.554843 −0.548281 0.057940 0.057640
(0.10108) (0.16427) (0.16459) (0.77559) (0.77140) (0.77257) (0.12126) (0.12131)
[0.88367] [−0.93124] [−0.93211] [−0.72725] [−0.71927] [−0.70969] [0.47782] [0.47513]
D(TMM_GPR_TUN(
−
4))
0.086691 −0.179598 −0.179809 −0.955703 −0.939649 −0.939773
(0.08183) (0.14575) (0.14604) (0.75946) (0.75756) (0.75863)
[1.05937] [−1.23227] [−1.23122] [−1.25839] [−1.24037] [−1.23878]
D(TMM_GPR_TUN(
−
5))
−0.276489 −0.276538 −0.541191 −0.529185 −0.539418
(0.12310) (0.12336) (0.68560) (0.68494) (0.68614)
[−2.24602] ** [−2.24175] ** [−0.78937] [−0.77260] [−0.78616]
D(TMM_GPR_TUN(
−
6))
0.104012 0.109391 0.104060
(0.56265) (0.56224) (0.56311)
[0.18486] [0.19456] [0.18479]
C−0.004008 −0.006034 −0.010384 −0.015309 0.005845 0.009081 0.021045
(0.01117) (0.01926) (0.06051) (0.06098) (0.09612) (0.00880) (0.01658)
[−0.35893] [−0.31334] [−0.17160] [−0.25103] [0.06081] [1.03183] [1.26959]
@TREND(93M01) 8.31 ×10−6−6.04 ×10−5−4.92 ×10−5
(5.6 ×10−5)(0.00025) (5.6 ×10−5)
[0.14960] [−0.23720] [−0.87587]

Economies 2025,13, 185 48 o 68
Table 12. Con .
Model (1) (2.1) (2.2) (3.1) (3.2) (3.3) (4.1) (4.2)
R-squa ed 0.475361 0.443073 0.443126 0.303078 0.304132 0.305105 0.190371 0.191801
Adj. R-squa ed 0.429407 0.373736 0.371271 0.194275 0.195494 0.193215 0.132102 0.130341
Sum sq. esids 0.551347 1.163108 1.162997 21.35113 21.31884 21.28902 1.434947 1.432412
S.E. equa ion 0.044858 0.068346 0.068480 0.300149 0.299921 0.300346 0.073725 0.073800
F-s a is ic 10.34433 6.390191 6.166966 2.785582 2.799503 2.726832 3.267121 3.120749
Log likelihood 516.9645 372.2382 372.2516 −38.80395 −38.59584 −38.40337 347.8957 348.1467
Akaike AIC −3.290732 −2.421624 −2.414602 0.558574 0.557061 0.562934 −2.309124 −2.303850
Schwa z SC −2.981331 −2.007292 −1.987322 1.058346 1.056833 1.075857 −2.052154 −2.034032
Mean dependen 0.001031 0.000907 0.000907 −0.003196 −0.003196 −0.003196 −0.000791 −0.000791
S.D. dependen 0.059385 0.086364 0.086364 0.334382 0.334382 0.334382 0.079137 0.079137
No es: S anda d e o s a e be ween pa en heses, -s a is ics a e be ween b acke s. *, ** and *** deno e he s a is ical signi icance a 10%, 5%, and 1%, espec i ely. C i ical alues o
-S uden : 1% (2.56), 5% (1.96), 10% (1.645). @T end(93M01) ep esen s a linea ime end s a ing in he i s mon h o 1993. C is a cons an . (
−
1) co esponds o he one-lagged pe iod
( a iable obse ed a
−
1). (
−
2) co esponds o he wo-lagged pe iods ( a iable obse ed a
−
2)). (
−
3) co esponds o he h ee-lagged pe iods ( a iable obse ed a
−
3). (
−
4)
co esponds o he ou -lagged pe iods ( a iable obse ed a
−
4). (
−
5) co esponds o he i e-lagged pe iods ( a iable obse ed a
−
5). (
−
6) co esponds o he six-lagged pe iods
( a iable obse ed a
−
6). D is he i s -di e ence ope a o . LNIPIT: Indus ial p oduc ion (in log). LNM2TND: Mone a y agg ega es in TND (in log). TMM: Money ma ke a e.
