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Do Supply Chain Disruptions Matter for Global Economic Conditions?★
William Ginna, Jamel Saadaouib
aLabcorp, Sr. Economist / Data Scientist, Artificial Intelligence, USA and Coburg University of Applied Sciences, Germany.
William.Ginn.OBA@said.oxford.edu
bUniversity of Paris 8, IEE, LED, 2 rue de la Liberté, Saint-Denis, France. jamelsaadaoui@ gmail.com
Abstract
The COVID-19 pandemic and its aftermath exposed the vulnerabilities of global supply chains, leading
to widespread delays and shortages that highlighted the interconnectedness of economies. This paper
examines the global impact of supply chain disruptions on economic conditions, drawing on literature related
to economic uncertainty, global economic integration, and the global supply chain disruptions. Using a
Bayesian Vector Autoregression (BVAR) model, we analyze the effect of supply chain shocks. The empirical
findings reveal that these disruptions significantly influence global economic stability, particularly through
their impact on aggregate inflation and the policy responses that accompanies them.
Keywords: Supply Chain, Business Cycles, and Global Economic Uncertainty.
JEL: E31, E32, D24
Highlights
•Supply chain disruptions transmit a world disturbance to the business cycle.
•Empirical findings indicate that supply chain pressures are an important driver of economic policy
uncertainty.
•Supply chain disruptions have significant effects on global economic stability, particularly influencing
inflation.
•Empirical results show that supply chain disruptions are a key factor driving the increase in policy
responses from most central banks during the post-pandemic period.
★Corresponding author: William Ginn.
Preprint March 17, 2025
1. Introduction
[I]t has been generally thought that monetary policy should limit its response to, or "look
through," supply shocks to the extent that they are temporary and idiosyncratic... Supply shocks
that have a persistent effect on potential output could call for restrictive policy to better align
aggregate demand with the suppressed level of aggregate supply. The sequence of shocks to
global supply chains experienced from 2020 to 2022 suppressed output for a considerable time
and may have persistently altered global supply dynamics. Such a sequence calls on policymakers
to use policy restraint to limit inflationary effects.
— U.S. Federal Reserve Chair Jerome H. Powell – November 20231
The COVID-19 pandemic and its aftermath exposed and exacerbated vulnerabilities within the global
economy. Among the most critical challenges has been the severe disruption of global supply chains. These
disruptions, initially triggered by factory closures, lock-downs and mobility restrictions, caused considerable
delays and shortages in the production and distribution of goods. As economies began to recover, the
situation became more complex, with the resurgence in demand quickly outpacing the constrained supply.
This imbalance not only highlighted the fragility of global supply networks but also underscored the profound
inter-connectedness of modern economies, where disruptions can have cascading effects globally.
In a world where economic activities are increasingly intertwined, disruptions to global supply chains
pose significant risks to economic stability. The interdependence of economies means that shocks can rapidly
propagate across borders, affecting production, trade, and financial markets worldwide. This phenomenon has
been observed during the pandemic, where disruptions had far-reaching implications, amplifying economic
volatility and uncertainty. Understanding these dynamics is crucial for policymakers and informing the
general public, as it provides insights into the vulnerabilities of the global economic system and the potential
for future shocks to disrupt global stability.
This paper seeks to explore the global dimensions of supply chain disruptions and their implications for
economic conditions. Our analysis is grounded in three distinct strands of literature, each of which provides
a framework for understanding the complex interactions between supply chain pressures and the broader
economy.
The first strand of literature addresses the concept of global economic integration and its implications
for business cycle synchronization. Numerous papers find evidence of a world business cycle (e.g., Kose
et al.,2003,Monfort et al.,2003,Ciccarelli and Mojon,2010,Ginn,2023a,Ginn,2023b), where economic
1Opening remarks ("Monetary Policy Challenges in a Global Economy") at the 24th Jacques Po-
lak Annual Research Conference, hosted by the International Monetary Fund, Washington, D.C. Source:
https://www.federalreserve.gov/newsevents/speech/powell20231109a.htm
2
activities in different countries are increasingly synchronized due to globalization. The implications of
global integration are profound, as it suggests that shocks can have immediate and significant impacts on
other regions, further amplifying the effects of global supply chain disruptions.
The second strand of literature focuses on economic uncertainty and its relationship with economic
conditions. Recent research has explored the role of oil prices in generating economic uncertainty, particularly
through the lens of Economic Policy Uncertainty (EPU), a measure developed by Baker et al. (2016). EPU
is negatively correlated with the business cycle (e.g., Bloom,2009 and Baker et al.,2016). The broader
implications of economic uncertainty are significant, as it affects firm-investment (e.g., Kang et al.,2014,
Handley and Limao,2015), stock prices (e.g., Kang and Ratti,2013a,Antonakakis et al.,2013,You et al.,
2017,Ginn,2023a), bank valuation (He and Niu,2018) and unemployment (e.g., Caggiano et al.,2014,
Caggiano et al.,2017). Ginn (2022) finds that natural disasters, which can be considered exogenous shocks
similar to supply chain disruptions, can also lead to increased aggregate uncertainty, underscoring the need
to understand how such disruptions can spill over into broader economic instability. Studies such as Kang
and Ratti (2013b), Antonakakis et al. (2014), Hailemariam et al. (2019) and Dufrénot, Ginn, Pourroy and
Sullivan (2024) examine the transmission mechanisms through which international oil prices can create
spillover effects on domestic economies, thereby increasing uncertainty.
The third strand, and the focus of our contribution, relates to global supply chain disruptions. Numerous
studies have established a link between these disruptions and diminished financial performance. For instance,
Hendricks and Singhal (2003) demonstrate that supply chain disruptions, such as production or shipping
delays, can significantly reduce shareholder value. Hendricks and Singhal (2005b) show that such disruptions
increase equity risk and lead to a decline in market value, while Hendricks and Singhal (2005a) find that
supply chain issues negatively impact a firm’s financial health by lowering revenue and operating income,
increasing costs, and raising total inventory levels. Notably, affected firms may take up to two years to fully
recover from the financial damage. Similarly, Baghersad and Zobel (2021) report that supply chain disruptions
are associated with reduced operating income, returns on sales and assets, as well as poor performance in
total asset turnover. The Global Supply Chain Pressure Index (GSCPI) provides a novel, comprehensive
measure of global supply chain disruptions (Benigno et al.,2022). Recent studies have identified supply
chain pressures as key drivers of inflation, particularly in the Euro area (Ascari et al.,2024), the US (Diaz et al.,
2023,Ginn and Saadaoui,2025a) and in relation to geopolitical risks (Asadollah et al.,2024). When supply
chains are strained—due to factors such as shipping delays, shortages of key inputs, or rising transportation
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costs—businesses face heightened uncertainty regarding production schedules, costs, and revenues. This
increases economic uncertainty, particularly when disruptions are prolonged. In addition, rising supply chain
pressures contribute to higher inflation due to supply shortages and escalating costs. The findings by Mrabet
et al. (2025) highlight that supply chains have a significant dynamic impact on consumer prices, especially
during economic crises such as the 2008 global financial crisis and the COVID-19 pandemic. As GSCPI
increases, central banks may be forced to tighten monetary policy, which introduces uncertainty regarding the
future course of inflation control measures. This uncertainty can raise EPU, especially as policymakers react
to supply-side challenges. Furthermore, global supply chain pressures often intersect with geopolitical risks,
such as trade wars, tensions along key shipping routes, or dependency on goods from politically unstable
regions. Consequently, a rise in the GSCPI may signal growing geopolitical tensions, further increasing
EPU as governments adapt trade, security, and foreign policies to mitigate these risks. Laumer and Schaffer
(2025) find that supply chain shocks amplify the typical effects of monetary policy on output and prices. This
amplification occurs due to an intensification of the credit channel: when supply chain pressures are high,
the sensitivity of credit costs (excess bond premium) to monetary policy increases.
