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

Trabelsi, Emna Article Monetary policy transmission under global versus local geopolitical risk: Exploring time-varying granger causality, frequency domain, and nonlinear territory in Tunisia Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Trabelsi, Emna (2025) : Monetary policy transmission under global versus local geopolitical risk: Exploring time-varying granger causality, frequency domain, and nonlinear territory in Tunisia, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 7, pp. 1-68, https://doi.org/10.3390/economies13070185 This Version is available at: https://hdl.handle.net/10419/329465 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Evans Received: 26 January 2025 Revised: 24 February 2025 Accepted: 7 March 2025 Published: 27 June 2025 Citation: Trabelsi, E. (2025). Monetary Policy Transmission Under Global Versus Local Geopolitical Risk: Exploring Time-Varying Granger Causality, Frequency Domain, and Nonlinear Territory in Tunisia. Economies,13(7), 185. https:// doi.org/10.3390/economies13070185 Copyright: © 2025 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Monetary Policy Transmission Under Global Versus Local Geopolitical Risk: Exploring Time-Varying Granger Causality, Frequency Domain, and Nonlinear Territory in Tunisia Emna Trabelsi 1,2 1Social and Economic Policy Analysis Laboratory, Higher Institute of Management of Tunis, University of Tunis, Tunis 2000, Tunisia; [email protected] 2Faculty of Economic and Management Sciences of Sousse, University of Sousse, Sousse 4023, Tunisia Abstract Using time-varying Granger causality, Neural Networks Nonlinear VAR, and Wavelet Coherence analysis, we evidence the unstable effect of the money market rate on industrial production and consumer price index in Tunisia. The effect is asymmetric and depends on geopolitical risk (low versus high). We show that global geopolitical risk has both detriments and benefits sides—it is a threat and an opportunity for monetary policy transmission mechanisms. Interacted local projections (LPs) reveal short–medium-term volatility or dampening effects, suggesting that geopolitical uncertainty might weaken the immediate impact of monetary policy on output and prices. In uncertain environments (e.g., high geopolitical risk), economic agents—households and businesses—may adopt a wait-and-see approach. They delay consumption and investment decisions, which could initially mute the impact of monetary policy. Agents may delay their responses until they gain more information about geopolitical developments. Once clarity emerges, they may adjust their behavior, aligning with the long-run effects observed in the Vector Error Correction Model (VECM). Furthermore, we identify an exacerbating investor sentiment following tightening monetary policy, during global and local geopolitical episodes. The impact is even more pronounced under conditions of high domestic weakness. Evidence is extracted through a novel composite index that we construct using Principal Component Analysis (PCA). Our results have implications for the Central Bank’s monetary policy conduct and communication practices. Keywords: geopolitical risk; monetary policy; precautionary behavior; investor sentiment; nonlinear effect; asymmetric impact 1. Introduction Since the early 2000s, the global economy has become increasingly interconnected, making it more vulnerable to the ripple effects of geopolitical and economic events. Major incidents such as the 9/11 attacks, the initial conflict in Ukraine, the ongoing US–China trade tensions, the COVID-19 pandemic, and the more recent war in Ukraine have highlighted the interconnected nature of economies worldwide. These events have not only disrupted regional stability, but have also triggered significant shifts in the global business and financial cycles. Kose et al. (2003), Monfort et al. (2003), Ciccarelli and Mojon (2010), Miranda-Agrippino and Rey (2020), and Ginn (2023a,2023b,2023c) emphasized the synchronized nature of global economic activities and financial markets, revealing how shocks Economies 2025,13, 185 https://doi.org/10.3390/economies13070185 Economies 2025,13, 185 2 of 68 in one part of the world can propagate rapidly across borders. Policymakers play a crucial role in mitigating the adverse effects of global shocks. They often resort to accommodative monetary policies—such as lowering interest rates or increasing liquidity—to stabilize their economies and counteract negative impacts on growth and employment. These measures help buffer domestic markets against external disruptions, but require careful calibration to avoid long-term consequences, such as inflationary pressures or financial instability. As globalization