LNTCER: Real e ec i e exchange a e (in log). LNGPR_TUN: Local geopoli ical isk (in log).
Economies 2025,13, 185 49 o 68
Table 13. VECM long- un and sho - un dynamics. Dependen a iable: D(LNCPI).
Model (1)
Assump ion In e cep and end in CE, (no end) in VAR
Coin eg a ing Eq: Coin Eq1
LNCPI(−1) 1.000000
LNM2TND(−1) −1.838852
(1.25620)
[−1.46382]
TMM(−1) 0.376102
(0.35593)
[1.05667]
LNTCER(−1) 1.626655
(5.22719)
[0.31119]
LNGPR_TUN(−1) 212.6330
(50.1796)
[4.23744] ***
TMM_GPR_TUN(−1) −54.34083
(11.1719)
[−4.86407] ***
C 6.806815
E o Co ec ion: D(LNCPI)
Coin Eq1 −0.000311
(9.0 ×10−5)
[−3.46473] ***
D(LNCPI(−1)) 0.260058
(0.06020)
[4.31971] ***
D(LNCPI(−2)) −0.098718
(0.05990)
[−1.64800] *
D(LNCPI(−3)) 0.227855
(0.06002)
[3.79617] ***
D(LNCPI(−4)) −0.105617
(0.05970)
[−1.76904] *
D(LNM2TND(−1)) 0.014519
(0.01592)
[0.91217]
Economies 2025,13, 185 50 o 68
Table 13. Con .
Model (1)
D(LNM2TND(−2)) 0.002582
(0.01645)
[0.15694]
D(LNM2TND(−3)) 0.023600
(0.01648)
[1.43172]
D(LNM2TND(−4)) 0.023508
(0.01619)
[1.45219]
D(TMM(−1)) −4.68 ×10−5
(0.00130)
[−0.03591]
D(TMM(−2)) 0.000957
(0.00133)
[0.71789]
D(TMM(−3)) 0.002958
(0.00133)
[2.21989] **
D(TMM(−4)) 0.000619
(0.00130)
[0.47449]
D(LNTCER(−1)) 0.001442
(0.01768)
[0.08156]
D(LNTCER(−2)) 0.005424
(0.01786)
[0.30368]
D(LNTCER(−3)) −0.007315
(0.01784)
[−0.41005]
D(LNTCER(−4)) −0.008215
(0.01769)
[−0.46444]
D(LNGPR_TUN(−1)) 0.050314
(0.02559)
[1.96629] **
D(LNGPR_TUN(−2)) 0.070708
(0.02652)
[2.66582] ***
Economies 2025,13, 185 51 o 68
Table 13. Con .
Model (1)
D(LNGPR_TUN(−3)) 0.046155
(0.02576)
[1.79177] *
D(LNGPR_TUN(−4)) 0.016653
(0.02251)
[0.73976]
D(TMM_GPR_TUN(−1)) −0.014463
(0.00579)
[−2.49694] **
D(TMM_GPR_TUN(−2)) −0.017414
(0.00581)
[−2.99739] ***
D(TMM_GPR_TUN(−3)) −0.012506
(0.00557)
[−2.24723] **
D(TMM_GPR_TUN(−4)) −0.005134
(0.00486)
[−1.05572]
C 0.002088
(0.00050)
[4.21679] ***
R-squa ed 0.242049
Adj. R-squa ed 0.172893
Sum sq. esids 0.002315
S.E. equa ion 0.002907
F-s a is ic 3.500033
Log likelihood 1340.128
Akaike AIC −8.760855
Schwa z SC −8.439861
Mean dependen 0.003646
S.D. dependen 0.003196
No es: S anda d e o s a e be ween pa en heses, -s a is ics a e be ween b acke s. *, ** and *** deno e he s a is ical
signi icance a 10%, 5%, and 1%, espec i ely. C i ical alues o -S uden : 1% (2.56), 5% (1.96), 10% (1.645).