In this paper, we contribute to the literature on the economic impacts of global supply chain disruptions
by estimating a BVAR model with sign restrictions at the global level, with a particular emphasis on their
effects on the world business cycle. Given the global nature of these disruptions, their consequences extend
beyond individual countries, having broad implications for the entire global economy. Using monthly data
from January 1999 to June 2024 at the global level, we analyze the transmission of global supply chain shocks
across interconnected economies.
Unlike previous studies that rely on country-specific models, our global modeling approach offers an
empirical framework to estimate how supply chain disruptions propagate across the global economy. In a
global context, economies are interconnected through goods, factors and financial markets. Estimating a
BVAR at the global level allows for the consideration of these interconnections that exist. This approach
not only helps explain post-pandemic inflation dynamics, but also sheds light on the increase in the interest
rate reaction, especially during the post-pandemic period, which in turn influence GEPU. Utilizing a BVAR
model, we examine the effects of GSCPI on economic conditions during key historical periods, including the
Great Moderation, the Global Financial Crisis and the COVID-19 pandemic and its aftermath. Our study links
supply chain disruptions transmit as a world disturbance to the global business cycle, contributing to lower
output and higher inflation, thereby resulting in higher GEPU especially during the post-pandemic economic
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recovery. These findings underscore the growing global risks posed by supply chain disruptions, offering
compelling evidence that policymakers must account for the international dimensions of these pressures when
designing economic policies aimed at mitigating their adverse effects.
The rest of the paper is structured as follows: in Section 2 describes the data. Section 3 discusses
the modeling methodology and empirical results. Section 4 provides policy recommendations. Section 5
concludes the paper.
2. Data
The model is based on monthly data spanning from 1999:JAN to 2024:JUN covering key macroeconomic
variable types. The variables included in our analysis include economic uncertainty, output (industrial pro-
duction), aggregate consumer price (CPI), global supply chain pressure index (GSCPI) and global economic
policy uncertainty (GEPU). The data is summarized in Table 1.
Table 1: Variable Selection
Item Symbol Source Description
Output ln𝑌𝑡OECD Global Production Index
Aggregate CPI ln 𝑃𝑡OECD Global Price Index
Interest Rate 𝑅𝑡OECD/FRED Interest rate
Supply chain shock 𝐺𝑆𝐶𝑃𝐼𝑡Federal Reserve of NY GSCPI
Economic Uncertainty ln 𝐸 𝑃𝑈𝑡FRED GEPU
Recession Dummy 𝛿𝑡FRED OECD Recession Dates (OECDNMERECM)
Growth variables are defined as 100 times the log-difference (year-on-year). Six global variables are
considered: GEPU, output, aggregate inflation, interest rate and GSCPI. Each variable is discussed in turn.
2.1. Global EPU
Building on the EPU index by Baker et al. (2016), Davis (2016) constructs a measure of GEPU. GEPU
is considered in the empirical analysis. EPU is negatively correlated with the business cycle (e.g., Bloom,
2009 and Baker et al.,2016).
5
2.2. Construction of GDP Weighted Global Aggregates
Following Dufrénot, Ginn and Pourroy (2024) and Ginn (2024b), we construct three global economic
indicators based on twenty-two sampled economies2using a GDP weighted index. The global indicators
include output (industrial production) growth3, output gap4, aggregate inflation and the interest rate5. The
weight of each economy in the index is derived from its economic size (proxied using annual GDP data based
on puchasing power parity ("PPP")), which is accordingly rebased (i.e., sums to one for each period). We
then apply the weights to each of the respective global indicators in the respective period, considering the
data is monthly. For consistency, the aggregate price (CPI) is seasonally adjusted via ARIMA X-12 algorithm
from the U.S. Census Bureau.
In the Appendix, Figure 8plots the period-to-period movements, which are quite stable where the
relationship evolves over a longer period of time. The international data for the twenty-two economies and
world data index is plotted in the Appendix (see Figure 8) and are used to represent international economic
conditions (henceforth, "WORLD").
2.3. Global Supply Chains
Global supply chain pressure index (𝐺 𝑆𝐶𝑃𝐼𝑡)is obtained from Benigno et al. (2022).
2.4. Global Variables
Figure 1plots the global data which shows the global dataset captures two major turning points. The
top-pane shows a sizable decline in output growth and the output gap, which occurred during the Global
2Based on IMF data, the twenty-two economies considered in this paper represent 75.2% of global output in purchasing power
parity (PPP) GDP, see e.g. https://www.imf.org/external/datamapper/PPPGDP@WEO. The twenty-two economies include:
Brazil ("BRA"), Canada ("CAN"), Switzerland ("CHE"), Chile ("CHL"), China ("CHN"), Columbia ("COL"), Czech Republic
("CZE"), Denmark ("DNK"), Euro zone (19 countries; "EUR"), United Kingdom ("GBR"), Hungary ("HUN"), India ("IND"), Israel
("ISR"), Japan ("JPN"), Mexico ("MEX"), Norway ("NOR"), South Korea ("KOR"), Poland ("POL"), Russia ("RUS"), Sweden
("SWE"), Turkey ("TUR") and the United States ("USA"). The Euro zone values are based on the 19 member countries (i.e., Austria,
Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands,
Portugal, Slovakia, Slovenia, and Spain).
3For India, manufacturing production index (FRED mnemonic INDPRMNTO01IXOBM) is used as opposed to total production
index (FRED mnemonic INDPROINDMISMEI), considering data availability (the correlation is 0.9918 for Jan 2000 to Dec 2018).