deepens, the need for coordinated international policy responses becomes increasingly evident in effectively addressing challenges that transcend national boundaries (Dang et al.,2025). Tunisia’s monetary policy faces increasing challenges amid climbing geopolitical tensions and domestic instability. The country’s central bank must navigate a complex environment characterized by external shocks, including regional conflicts and global economic uncertainty, which exacerbate volatility in foreign exchange markets and capital flows (Mbarek et al.,2019;Moussa & Talbi,2019;Jallouli & Yalouli,2022;Belhoula,2024; Trabelsi,2024a). In such a context, the central bank is often forced to adopt restrictive measures, such as raising interest rates, to stabilize prices and the currency. However, these measures can inadvertently slow economic growth and deepen social discontent, creating a delicate balancing act between ensuring macroeconomic stability and fostering economic resilience. Since the late 1980s, Tunisia’s monetary policy has undergone substantial changes to facilitate structural reforms and address emerging economic challenges (Ghali & Mohnen, 2004). In 1987, the Central Bank of Tunisia (CBT) implemented a strategy centered on regulating money supply growth to achieve price stability, specifically targeting a growth rate of the money supply (M2) that was 2% lower than the anticipated nominal GDP growth, under the assumption of a stable money multiplier. This framework was designed to synchronize the base money supply with the growth objectives for M2. A significant transformation occurred in 2006 with the revision of Organic Law No. 58–90, which explicitly defined the CBT’s primary objective as maintaining price stability (see Murphy, 1999;S. J. Friedman & Sharkey,2010;Wahyuningsih & Fatmawati,2024). This reform was executed in two phases: initially, by managing the monetary base to target broad money (M3) in alignment with nominal GDP growth, and subsequently, by adopting an inflationtargeting framework that employed interest rates as the principal policy tool. To improve market regulation, the CBT introduced deposit and lending facilities in 2009, enhancing the flexibility of monetary policy implementation. However, the economic upheaval following the 2011 revolution led to considerable disruptions, including increased cross-border smuggling and illicit trade with Libya and Algeria, which intensified inflationary pressures and necessitated a greater reliance on inflation-targeting mechanisms (Arieff & Humud, 2011,2014;End et al.,2020). The progression of monetary policy in Tunisia illustrates the CBT’s adaptive responses to economic and financial challenges over the past decade. The introduction of new legislation (Law No. 2016-35, enacted on 25 April 2016) reaffirmed price stability as the central objective, while also elevating the importance of financial stability relative to the 2006 framework (see Tayssir & Feryel,2018). In the aftermath of the Arab Spring, the CBT transitioned towards greater exchange rate flexibility, coinciding with the emergence of a structural liquidity deficit within the banking sector. The terrorist attacks of 2015 further complicated monetary policy, leading to a substantial increase in central bank refinancing, which compromised control over monetary aggregates and undermined initial efforts to mitigate inflation through policy rate increases (Arieff,2018;Salem & Bouaziz,2024). In light of these challenges, the CBT has adopted more stringent tightening measures since 2018 to address rising inflation. To achieve its price stability mandate, the central bank Economies 2025,13, 185 3 of 68 has established both an intermediate objective—focused on inflation forecasting—and an operational objective, which involves guiding the interbank rate as the primary instrument of monetary policy. In the absence of these interventions, the repercussions of inaction would have been dire: inflation could have escalated to double-digit levels, and real interest rates would have remained significantly negative, thereby promoting excessive credit expansion and consumption. Additionally, heightened demand for imported goods would have exacerbated the current account deficit, depleting foreign exchange reserves and intensifying inflationary pressures. Furthermore, escalating inflation would have reduced deposit returns, undermining bank resources and increasing dependence on central bank refinancing, thereby heightening liquidity risks. The evolving policy framework of the CBT highlights the essential role of monetary policy in sustaining economic stability amidst persistent domestic and external shocks (Ben Youssef,2024). This research focuses on Tunisia, an emerging economy that has been notably