@T end(93M01) ep esen s a linea ime end s a ing in he i s mon h o 1993. C is a cons an . (
−
1) co esponds
o he one-lagged pe iod ( a iable obse ed a
−
1). (
−
2) co esponds o he wo-lagged pe iods ( a iable
obse ed a
−
2)). (
−
3) co esponds o he h ee-lagged pe iods ( a iable obse ed a
−
3). (
−
4) co esponds o
he ou -lagged pe iods ( a iable obse ed a
−
4). D is he i s -di e ence ope a o . LNCPI: Consume p ice
index (in log). LNM2TND: Mone a y Agg ega es 2 in TND (in log). TMM: Money ma ke a e. LNTCER: Real
e ec i e exchange a e (in log), LNGPR_TUN: Local geopoli ical isk (in log).
In conclusion, we ha e examined he se ial co ela ion o he esiduals and ound
no eason o ejec he null hypo hesis o no au oco ela ion a a 5% le el o signi icance,
indica ing ha he VECM is applicable in all models, excep Model 1.4.1 in Table 10 (colo ed
in yellow) (see Tables S30–S39 in he Supplemen a y File).
Economies 2025,13, 185 52 o 68
4.7. Discussion
The VECM indings sugges a mix u e o heo ies ha in e ac ed o con ibu e o he
ansmission mechanism o he money ma ke a e. We p o ide key akeaways below.
4.7.1. I e e sibili y Theo y
The i e e sibili y heo y sugges s ha i ms delay in es men decisions when aced
wi h unce ain y because o he high cos o e e sing such in es men s. Howe e , in he
case o igh ening in e es a es in Tunisia, he ollowing occu s:
•
Imp o emen in agg ega e p oduc ion—Highe in e es a es may c ea e a mo e s able
mac oeconomic en i onmen by educing specula i e ac i i y and in la iona y p es-
su es in he long un. This s abili y could encou age i ms o in es in p oduc i e
ac i i ies despi e highe bo owing cos s, especially i unce ain y is educed;
•
Inc eased consume p ices—I i ms pass on highe bo owing cos s o consume s ia
highe p ices, i e lec s igidi y in p icing decisions. This aligns wi h he i e e sibili y
heo y, whe e i ms, once commi ed o p oduc ion and p icing s a egies, ind i
di icul o e e se cou se quickly in esponse o changing mone a y policy.
4.7.2. C edi Channel Theo y
The c edi channel heo y highligh s how mone a y policy a ec s he economy h ough
i s impac on c edi a ailabili y and cos . I has wo key mechanisms (Ma eu ,2005):
•
Bank lending channel—Tigh ening in e es a es educe bank ese es and loanable
unds, inc easing he cos o bo owing. This migh ini ially cons ain in es men and
p oduc ion, bu o e ime, i ms ha su i e he c edi squeeze become mo e e icien ,
leading o long- e m imp o emen s in agg ega e p oduc ion;
•
Balance shee channel: Highe in e es a es inc ease he cos o se icing deb , wo s-
ening he inancial heal h o bo owe s. This could lead i ms o pass on hese cos s o
consume s, con ibu ing o highe consume p ices. Addi ionally, households migh
educe consump ion due o highe deb bu dens, ye his impac seems o e shadowed
by in la iona y dynamics.