For China, we use total production excluding construction (FRED mnemonic CHNPRINTO01IXPYM). As the production index for
China includes missing values, the Kalman smoother using an ARIMA state space representation is used to impute missing values.
4The cyclical component of the OECD production index is estimated by HP filter. The HP filter is used to detrend the data into a
cyclical stationary component (Hodrick and Prescott,1997). The trend is based on 𝜆= 129,600, a standard value for data at monthly
frequency (see e.g. Ravn and Uhlig (2002))
5For India, the interest rate is based on the 90 day Treasury Bill interest rate (e.g., Patnaik et al.,2011,Gabriel et al.,2012,
Saxegaard et al.,2010,Anand et al.,2014,Ginn and Pourroy,2020).
6
Financial Crisis (GFC)6and onset of the spread of the COVID-19 virus. There was a noticeable decrease
in aggregate inflation and interest rate during these two time periods, albeit inflation aggregate inflation
increased almost in parallel with the rise in GSCPI, corresponding with an in interest rates.
Figure 1: Global Economic Data
Shaded areas indicate OECD recession dates.
3. Methodology and Results
We estimate the dynamic responses of the endogenous variables using a Bayesian Vector Autoregression
(BVAR) model with sign restrictions. While the BVAR approach is ideal for analyzing the interactions among
macroeconomic variables, there remains limited understanding of the direct impact of GSCPI on GEPU. To
address this, we first examine how GSCPI affects GEPU, controlling for other key factors, by employing both
Generalized Method of Moments (GMM) and Generalized Additive Models (GAM).
6According to the NBER, the recession dates for the U.S. is between 2007:DEC to 2009:JUN.
7
3.1. Determinants of Global EPU
Given the potential bidirectional causality between GEPU and other macroeconomic conditions, we turn
to a General Method of Moments (GMM) and General Additive Model (GAM) to further explore the specific
effect of GSCPI on GEPU.
We incorporate a GMM approach to address potential endogeneity concerns that arise from this bidi-
rectional relationship. Unlike Ordinary Least Squares (OLS), which assumes exogeneity of the explanatory
variables, GMM allows for the use of lagged control variables to account for endogeneity, thereby enhancing
the reliability of the estimates. By utilizing lagged instruments, the GMM framework ensures a more robust
treatment of the causal relationships between GSCPI, GEPU, and other economic variables.
Parallelly, we employ a GAM to explore the nonlinear interactions among variables without necessarily
imposing restrictive parametric assumptions, a priori. This flexibility enables us to capture more complex
dynamic relationships, especially when there are nonlinear effects between GSCPI and GEPU.
3.1.1. GMM
The GMM is formalized as follows:
ln 𝐸 𝑃𝑈𝑡=𝛼+𝛽𝑇𝑥𝑡+𝐵(𝐿)Θ𝑡+𝑢𝑡(1)
where 𝑥𝑡is a vector of up to four control variables in the set 𝑥𝑡=[Δln(𝑌𝑡),Δln(𝑃𝑡), 𝑅𝑡, 𝐺𝑆𝐶𝑃𝐼𝑡], which
includes output growth, aggregate inflation, interest rate and GSCPI data. Accordingly, we estimate two
models, the first is based on output growth, inflation and interest rate ("M1"). The second model is based on
the former and additionally includes GSCPI ("M2"). The model further includes an intercept (𝛼), a vector
of lagged control variables (Θ𝑡) and an error term (𝑢𝑡). We set the lag operator (i.e., B(L)) of the control
variables to 1. Accordingly, we estimate how economic conditions affect GEPU over the sample period.
Table 2demonstrates that GEPU is associated with lower output growth, higher inflation and lower interest
rate consistent with the literature (e.g., Hailemariam et al.,2019,Ginn,2022,Dufrénot, Ginn and Pourroy,
2024,Dufrénot, Ginn, Pourroy and Sullivan,2024). We further find that GEPU increases in response to
higher GSCPI. All coefficients in Table 2are statistically significant.
The results for M1 and M2 suggests that GEPU is associated with lower output growth, higher inflation and
lower interest rate. M2 demonstrates a positive relationship between GEPU and GSCPI. Table 3demonstrates
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Table 2: GMM Regression
M1 M2
𝛼5.4849*** 5.4224***
(0.1541) (0.1888)
𝛽𝑌-0.0427*** -0.0403***
(0.0061) (0.0039)
𝛽𝑃0.2478*** 0.1596***
(0.0086) (0.0116)
𝛽𝑅-0.4474*** -0.3240***
(0.0340) (0.0557)
𝛽𝐺𝑆𝐶 𝑃 𝐼 0.1238***
(0.0225)
AIC 256.1781 247.6558
Note: *, ** and *** denote significance
at 10%, 5% and 1%, respectively.
that coefficients are statistically significant. The Akaike Information Criterion (AIC) indicates that M2, which
includes GSCPI, provides a better fit than M1.
3.1.2. GAM
We further explore the joint effect of economic variables impact GEPU via GAM. Specifically, we
aim to account for potential non-linear interactions without imposing strong parametric assumptions on the
functional form. The GAM model is specified as:
ln 𝐸 𝑃𝑈𝑡=𝛼+𝑓𝑌, 𝑃 ,𝑅 ,𝐺𝑆 𝐶 𝑃𝐼 (Δln(𝑌𝑡),Δln(𝑃𝑡), 𝑅𝑡, 𝐺 𝑆𝐶𝑃 𝐼𝑡) + 𝜖𝑡(2)
The smooth term (i.e., 𝑓𝑌 , 𝑃, 𝑅, 𝐺𝑆𝐶 𝑃 𝐼 ) jointly models the relationship between the four variables on GEPU,
accounting for their possible interactions and non-linear effects.
We further investigate whether there are interaction effects between the explanatory variables. The GAM
framework allows us to relax the assumption of a linear response to the input variables.7The GAM can be
formalized as follows:
We estimate the interaction of a set of control variables as a tensor product (Wood et al.,2013). The
GAM model allows for a set of basis functions for each marginal function for the interaction of variable
combination 𝑓𝑌, 𝑃 ,𝑅 ,𝐺 𝑆𝐶 𝑃𝐼 (Δln(𝑌𝑡),Δln(𝑃𝑡), 𝑅𝑡, 𝐺 𝑆𝐶𝑃 𝐼𝑡).
Based on the estimated models, we provide a three-dimensional interaction surface plot of fitted values
of ln 𝐺 𝐸 𝑃𝑈𝑡(vertical axis) based on the explanatory variable combination (Breheny and Burchett,2017).
7The model is estimated from the data without imposing a linear functional form, as in an OLS model.