underexplored in scholarly literature. As a result, the findings of this study may have important implications for central bankers and macroeconomists. The selection of Tunisia as a case study is based on three primary considerations. First, Tunisia exemplifies a small, open, emerging economy that is undergoing institutional transition within the Middle East North Africa (MENA) region. Over the past decade, the country has shifted its exchange rate and monetary policy framework from a currency basket system to a managed floating regime, which theoretically enhances the effectiveness of monetary policy. Additionally, Tunisia has recently granted legal independence to its central bank. If this independence translates into practical effectiveness, it is expected to strengthen monetary policy by providing the central bank with greater autonomy in decision-making and improving the signaling capacity for market participants. Furthermore, central bank independence is anticipated to enhance the efficiency of monetary policy by limiting direct government financing, thereby increasing financial autonomy and reinforcing accountability through more transparent policy communication. Second, the CBT has consistently raised interest rates to alleviate inflationary pressures, reflecting trends seen in many other countries. However, the effectiveness of this strategy remains debatable, as inflation continues to remain high despite the tightening of monetary policy. Furthermore, the persistent depreciation of the exchange rate and the widening trade deficit cast doubt on the efficacy of interest rate hikes. The discretionary nature of Tunisia’s monetary policy, which has not yet adopted a clear targeting framework, further complicates this situation. Third, research is scarce regarding aggregate production and inflationary responses to monetary policy shocks in Tunisia, with a lack of consensus on both the magnitude and direction of these effects. Furthermore, existing studies predominantly focus on overall GDP, leaving the responses of sectoral components largely unexamined. Previous analyses have often treated monetary policy as a “black box”, in the words of Mimoun et al. (2024), failing to investigate how various transmission mechanisms influence the economy. We study the effectiveness of monetary policy transmission in Tunisia. We use advanced econometric techniques to compare the money market rate behavior under high and low geopolitical risks. Frequently, the production–money market rate–risk relationship is puzzling. First, we solve the enigma by distinguishing between global and local geopolitical risk. Second, we consider production at the aggregate and the sectoral levels. Third, we delve into a deeper analysis of monetary policy’s (in)effectiveness by establishing a novel composite index of investor sentiment in the stock markets. As such, our contribution is distinctive and comprehensive in terms of the research question, the context, and the econometric methods used, and it offers a wide range of practical impli- Economies 2025,13, 185 4 of 68 cations for the practices of central banks that can be applied to similar emerging small open economies. The remainder of the paper is structured as follows. Section 2provides an overview of theoretical underpinnings and related empirical studies. Section 3outlines the research methodology. We present the findings in Section 4, and Section 5concludes. 2. Literature Review 2.1. Theoretical Deliberations 2.1.1. Irreversibility Theory In unstable environments, a crucial question of whether monetary policy is effective arises. Earlier theories suggest that monetary policy becomes less effective when uncertainty is high. Investors’ sentiment plays a key role in channeling uncertainty into monetary policy. This was outlined by the irreversibility theory (B. S. Bernanke,1983; Bloom,2009;Bloom,2014). Uncertainty affects firms and economies in two primary ways; first, through the “real options” mechanism, where the irreversibility of investment decisions encourages firms to delay or remain flexible amid uncertainty. This hesitancy, as described by Henry (1974), extends beyond investment to consumer behavior, particularly for durable goods such as homes and cars, which are often deferred during uncertain times. This delay in consumption is less pronounced for nondurable goods but still impacts overall economic activity. Bloom (2014) expanded on this by linking uncertainty with reduced responsiveness to economic stimuli, like tax cuts or interest rate adjustments, necessitating stronger stabilization policies during volatile periods. Furthermore, high uncertainty disrupts productivity growth by stalling resource reallocation, a key driver of economic efficiency. Second, uncertainty influences financial dynamics by raising risk premiums. Investors demand higher compensation for increased risks, escalating financing costs and default probabilities. This, in turn, elevates bankruptcy expenses and contributes to a slowdown in economic activity. Arellano et al. (2010) and Christiano et al. (2014) highlighted how uncertainty amplifies the likelihood of extreme adverse outcomes, further discouraging borrowing and investment. Moreover, uncertainty fuels “ambiguity aversion”, where economic agents focus on worst-case scenarios, reducing confidence and promoting precautionary savings over consumption. 