4.8. Robus ness Check: In es o Sen imen Sensi i i y o Mone a y Policy Changes
Mone a y policy aims o achie e wo p ima y goals: main aining p ice s abili y and
os e ing sus ainable economic g ow h (Ma schne & Ce e a,2021). These goals can
be ealized h ough he in luence o mone a y policy on inancial ma ke s, including
s ock ma ke s (Ku o ,2010). Fu he mo e, unce ain y and isk shocks can ad e sely
a ec businesses by de e ing in es men and p oduc ion, and impac households by
dec easing hei consump ion endencies. The indings sugges ha acking in es o
sen imen ega ding he ma ke can p o ide insigh s in o hei inancial decisions, making i
a aluable indica o o p edic ing he ajec o y o he Tunisian economy. Fo his pu pose,
we ha e collec ed mon hly da a on in es o sen imen (IS1) using di e en sou ces. We
cons uc a composi e index using P incipal Componen Analysis (PCA) and he So max
no maliza ion echnique. Da a include he ola ili y o TUNINDEX s ock p ices de i ed
om an ARMA(1,1)-GARCH(1,1) condi ional a iance modeling (downloadable om:
h ps:// .in es ing.com/indices/ unindex-his o ical-da a (accessed on 24 Janua y 2025)),
p ice–ea ning a io (ex ac ed om: h ps://www.ceicda a.com/en/ unisia/ unis-s ock
-exchange-p ice-ea nings- a io/pe- a io- se), di idend yield (ex ac ed om: h ps://www
.ceicda a.com/en/ unisia/ unis-s ock-exchange-di idend-yield (accessed on 24 Janua y
2025)), ma ke capi aliza ion (ex ac ed om: h ps://www.ceicda a.com/en/ unisia/
unis-s ock-exchange-ma ke -capi aliza ion/ma ke -capi aliza ion- se), and he a ia ion
o he exchange a e (USD/TND) (downloadable om: h ps://www.bc .go . n/bc /

Economies 2025,13, 185 53 o 68
si ep od/ ableau
_
s a is ique.jsp?pa ams=PL213010 (accessed on 24 Janua y 2025)). Due o
missing alues, ou sample boils down o 09/2007–08/2024.
So max no maliza ion, o he no malized exponen ial unc ion, mi iga es he in luence
o ou lie s while e aining hem in he da ase . I p ese es impo an da a wi hin one
s anda d de ia ion o he mean h ough a nonlinea ans o ma ion using a sigmoidal
unc ion. The So max unc ion no malizes a a iable (
xi
) using exponen ial unc ion,
mean (x), and s anda d de ia ion (σ), as ollows:
zi =1
1−e−(xi −xi)
σ
xi
a e he a iables
i
used o calcula e he composi e index and obse ed a ime
om he
se {Vola ili y o TUNINDEX, P ice Ea ning Ra io, Di idend Yield, Ma ke Capi aliza ion,
Exchange Ra e}.
The composi e index is calcula ed as ollows:
IS1 =
5
∑
i=1
wizi
whe e
wi
a e he i s p incipal componen loadings ( om PCA). The So max no maliza ion
ensu es ha all a iables a e ans o med in o a compa able scale while p ese ing ela i e
di e ences. PCA cap u es he mos impo an common a ia ion in hese i e a iables,
and he i s h ee componen s a e used as he composi e index because hey explain he
highes p opo ion o a iance.
Fu he mo e, he alidi y o PCA is checked h ough Kaise –Meye –Olkin (KMO = 0.564
> 0.5) and Ba le ’s es o sphe ici y (Chi-Squa e = 261.411, p- alue = 0.000).
The esul ing in es o sen imen index IS1 is a ime se ies e lec ing he agg ega ed
in es o sen imen in Tunisia (see Figu e 13). The e olu ion o IS1 in Tunisia e lec s he
impac o global and domes ic e en s on ma ke con idence. Sen imen plumme ed du ing
he 2007–2009 inancial c isis, and showed a g adual eco e y om 2010 o 2013, albei wi h
ola ili y linked o Tunisia’s poli ical ansi ion. F om 2014 o 2018, IS1 displayed mode a e
s abili y wi h in e mi en luc ua ions, ollowed by a sha p decline in 2019–2020 due o he
COVID-19 pandemic and domes ic economic challenges. Sen imen emained his o ically
low om 2021 o 2023, e lec ing p olonged pessimism d i en by poli ical ins abili y and
economic s agna ion. Howe e , a no able eco e y in 2024 sugges s enewed op imism,
po en ially d i en by imp o ing economic condi ions and policy in e en ions.