9
Table 3: Empirical Results (GAM Regression)
Parametric coefficients: Estimate Std Error t-value p-value
𝛼4.843 0.0376 128.9 <0.01
Non-parametric coefficients: EDF REF.DF F-value p-value
𝑓𝑌, 𝑃 ,𝑅 ,𝐺𝑆 𝐶 𝑃𝐼 104.6 112.6 8.38 <0.01
Adjusted R20.772
Standard errors and reference degrees of freedom for the estimated terms are provided
along with the t-value and F-value, respectively.
Figure 2plots how the interaction terms influence GEPU. The left-pane indicates that higher GEPU tends
to be associated with lower output growth and higher inflation, which can be characterized as a period of
"stagflation". The middle-pane indicates that GEPU is higher during periods of low output growth and
interest rate. The right-pane shows that GEPU tends to be highest when there is low output growth and high
GSCPI.
Figure 2: Interaction Plots (GAM Regression)
In both the GMM and GAM analyses, the empirical findings show that GEPU is positively associated
with GSCPI.
3.2. Bayesian Vector Autoregression Model
The model follows the theoretically grounded approach proposed by Giannone et al. (2015), which is
commonly used in macroeconometric research (e.g., Cross et al.,2020,Bańbura et al.,2015,Cimadomo
et al.,2022,Angelini et al.,2019,Ginn,2024a,Ginn and Saadaoui,2025a,inter alia). We estimate a BVAR
10
model with sign restriction based on the BVAR package by Kuschnig and Vashold (2021). The BVAR model
is generalized as follows:
𝑦𝑡=𝛽0+A1𝑦𝑡−1+... +A𝑝𝑦𝑡−𝑝+𝜖𝑡, 𝜖𝑡∼ N ( 0,Σ), 𝑡 ={1, ... , 𝑇 }(3)
where 𝑦𝑡is a column vector of (𝑞×1)endogenous variables; 𝛽0is an intercept vector of (𝑞×1)terms; p
is the lag length (based on Akaike information criterion of eleven lags); and 𝜖𝑡is an error term of (𝑞×𝑞)
with mean zero with variance-covariance matrix Σ. The BVAR is estimated using a Normal-Inverse-Wishart
prior based on 5,000 simulations.
The vector of macroeconomic variables includes the following:
𝑦𝑇
𝑡=[𝐺𝑆𝐶𝑃𝐼𝑡,ln 𝐺 𝐸 𝑃𝑈𝑡,Δln𝑌𝑡,Δln 𝑃𝑡, 𝑅𝑡](4)
where 𝑦𝑇
𝑡represents the transpose of a column vector consisting of GSCPI, GEPU, output growth,
aggregate inflation, interest rate. Identification in the model is based on economic theory and is achieved
through sign restrictions, which impose constraints on the contemporaneous relationships between variables.
Specifically, GSCPI is ordered first in Equation 4, as GSCPI represents global supply chain disruptions, which
are not directly influenced by domestic economic conditions and therefore assumed to be contemporaneously
exogenous. EPU is ordered second (e.g., Baker et al.,2016,Dufrénot, Ginn and Pourroy,2024). The
subsequent variables — output growth, aggregate inflation, and interest rate — are ordered following the
existing literature (e.g., Boivin and Giannoni,2006).
A GSCPI shock, which reflects disruptions in global supply chains, is expected to raise uncertainty, reduce
output, and increase both inflation and interest rates. The efficiency of global supply chains plays a critical
role in the business cycle, as disruptions can cause frictions that affect both supply and demand, leading to
inefficiencies and ultimately impacting production levels. These disruptions, often associated with delays
and higher costs, can suppress economic output, making it crucial to understand how these frictions affect
economic conditions. As global economies are interconnected, examining the effects of supply chain shocks
is essential to understand the global business cycle. Furthermore, supply chain disruptions can aggravate
inflation. When goods are in short supply due to bottlenecks, prices tend to rise, fueling inflationary pressures.
GSCPI serves as a vital indicator of supply chain stress, tracking factors like transportation costs, delivery
11
delays, and inventory shortages.
In the model specification, we note that the interest rate response reflects the "collective stance" by major
central banks (Ratti and Vespignani,2016). The transmission of monetary policy remains equivocal insofar
that policy responses are not necessarily coordinated. Domestic financial conditions can potentially become
vulnerable global shocks, which can complicate monetary policy decision making (Kamin,2010). While
co-movement in domestic inflation rates may be related to cyclical fluctuations in the world economy, as
Woodford (2007) shows that globalization does not impair the ability of central banks to control domestic
inflation through national monetary policy.
3.2.1. IRFs and FEVD
To enhance the analysis of dynamic responses from endogenous variables, we utilize a BVAR model with
sign restrictions, producing impulse response functions (IRFs) alongside 68% and 90% confidence bands
and forecast error variance decomposition (FEVD). In the previous section, a main finding is that a GSCPI
increases GEPU.
Identification is based on economic theory and prior empirical findings using sign restrictions. This
identification methodology has been pioneered by several authors, including Faust (1998), Canova and
De Nicolo (2002) and Uhlig (2005), inter alia. The fundamental concept underlying this approach is to
ascertain the identification of structural shocks by examining whether the patterns of impulse responses align
with established economic theory. The identified sign restrictions are summarized in Table 4.
Recent studies (e.g., Diaz et al.,2023,Asadollah et al.,2024,Ascari et al.,2024,Ginn and Saadaoui,
2025a) highlight the critical role that global supply chain pressures play in driving inflation and output
reductions. Ascari et al. (2024) and Ginn and Saadaoui (2025a) further show that GSCPI is a significant
factor in Phillips curve estimations for both the euro area and the U.S. Table 2highlights that GEPU is
associated with lower output growth, higher inflation, and lower interest rates, consistent with the literature
(e.g., Hailemariam et al.,2019,Ginn,2022,Dufrénot, Ginn and Pourroy,2024,Dufrénot, Ginn, Pourroy
and Sullivan,2024). In the prior section (Section 3.1), we find a positive relationship between GSCPI and
GEPU using two models (i.e., GMM and GAM) with statistically significant coefficients. The theoretical
expectation that GEPU rises in response to heightened GSCPI is also supported by Ginn (2024b), who shows
that supply chain disruptions destabilize global economic conditions through lower output, higher inflation,
and lower equity returns. This relationship underscores the importance of considering GSCPI as a key driver
12
of uncertainty. These empirical findings provide a strong basis for sign identification in the BVAR model.
An uncertainty shock is typically negatively related to the business cycle (e.g., Bloom,2009,Baker
et al.,2016). Aggregate demand shocks, characterized by unexpected increases in global real activity, tend
to reduce uncertainty but increase inflation. On the supply side, cost-push shocks, which increase inflation
while lowering output, prompting central banks to raise interest rates resulting in elevated GEPU. Finally, a
contractionary interest rate shock has a negative effect on output and inflation (Uhlig,2005).