2.1.2. Credit Transmission Channel Theory According to the credit transmission channel theory, the nonlinear effects of interest rates during financial crises are less substantial than monetary policy shocks. Because of the greater external financing premiums, businesses are facing liquidity limitations (as noted by Romer & Romer,1993;Morgan,1998;B. Bernanke,1999;He & Krishnamurthy,2013; Brunnermeier & Sannikov,2014). According to the idea, changes in the external finance premium—which represents the cost differential between money raised internally (via retained earnings) and externally (through stock or debt)—amplify the immediate impact of monetary policy on interest rates. The external finance premium highlights flaws in the credit markets that lead to a discrepancy between the expenses and the profits that lenders anticipate. The “credit view” holds that monetary policy tends to affect the external finance premium in the same way as it affects open-market interest rates. The overall effect of monetary policy on borrowing rates is strengthened by this additional policy effect on the external finance premium, which in turn influences real spending and economic activity (see B. S. Bernanke & Gertler,1995). Economies 2025,13, 185 5 of 68 2.1.3. Central Bank Communication and Credibility Economic agents’ expectations depend on how they—economic agents—digest the central bank’s messages. The content, clarity, timing, and credibility of monetary policy announcements have long been surveyed in the literature (Woodford,2005;Blinder et al., 2008;Blinder,2009;De Haan & Sturm,2019). Central banks are often perceived as having superior insights into the economic outlook due to their extensive forecasting resources and ability to analyze unobservable economic factors. Research, such as that by Andersson et al. (2006), showed that financial markets respond significantly to the information central banks share, with investors adjusting their views based on these announcements. Kohn and Sack (2003) emphasized that private agents tend to trust central banks’ forecasts, particularly when the institution has built a strong reputation for accurate economic assessments. Economic agents vary in their levels of sophistication—some favor straightforward, concise information, while others seek in-depth, detailed analyses to inform their decisions (Filardo, 2004). Some recent research has looked into splitting monetary policy shocks into surprise policy shocks and central bank information shocks through high-frequency identification strategies (e.g., Jaroci´nski & Karadi,2017,2020;Laséen,2020;Ostapenko,2020;Laumer & Santos,2024). Others, like Hansen et al. (2019), employed a different strategy to show that future expectations are sensitive to communicating about uncertainty. 2.2. Empirical Literature There is a growing body of literature on the effects of monetary policy under uncertainty focusing on the US market (e.g., Tenreyro & Thwaites,2016;Barnichon & Matthes, 2018;Alpanda et al.,2021;Bruns & Piffer,2023; etc.). Those papers show that tightening monetary policy under heightened uncertainty generally slows the monetary transmission mechanism. Evidence is retrieved from nonlinear modeling such as Vector Autoregressive (VAR) and local projections (LPs). Papers that present research questions similar to ours are very novel. The influential work by Caldara and Iacoviello (2022) introduced a news-based geopolitical risk indicator, demonstrating that a geopolitical risk shock has contractionary effects on the economy, with investment in industries more exposed to geopolitical risks being particularly affected. Caldara et al. (2024) expanded this analysis to a comprehensive panel of countries, revealing that during geopolitical events, supply-side disruptions typically outweigh reduced aggregate demand, resulting in increased inflation and varied effects on economic activity. Notably, while geopolitical risk shocks stimulate economic activity in the US—largely due to heightened defense spending—they lead to recessions in other nations. Building on this, Bondarenko et al. (2024) refined the geopolitical risk index