We ha e checked he uni oo es s h ough PP and ADF, and ind ha IS1 is in eg a ed
in he o de o 1 (I(1)) (see Table A3 in Appendix A.2). VAR lag o de selec ion using AIC
and he esul s o he Johansen coin eg a ion es a e eadable om Tables S40 and S41
in he Supplemen a y File, jus i ying he use o VECM again. The alidi y o VECM
passes h ough he Po man eau es o se ial co ela ion in esiduals (see Table S42 in he
Supplemen a y File).
The esul s in Table 14 indica e ha he coe icien o he e o co ec ion e m (Coin-
Eq1) is nega i e and s a is ically signi ican a 5%, con i ming a long- un equilib ium
ela ionship be ween he a iables. The sho - un indings indica e a pe sis ence o in es o
sen imen , cap u ed by he signi ican one-pe iod lagged D(IS1), while o he coe icien s
a e s a is ically insigni ican . In he long un, he impac o he money ma ke a e on
in es o sen imen is asymme ic acco ding o he geopoli ical isk egime. When conside -
ing he global geopoli ical isk (LNGPR) (model (1)), we ind ha a 1% inc ease in TMM
inc eases IS1 by 0.05% (low egime), while i dec eases IS1 by 1.18%. The magni ude o
he ma ginal e ec unde high global geopoli ical ensions is ul ima ely mo e impo an ,
Economies 2025,13, 185 54 o 68
emphasizing ha mone a y policy shi s in es o sen imen du ing in ense geopoli ical
episodes. Now ocusing on model (2) in Table 14, we ind ha he ma ginal impac o
TMM is abou 0.22 uni s in he low-local-geopoli ical- isk egime, compa ed o
−
3.85 uni s
in he high egime. Again, changes in in es o sen imen a e mo e p onounced du ing
pe iods o u bulen local weakness. The magni udes o he ma ginal e ec o TMM (in
he absolu e alue) a e highe when we inco po a e local geopoli ical isk (LNGPR_TUN)
in o he model. The inding s eng hens ou p e ious esul s abou delayed in es men
ollowing pessimism, and ha in es men decisions a e mo e sensi i e o local weaknesses.
-4
-3
-2
-1
0
1
2
3
4
Sep-07
May-08
Jan-09
Sep-09
May-10
Jan-11
Sep-11
May-12
Jan-13
Sep-13
May-14
Jan-15
Sep-15
May-16
Jan-17
Sep-17
May-18
Jan-19
Sep-19
May-20
Jan-21
Sep-21
May-22
Jan-23
Sep-23
May-24
IS1
Figu e 13. E olu ion o he composi e index o IS1 using PCA and he So max no maliza ion echnique.
Table 14. VECM long- and sho - un dynamics. Dependen a iable: D(IS1).
Model (1)
Assump ion No in e cep o end in CE o VAR
Coin eg a ing Eq: Coin Eq1
IS1(−1) 1.000000
TMM(−1) −6.514690
(1.08392)
[−6.01031] ***
LNGPR_TUN(−1) −0.672064
(0.26701)
[−2.51698] **
TMM_GPR_TUN(−1) 1.558309
(0.23433)
[6.65002] ***
Economies 2025,13, 185 55 o 68
Table 14. Con .
Model (1)
E o Co ec ion: D(IS1)
Coin Eq1 −0.034268
(0.01732)
[−1.97813] **
D(IS1(−1)) −0.123440
(0.07343)
[−1.68104] *
D(TMM(−1)) −0.212340
(0.58932)
[−0.36032]
D(LNGPR_TUN(−1)) −0.273958
(0.73332)
[−0.37359]
D(TMM_GPR_TUN(−1)) 0.078589
(0.12776)
[0.61513]
R-squa ed 0.037559
Adj. R-squa ed 0.016861
Sum sq. esids 31.67702
S.E. equa ion 0.412682
F-s a is ic 1.814633
Log likelihood −99.43415
Akaike AIC 1.093551
Schwa z SC 1.178689
Mean dependen −0.010673
S.D. dependen 0.416206
No es: S anda d e o s a e be ween pa en heses, -s a is ics a e be ween b acke s. *, ** and *** deno e he s a is ical
signi icance a 10%, 5%, and 1%, espec i ely. C i ical alues o -S uden : 1% (2.56), 5% (1.96), 10% (1.645).