Table 4: Identified Shocks
Shock
Response of Global Uncertainty Demand Supply Monetary
supply cost-push policy
Supply Chain Pressure (GSCPI) +
Global EPU + + - +
Output growth - - + - -
Aggregate inflation + + + -
Policy rate + + +
The first column of Figure 3shows the effects of shocks to GSCPI, where an increase in supply chain
pressures leads to a persistent rise in uncertainty (GEPU), a decline in output and rise in inflation, accompanied
by a rise in interest rates. These results align with expectations that disruptions in supply chains create
economic uncertainty, constrain production, and exert inflationary pressures.
The second column of Figure 3examines shocks to GEPU, showing that higher economic uncertainty
negatively impacts output and inflation while also leading to lower interest rates. This suggests that heightened
uncertainty weakens economic activity, prompting monetary policy responses to mitigate the slowdown.
The third column of Figure 3focuses on output shocks, where positive output shocks reduce supply chain
pressures (lower GSCPI), stabilize economic uncertainty, and lead to increased inflation and rising interest
rates over time.
The fourth column of Figure 3investigates inflation shocks, revealing that higher inflation initially reduces
output while leading to higher interest rates, likely as central banks respond to control inflationary pressures.
Meanwhile, inflation shocks appear to slightly increase supply chain pressures, possibly reflecting rising costs
affecting global trade.
Lastly, the fifth column of Figure 3analyzes interest rate shocks, where a rise in rates tends to lower
inflation, weaken output, and reduce uncertainty (GEPU), consistent with the expected contractionary effects
of monetary tightening.
The results of the FEVD are presented in the Appendix (see Section 6.2, Figure 9). The long-term effect
13
Figure 3: BVAR IRFs (Baseline Model)
of GSPCI (period 24) on GEPU, output, aggregate inflation, and the interest rate is 5%, 6%, 35% and 7%,
respectively.
3.2.2. Historical Decomposition
We provide the historical decomposition for output growth, aggregate inflation and the interest rate. Each
is discussed in turn. The analysis is organized chronologically, covering key periods to highlight the impact
of various shocks.
14
Figure 4: Historical Decomposition of Output
Shaded areas indicate OECD recession dates.
Figure 5: Historical Decomposition of Inflation
Shaded areas indicate OECD recession dates.
3.2.3. Pre-Global Financial Crisis (GFC)
The historical decomposition for output growth during the pre-GFC period shows relatively stable con-
ditions with limited influence from GSCPI. Shocks to global supply chain pressures had a mitigating effect
on inflation, reducing inflationary pressures during this period. Output growth remained stable with minimal
impact from global supply chain pressures. This period, commonly referred to as the "Great Moderation"
(e.g., Galí and Gambetti,2009,Bernanke,2012), can be characterized by reduced volatility in economic
activity and steady growth, indicating a period of relative economic stability.
15
Figure 6: Historical Decomposition of Rate
Shaded areas indicate OECD recession dates.
3.2.4. GFC
The GFC was a major global event that corresponds with a clear reduction in output growth. There was
also a decrease in inflation during this period, likely reflecting the contraction in aggregate demand.
3.2.5. Post-GFC
In the aftermath of the GFC, output growth was primarily driven by demand shocks in the aftermath
of the GFC period. During that time, supply chain pressures began to play a more noticeable role8, where
demand shocks were the dominant contributing factor, significantly lowered inflation during this period.
3.2.6. COVID-19 Pandemic
COVID-19 emerged as a pandemic in December 2019 and swiftly spread across the globe, leading to
severe consequences such as widespread fatalities, economic stagnation and heightened uncertainty. The
pandemic’s effects were profound and persistent, prompting lockdowns and other measures to mitigate the
spread of the virus.
The COVID-19 pandemic severely disrupted global supply chains, with factory closures, lockdowns, and
mobility restrictions causing bottlenecks, surging shipping costs, and extended delivery times. Despite these
8The increase in inflation can be attributed to natural disasters (e.g., the Tohoku earthquake and Thailand flooding in 2011) (e.g.,
Benigno et al.,2022,Ascari et al.,2024).
16
disruptions, demand shocks remained a dominant force in output growth, with GSCPI shocks also playing
a significant role during 2020. The pandemic notably impacted inflation, as global supply chain pressures
substantially contributed to inflationary pressures (e.g., Benigno et al.,2022,Ascari et al.,2024).
3.2.7. Recent Period (Post-Pandemic)
As the effects of the pandemic began to subside, output growth started to stabilize. The role of global
supply chain pressures has led to an inflationary environment to the extent that it was a dominant factor for
this sample time period, albeit the effect of global supply chain disruptions has been diminishing. These
disruptions contributed to amplifying inflation during the post-pandemic period. Additionally, surging energy
and food prices due to the Russia-Ukraine conflict further exacerbated inflation.9The influence of demand
shocks continues to be significant, but the overall inflation rate is stabilizing, though the persistence of
inflationary pressures has had a notable impact on interest rates.
The collective stance of central banks raised the interest rates during the post-pandemic period aimed
to reduce inflationary pressures emanating from global supply chain pressures. These actions also helped
anchor inflation expectations. According to U.S. Federal Reserve Chair Jerome Powell, "[o]ur restrictive
monetary policy helped restore balance between aggregate supply and demand, easing inflationary pressures
and ensuring that inflation expectations remained well anchored."10
3.3. Additional Models (Robustness)
To enrich the robustness of the economic analysis, we include five alternative models:
•Model II: the model is estimated prior to 2020 (i.e., before the start of the global pandemic). The IRFs
are provided in Figure 10 in the Appendix.
•Model III: we replace output growth with the output gap. The IRFs are provided in Figure 11 in the
Appendix.
9Zhang et al. (2023) find that the Russia-Ukraine conflict, which began in February 2022 and is ongoing at the time of this
writing, led to an increase crude oil prices and volatility. Özocaklı et al. (2024) find that international grain prices (wheat, maize and
barley) spiked during the onset of the ongoing Russia-Ukraine conflict. Ginn (2023b) shows that while energy and food inflation can
create a "second round” effect on headline inflation, agriculture inflation has the most significant impact on aggregate inflation.
10See https://www.federalreserve.gov/newsevents/speech/powell20240823a.htm.
17
•Model IV: we replace GDP based on PPP with nominal GDP, which is used in the time-varying
calculation of the relative proportions to determine global economic variables. The IRFs are provided
in Figure 12 in the Appendix.
•Model V: we estimate the Baseline Model using two lags (based on Schwartz information criterion).
The IRFs are provided in Figure 13 in the Appendix.
•Model VI: we change the ordering in the Baseline Model such that Global EPU is ordered last GEPU
is ordered last, following (Colombo,2013).11 The IRFs are provided in Figure 14 in the Appendix.
The empirical findings from the six models demonstrate that supply chain shocks are an important conduit
on economic conditions.
4. Policy recommendations
This study highlights the critical role of the Global Supply Chain Price Index (GSCPI) in influencing key
macroeconomic variables, including output, inflation, interest rates, and economic uncertainty. Our analysis
reveals that supply chain disruptions, particularly those triggered by global events such as the COVID-19
pandemic and geopolitical tensions, have both immediate and lasting effects on macroeconomic stability.
As previous studies have noted (Benigno et al.,2022,Di Giovanni et al.,2022,Asadollah et al.,2024),
these disruptions contribute significantly to inflation through the transportation cost channel, where delays
and higher transportation costs are passed on to consumers. This mechanism is central to the relationship
between inflation and the Global Economic Policy Uncertainty (GEPU) index. Based on our findings from
the BVAR model, strengthening the resilience of institutions and firms against such shocks is essential for
improving economic stability.
Data Collection and Monitoring
The first step in addressing supply chain vulnerabilities is enhancing data collection. Government
agencies should increase the volume and scope of data collected on GSCPI and ensure its dissemination to
policymakers. Regularly tracking the factors influencing GSCPI and forecasting future developments can help
11The rationale for maintaining that GSCPI is ordered first (i.e., is contemporaneously exogenous) follows the exogenous nature of
GSCPI which can influence EPU. GSCPI is often driven by external factors (e.g., geopolitical tensions, pandemics, trade restrictions)
which are largely exogenous, implying that GSCPI can be seen as an external shock thereby setting off a chain of reactions, which
can subsequently increase EPU. This theoretical foundation supports the notion that supply chain disruptions are a primary source
of shocks, while policy uncertainty is a reactionary variable.
18
educate the public about potential disruptions and reduce economic uncertainty. International coordination
on data sharing and analysis could also improve global responses to such disruptions.
Strengthening Institutions
Given the persistent nature of supply chain disruptions, policymakers must prioritize strategies that
minimize vulnerabilities to external shocks. This includes strengthening both fiscal and monetary policy
frameworks to support economic stability.
Fiscal policy should focus on diversifying supply sources across regions and countries, minimizing
reliance on single-source suppliers and reducing risks from localized disruptions. Critical infrastructure
investments—such as in transportation and logistics networks—are vital to ensure the smooth flow of goods
during periods of disruption. Fiscal measures, including subsidies, tax incentives, and other support mecha-
nisms, should target sectors most vulnerable to supply chain disruptions, such as manufacturing, agriculture,
and transportation (Mrabet et al.,2025). Additionally, incentivizing domestic production of essential goods
can reduce dependence on international supply networks.
A flexible and proactive monetary policy framework is equally crucial. Our study indicates that GSCPI has
a lasting impact on inflation, suggesting the need for central banks to incorporate global supply chain indicators
into their forecasting models. A flexible inflation-targeting approach, which accounts for disruptions in global
supply chains, is necessary to manage inflation effectively. Furthermore, international coordination among
central banks can mitigate the broader economic consequences of supply chain disruptions by aligning
monetary policies across regions. Clear and transparent communication from central banks about their
monetary strategies and inflation expectations is also vital for stabilizing market expectations.
Monetary policy plays a critical role in reducing uncertainty and anchoring inflation expectations, both of
which are essential for maintaining economic stability. Central banks can manage inflation expectations by
clearly communicating their commitment to price stability, which is key in shaping the public’s expectations
about future inflation. As highlighted by Svensson (1997), effective communication is central to anchoring
inflation expectations. When the central bank conveys a clear policy stance, especially in response to shocks
such as GSCPI, it helps reduce the uncertainty surrounding future economic conditions. Implementing
targeted liquidity support to sectors most affected by supply disruptions can prevent financial distress from
spreading (Tabachová et al.,2024), offering crucial stabilization during times of crisis.
By signaling how it intends to respond to such shocks, the central bank can reassure markets and the
public, reducing volatility and preventing inflation expectations from becoming unanchored. This allows
19
businesses and consumers to make more informed decisions, fostering a more stable economic environment.
"Monetary policy must forthrightly address any risks of a potential de-anchoring of inflation expectations, as
well-anchored expectations help facilitate bringing inflation back to our target. The sharp policy tightening
during 2022 likely contributed to keeping inflation expectations well anchored."12
Therefore, both monetary and fiscal policies share the common goal of ensuring long-term economic
stability and resilience.
International Cooperation
In an interconnected global economy, addressing supply chain disruptions requires robust international
cooperation. Policymakers should prioritize strengthening trade agreements that facilitate the smooth move-
ment of goods, reduce trade barriers, and harmonize regulations across countries. International collaboration
can help mitigate these disruptions more effectively, contributing to global economic stability. Given the
political dimension of supply chain disruptions, stemming from geopolitical tensions, diplomatic efforts to
reduce such disruptions are also crucial. Using panel data from 20 economies, Ginn and Saadaoui (2025b)
find that the interest rate reaction decreases in the short run following a geopolitical risk shock, where the
policy reaction is accommodating in the short run to limit the negative effects on consumer sentiment, how-
ever the reaction increases in the medium term, where the central bank is committed to combating inflation
pressures.
5. Conclusion
The COVID-19 pandemic exposed critical vulnerabilities within global supply chains, with production
delays, shortages, and supply chain disruptions underscoring the fragility of interconnected economies. As
economies began to recover, a surge in demand quickly outpaced supply, intensifying inflationary pressures
and revealing the vulnerabilities of global supply networks. This study investigates the global economic
consequences of these disruptions, particularly their impact on inflation dynamics and economic uncertainty,
providing valuable insights into how supply chain shocks affect economic conditions.
By utilizing a Bayesian Vector Autoregression (BVAR) model, this study analyzes the transmission of
global supply chain shocks across 22 economies. The results demonstrate that disruptions in global supply
chains, as measured by the Global Supply Chain Pressures Index (GSCPI), have a significant impact on
12Source: https://www.federalreserve.gov/newsevents/speech/powell20231109a.htm.
20
output, inflationary pressures, and economic uncertainty. These shocks are not contained within individual
economies but propagate through global networks, complicating the task for policymakers who must account
for the international dimension of these disruptions when formulating policy.
This research extends the literature, emphasizing the pivotal role of global supply chain pressures in
shaping economic outcomes. Through a novel empirical approach that incorporates a comprehensive BVAR
model for identifying structural shocks, this study enriches the existing literature on inflation drivers. Our
findings highlight that GSCPI disruptions are crucial in amplifying inflationary pressures and contributing
to economic uncertainty, especially during global crises. These insights underline the growing importance
of global supply chain resilience in managing macroeconomic stability.