@T end(93M01) ep esen s a linea ime end s a ing in he i s mon h o 1993. C is a cons an . (
−
1) co esponds
o he one-lagged pe iod ( a iable obse ed a
−
1). D is he i s -di e ence ope a o . IS1: In es o sen imen .
TMM: Money ma ke a e. LNGPR_TUN: Local geopoli ical isk (in log).
The indings sugges se e al policy ecommenda ions. To imp o e in es o sen imen
in Tunisia, policymake s should ocus on s abilizing inancial ma ke s by educing s ock
ma ke ola ili y, enhancing ma ke capi aliza ion, and p omo ing anspa ency and co -
po a e go e nance. Measu es o mi iga e exchange a e ola ili y, such as main aining
adequa e ese es and o e ing hedging ins umen s, a e c ucial. S eng hening ea nings
eliabili y, os e ing consis en di idend policies, and suppo ing di e si ied in es men
po olios can boos ma ke con idence. Mone a y policy should align wi h ma ke dynam-
ics, using in e es a e adjus men s and o wa d guidance o educe unce ain y. Meanwhile,
add essing geopoli ical isks and enhancing in es o p o ec ions will u he a ac local
and o eign in es men , os e ing sus ainable economic g ow h.
Economies 2025,13, 185 56 o 68
5. Concluding No es
Cen al banks ace isk and unce ain ies in o mula ing mone a y policy, pa icula ly
ega ding i s e ec i eness du ing high-unce ain y pe iods. I is i al o examine he e ec s
o in e es a es on o he a iables unde a ying economic condi ions, especially when
geopoli ical isk is high o low (Balcila e al.,2022). Unde s anding he ela ionship be-
ween in e es a es and mac oeconomic a iables is c ucial o assessing he e ec i eness
o mone a y policy. The li e a u e is di ided on whe he isk diminishes his e ec i eness;
he i e e sible heo y sugges s i does, while he c edi ansmission channel heo y a -
gues o he wise. In his pape , we p o ide meaning ul insigh s in o Tunisia’s ansmission
mechanism o he money ma ke a e by applying a ba e y o ad anced econome ic
echniques. The ime- a ying G ange causali y e eals signi ican phases o connec ion
and disconnec ion, highligh ing he imp ope unc ioning o he in e es a e, which is
used as a p ima y ool o mone a y policy by CBT. The Wa ele cohe ence analysis u he
p o es he lagged e ec s o he money ma ke a e. Bo h global and local geopoli ical isks
pe u bed he e ec i eness o mone a y policy. The esul s e eal signi ican implica ions
o he e ec i eness o he money ma ke a e as an economic policy ins umen , pa ic-
ula ly in he con ex o i e e sibili y heo y and p ecau iona y beha io . The obse ed
oscilla ing and delayed esponses o indus ial p oduc ion in LPs highligh a ola ile and
s a e-dependen ela ionship. This sugges s ha unde heigh ened unce ain y, such as
geopoli ical isk, i ms a e hesi an o adjus p oduc ion o make new in es men s. This
aligns wi h i e e sibili y heo y, which a gues ha i ms adop a cau ious app oach when
aced wi h unce ain y, limi ing he esponsi eness o indus ial p oduc ion o mone a y
policy changes. The p ecau iona y beha io is e iden he e, as i ms delay decisions in a
“wai -and-see” manne , u he educing he e ec i eness o he money ma ke a e as a ool
o in luence p oduc ion dynamics. Consequen ly, mone a y policy alone may no be su i-
cien o s imula e o cons ain indus ial ac i i y du ing unce ain pe iods, necessi a ing
complemen a y measu es such as a ge ed iscal in e en ions o isk- educ ion s a egies.