The findings of this research hold significant policy implications, both in the short and long term. In the
short term, disturbances originating from GSCPI can trigger a spillover effect on GEPU, highlighting the need
for adaptive responses. Understanding how GSCPI disrupts economic conditions is therefore a crucial issue
for policymakers seeking to stabilize economies in an increasingly interconnected world. For policymakers,
the findings underscore the necessity of addressing vulnerabilities within global supply chains in future
economic planning. Central banks and fiscal authorities must take a broader international perspective when
responding to inflation, as supply chain disruptions can exacerbate local economic shocks. Policies aimed
at strengthening supply chain resilience, improving global coordination, and adopting more flexible policy
frameworks are essential to mitigating the economic impacts of future disruptions.
Overall, this paper provides novel insights into the role of supply chain pressures in shaping inflation and
broader economic stability. The interconnected nature of global economies necessitates that policy responses
to inflation consider the dynamics of global supply chains. Our findings emphasize the importance of policies
aimed at strengthening supply chain resilience, integrating global risks into inflation forecasting models, and
enhancing international collaboration. As the world continues to grapple with the long-term effects of the
pandemic and other global disruptions, these insights are vital for preparing for future economic challenges
and safeguarding long-term economic stability. Policymakers must remain vigilant to developments in global
supply chains, as disruptions can rapidly undermine economic stability.
Acknowledgements:
Research funding: none declared.
The authors state no conflict of interest.
21
Author contributions: All the authors have accepted responsibility for the entire content of this submitted
manuscript and approved submission.
22
References
Anand, R., Ding, D. and Tulin, V. (2014), Food inflation in India: the role for monetary policy, number
14-178, International Monetary Fund.
Angelini, E., Lalik, M., Lenza, M. and Paredes, J. (2019), ‘Mind the gap: A multi-country BVAR benchmark
for the Eurosystem projections’, International Journal of Forecasting 35(4), 1658–1668.
Antonakakis, N., Chatziantoniou, I. and Filis, G. (2013), ‘Dynamic co-movements of stock market returns,
implied volatility and policy uncertainty’, Economics Letters 120(1), 87–92.
Antonakakis, N., Chatziantoniou, I. and Filis, G. (2014), ‘Dynamic spillovers of oil price shocks and economic
policy uncertainty’, Energy Economics 44, 433–447.
Asadollah, O., Carmy, L. S., Hoque, M. R. and Yilmazkuday, H. (2024), ‘Geopolitical risk, supply chains,
and global inflation’, The World Economy 47(8), 3450–3486.
Ascari, G., Bonam, D. and Smadu, A. (2024), ‘Global supply chain pressures, inflation, and implications for
monetary policy’, Journal of International Money and Finance 142, 103029.
Baghersad, M. and Zobel, C. (2021), ‘Assessing the extended impacts of supply chain disruptions on firms:
An empirical study’, International Journal of Production Economics 231, 107862.
Baker, S., Bloom, N. and Davis, S. (2016), ‘Measuring economic policy uncertainty’, The Quarterly Journal
of Economics 131(4), 1593–1636.
Bańbura, M., Giannone, D. and Lenza, M. (2015), ‘Conditional forecasts and scenario analysis with vector
autoregressions for large cross-sections’, International Journal of Forecasting 31(3), 739–756.
Benigno, G., Di Giovanni, J., Groen, J. and Noble, A. (2022), ‘The GSCPI: A new barometer of global supply
chain pressures’, FRB of New York Staff Report (1017).
Bernanke, B. (2012), ‘The great moderation’, Hoover Institution, Stanford University Book Chapters .
Bloom, N. (2009), ‘The impact of uncertainty shocks’, Econometrica 77(3), 623–685.
Boivin, J. and Giannoni, M. (2006), ‘Has monetary policy become more effective?’, The Review of Economics
and Statistics 88(3), 445–462.
Breheny, P. and Burchett, W. (2017), ‘Visualization of regression models using Visreg.’, R Journal 9(2), 56.
Caggiano, G., Castelnuovo, E. and Figueres, J. (2017), ‘Economic policy uncertainty and unemployment in
the United States: A nonlinear approach’, Economics Letters 151, 31–34.
Caggiano, G., Castelnuovo, E. and Groshenny, N. (2014), ‘Uncertainty shocks and unemployment dynamics
in US recessions’, Journal of Monetary Economics 67, 78–92.
Canova, F. and De Nicolo, G. (2002), ‘Monetary disturbances matter for business fluctuations in the G-7’,
Journal of Monetary Economics 49(6), 1131–1159.
Ciccarelli, M. and Mojon, B. (2010), ‘Global inflation’, The Review of Economics and Statistics 92(3), 524–
535.
Cimadomo, J., Giannone, D., Lenza, M., Monti, F. and Sokol, A. (2022), ‘Nowcasting with large Bayesian
vector autoregressions’, Journal of Econometrics 231(2), 500–519.
23
Colombo, V. (2013), ‘Economic policy uncertainty in the US: Does it matter for the euro area?’, Economics
Letters 121(1), 39–42.
Cross, J., Hou, C. and Poon, A. (2020), ‘Macroeconomic forecasting with large Bayesian VARs: Global-local
priors and the illusion of sparsity’, International Journal of Forecasting 36(3), 899–915.
Davis, S. J. (2016), An index of global economic policy uncertainty, Technical report, National Bureau of
Economic Research.
Di Giovanni, J., Kalemli-Özcan, Silva, A. and Yildirim, M. A. (2022), Global supply chain pressures,
international trade, and inflation, Technical report, National Bureau of Economic Research.
Diaz, E., Cunado, J. and de Gracia, F. P. (2023), ‘Commodity price shocks, supply chain disruptions and US
inflation’, Finance Research Letters 58, 104495.
Dufrénot, G., Ginn, W. and Pourroy, M. (2024), ‘Climate pattern effects on global economic conditions’,
Economic Modelling 141, 106920.
Dufrénot, G., Ginn, W., Pourroy, M. and Sullivan, A. (2024), ‘Does state dependence matter in relation to oil
price shocks on global economic conditions?’, Studies in Nonlinear Dynamics & Econometrics .
Faust, J. (1998), The robustness of identified VAR conclusions about money, in ‘Carnegie-Rochester confer-
ence series on public policy’, Vol. 49, Elsevier, pp. 207–244.
Gabriel, V., Levine, P., Pearlman, J. and Yang, B. (2012), An Estimated DSGE Model of the Indian Economy,
The Oxford Handbook of the Indian Economy, (Ed). Chetan Ghate, Oxford University Press.
Galí, J. and Gambetti, L. (2009), ‘On the sources of the great moderation’, American Economic Journal:
Macroeconomics 1(1), 26–57.
Giannone, D., Lenza, M. and Primiceri, G. (2015), ‘Prior selection for vector autoregressions’, Review of
Economics and Statistics 97(2), 436–451.
Ginn, W. (2022), ‘Climate disasters and the macroeconomy: Does state-dependence matter? Evidence for
the US’, Economics of Disasters and Climate Change 6, 141–161.
Ginn, W. (2023a), ‘The impact of economic policy uncertainty on stock prices’, Economics Letters
233, 111432.
Ginn, W. (2023b), ‘World output and commodity price cycles’, International Economic Journal pp. 1–25.
Ginn, W. (2024a), ‘Agricultural fluctuations and global economic conditions’, Review of World Economics
160(3), 1037–1056.
Ginn, W. (2024b), ‘Global supply chain disruptions and financial conditions’, Economics Letters 239, 111739.
Ginn, W. and Pourroy, M. (2020), ‘The contribution of food subsidy policy to monetary policy’.
Ginn, W. and Saadaoui, J. (2025a), ‘Impact of supply chain pressures on financial leverage’, International
Review of Financial Analysis 98, 103883.
Ginn, W. and Saadaoui, J. (2025b), ‘Monetary policy reaction to geopolitical risks in unstable environments’,
Macroeconomic Dynamics .
24
Hailemariam, A., Smyth, R. and Zhang, X. (2019), ‘Oil prices and economic policy uncertainty: Evidence
from a nonparametric panel data model’, Energy Economics 83, 40–51.
Handley, K. and Limao, N. (2015), ‘Trade and investment under policy uncertainty: theory and firm evidence’,
American Economic Journal: Economic Policy 7(4), 189–222.
He, Z. and Niu, J. (2018), ‘The effect of economic policy uncertainty on bank valuations’, Applied Economics
Letters 25(5), 345–347.
Hendricks, K. and Singhal, V. (2003), ‘The effect of supply chain glitches on shareholder wealth’, Journal of
Operations Management 21(5), 501–522.
Hendricks, K. and Singhal, V. (2005a), ‘Association between supply chain glitches and operating perfor-
mance’, Management Science 51(5), 695–711.
Hendricks, K. and Singhal, V. (2005b), ‘An empirical analysis of the effect of supply chain disruptions on
long-run stock price performance and equity risk of the firm’, Production and Operations Management
14(1), 35–52.
Hodrick, R. and Prescott, E. (1997), ‘Postwar US business cycles: an empirical investigation’, Journal of
Money, Credit and Banking pp. 1–16.
Kamin, S. (2010), Financial globalization and monetary policy, Technical Report 1002, International Finance
Discussion Papers.
Kang, W., Lee, K. and Ratti, R. (2014), ‘Economic policy uncertainty and firm-level investment’, Journal of
Macroeconomics 39, 42–53.
Kang, W. and Ratti, R. (2013a), ‘Oil shocks, policy uncertainty and stock market return’, Journal of
International Financial Markets, Institutions and Money 26, 305–318.
Kang, W. and Ratti, R. (2013b), ‘Structural oil price shocks and policy uncertainty’, Economic Modelling
35, 314–319.
Kose, M., Otrok, C. and Whiteman, C. (2003), ‘International business cycles: World, region, and country-
specific factors’, American Economic Review 93(4), 1216–1239.
Kuschnig, N. and Vashold, L. (2021), ‘BVAR: Bayesian vector autoregressions with hierarchical prior
selection in R’, Journal of Statistical Software 100, 1–27.
Laumer, S. and Schaffer, M. (2025), ‘Monetary policy transmission under supply chain pressure’, European
Economic Review 172, 104949.
Monfort, A., Renne, J.-P., Rüffer, R. and Vitale, G. (2003), ‘Is economic activity in the G7 synchronized?
common shocks versus spillover effects’, Common Shocks Versus Spillover Effects (November 2003) .
Mrabet, Z., Alsamara, M., Mimouni, K. and Awwad, A. (2025), ‘Do supply chain pressures affect consumer
prices in major economies? new evidence from time-varying causality analysis’, Economic Modelling
142, 106914.
Patnaik, I., Shah, A. and Bhattacharya, R. (2011), Monetary policy transmission in an emerging market
setting, number 11-15, International Monetary Fund.
25
Ratti, R. A. and Vespignani, J. L. (2016), ‘Oil prices and global factor macroeconomic variables’, Energy
Economics 59, 198–212.
Ravn, M. and Uhlig, H. (2002), ‘On adjusting the Hodrick-Prescott filter for the frequency of observations’,
Review of Economics and Statistics 84(2), 371–376.
Saxegaard, M., Anand, R. and Peiris, S. (2010), An estimated model with macrofinancial linkages for India,
number 10-21, International Monetary Fund.
Svensson, L. (1997), ‘Inflation forecast targeting: Implementing and monitoring inflation targets’, European
Economic Review 41(6), 1111–1146.
Tabachová, Z., Diem, C., Borsos, A., Burger, C. and Thurner, S. (2024), ‘Estimating the impact of supply
chain network contagion on financial stability’, Journal of Financial Stability 75, 101336.
Uhlig, H. (2005), ‘What are the effects of monetary policy on output? results from an agnostic identification
procedure’, Journal of Monetary Economics 52(2), 381–419.
Wood, S., Scheipl, F. and Faraway, J. (2013), ‘Straightforward intermediate rank tensor product smoothing
in mixed models’, Statistics and Computing 23(3), 341–360.
Woodford, M. (2007), ‘Globalization and monetary control’, National Bureau of Economic Research .
You, W., Guo, Y., Zhu, H. and Tang, Y. (2017), ‘Oil price shocks, economic policy uncertainty and industry
stock returns in China: Asymmetric effects with quantile regression’, Energy Economics 68, 1–18.
Zhang, Q., Yang, K., Hu, Y., Jiao, J. and Wang, S. (2023), ‘Unveiling the impact of geopolitical conflict on
oil prices: A case study of the Russia-Ukraine War and its channels’, Energy Economics 126, 106956.
Özocaklı, D., Başar, B., Ekşi, H. and Ginn, W. (2024), ‘Effect of grain corridor agreement on grain prices’,
International Economic Journal .
26
6. Appendix
6.1. International Data
Figure 7: Time Varying GDP Weighted Index
Source: IMF data (in purchasing power parity terms).
27
Figure 8: International Economic Data
Shaded areas indicate OECD recession dates.
28
6.2. Variance Decomposition
Figure 9: Variance Decomposition
29
6.3. Alternative BVAR Models
Figure 10: BVAR IRFs (Model II)
30
Figure 11: BVAR IRFs (Model III)
31
Figure 12: BVAR IRFs (Model IV)
32
Figure 13: BVAR IRFs (Model V)
33
Figure 14: BVAR IRFs (Model VI)
34