Fo consume p ices, he esponse o mone a y shocks is weak and sho -li ed, in-
dica ing a limi ed pass- h ough e ec o he money ma ke a e on in la ion. This could
be a ibu ed o p ice s ickiness, weak demand-side p essu es, o supply-side dominance
unde condi ions o geopoli ical unce ain y. The p ecau iona y beha io o i ms and
consume s may also con ibu e o his mu ed esponse, as p icing adjus men s a e likely
conse a i e du ing unce ain imes. These indings sugges ha mone a y policy elying
on he money ma ke a e may s uggle o ancho in la ion expec a ions e ec i ely, pa icu-
la ly du ing pe iods o heigh ened isk. Al e na i e ools, such as exchange a e policies o
di ec ma ke in e en ions, may be equi ed o achie e desi ed in la ion ou comes.
The indings o VECM highligh he nonlinea na u e o mone a y policy e ec i eness.
The in e ac ion wi h bo h global and local geopoli ical isk emphasizes he asymme ic
impac o mone a y shocks, wi h he money ma ke a e appea ing less e ec i e in s imu-
la ing p oduc ion o con olling in la ion du ing high-local isk pe iods. This limi a ion
ein o ces he need o policymake s o adop adap i e and lexible amewo ks ha ac-
coun o he b oade mac oeconomic en i onmen . To s eng hen hese esul s, we ha e
cons uc ed a no el composi e index o Tunisian in es o sen imen h ough PCA. We ind
ha igh ening in e es a es de e io a es in es o sen imen sha ply du ing heigh ened
pe iods o isk, wi h a mo e p onounced impac unde high le els o local geopoli ical isk.
Con ac iona y mone a y policy inc eases pessimism and ea among i ms, discou aging
hem om in es ing.
We ha e e idence, howe e , o an e ec i e mone a y policy ansmission on agg ega e
indus ial p oduc ion du ing pe iods o high global geopoli ical isk. Examining indus ial
p oduc ion a he sec o al le el yields u he insigh s in o he puzzling ela ionship wi h
Economies 2025,13, 185 63 o 68
Table A3. Uni oo es esul s o IS1.
UNIT ROOT TEST TABLE (PP)
A Le el
IS1
Wi h Cons an
-S a is ic −1.4953
P ob. 0.5342
no
Wi h Cons an and T end
-S a is ic −3.3420
P ob. 0.0625
*
Wi hou Cons an and
T end
-S a is ic −1.5060
P ob. 0.1235
no
A Fi s Di e ence
d(IS1)
Wi h Cons an
-S a is ic −16.1866
P ob. 0.0000
***
Wi h Cons an and T end
-S a is ic −16.2158
P ob. 0.0000
***
Wi hou Cons an and
T end
-S a is ic −16.2182
P ob. 0.0000
***
UNIT ROOT TEST TABLE (ADF)
A Le el
IS1
Wi h Cons an
-S a is ic −1.6809
P ob. 0.4394
no
Wi h Cons an and T end
-S a is ic −3.4282
P ob. 0.0505
*
Wi hou Cons an and
T end
-S a is ic −1.6896
P ob. 0.0862
*

Economies 2025,13, 185 64 o 68
Table A3. Con .
A Fi s Di e ence
d(IS1)
Wi h Cons an
-S a is ic −16.1804
P ob. 0.0000
***
Wi h Cons an and T end
-S a is ic −16.2158
P ob. 0.0000
***
Wi hou Cons an and
T end
-S a is ic −16.2123
P ob. 0.0000
***
No es: (*) signi ican a 10%; (**) signi ican a 5%; (***) signi ican a 1% and (no) no signi ican .
No es
1Fo u he de ails on how GPR is de i ed, we e e eade s o Calda a and Iaco iello (2022).
2T ying an op imize like he False Nea es Neighbo is no app op ia e because he se ies a e no su icien ly long.
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