by utilizing country-specific news sources, offering a perspective that better reflects local geopolitical dynamics instead of a predominantly Western viewpoint. Pinchetti (2024) categorized geopolitical risk shocks into those arising from energy market disruptions and those linked to weak aggregate demand. Brignone et al. (2024) highlighted that those large geopolitical shocks primarily affect the economy by increasing uncertainty. Kilian et al. (2024) proposed a dynamic stochastic general equilibrium (DSGE) global economy model, examining the relationship between geopolitical oil price risks and economic fluctuations. Their findings indicate that while geopolitical tension plays a critical role in driving oil price volatility, it has a limited impact on broader macroeconomic fluctuations. In episodes of high geopolitical tensions, it is important to account for monetary–fiscal coordination (Franconi,2024). How geopolitical risk affects business cycles and economic conditions is a matter of research. Geopolitical risk shocks influence the economy through various channels, with some exerting inflationary pressures, while others have deflationary effects. Inflationary channels include the impact on commodity prices, particularly oil, as highlighted by Mignon and Saadaoui (2024), and Economies 2025,13, 185 6 of 68 the currency channel, as discussed by Gopinath (2015), Kisswani and Elian (2021), and Yilmazkuday (2025). Conversely, deflationary effects arise from channels such as consumer sentiment, where heightened uncertainty dampens spending, and financial conditions, which tighten during periods of geopolitical instability (Forbes & Warnock,2012;Forbes et al.,2017;Choi & Hadad,2025). Our contribution. We distinguish our work from the existing literature by focusing on Tunisia. Since the late 1980s, Tunisia has implemented market-oriented reforms, though the state continues to play a substantial role. It influences consumer prices, employs a significant portion of the labor force, and actively participates in wage negotiations. State-owned banks dominate the financial sector, which remains underdeveloped despite a notable level of lending relative to economic output. The sector faces challenges, including a lack of an efficient securities market, reliance on collateral-based lending, and a considerable burden of non-performing loans. The informal economy is significant, contributing substantially to employment in the private sector. Before 2011, state capture was prevalent; businesses connected to the presidential family outperformed competitors, and dominated specific sectors that were heavily regulated and restricted for foreign investment ( Przystupa & Wróbel,2015 ). After an unsuccessful mix of exchange rate and monetary aggregate targeting, the CBT now uses interest rates as the primary monetary tool. The 2011 revolution marked a major shift in the country’s economic landscape. In 2016, the CBT became independent. Tunisia presents the key characteristics of small, trade-open, emerging market economies, and serves as a valuable case for analysis. In our work, we argue that the Tunisian monetary policy’s reaction to geopolitical risk is subject to both inflationary and deflationary channels. There are instances in which the central bank cares about curbing inflation, while in others, dealing with investors’ sentiments becomes a priority (Ginn & Saadaoui,2024). 3. Research Methodology 3.1. Data Collection 3.1.1. The Geopolitical Risk Index A key aspect of economic policy planning involves addressing risks associated with geopolitical events. These include wars, political unrest, elections, natural disasters, shifts in political regimes, and escalating tensions between nations. Such geopolitical uncertainties influence economic conditions and financial markets (Kisswani & Elian,2021). Caldara and Iacoviello (2022) developed a novel indicator to quantify adverse geopolitical events by analyzing the frequency of related articles in prominent newspapers. Their measure, known as the Geopolitical Risk (GPR) index, captures the dynamics of geopolitical tensions and their economic implications from 1900 onwards. The GPR index is derived using automated text analysis from the digital archives of ten major newspapers—Chicago Tribune, The Daily Telegraph, Financial Times, The Globe and Mail, The Guardian, Los Angeles Times, The New York Times, USA Today, The Wall Street Journal, and The Washington Post. The index calculation involves identifying articles discussing adverse geopolitical events each month and expressing them as a proportion of the total number of articles. The dataset consists of approximately 25 million news articles from leading English-language newspapers, covering publications from 1900 to the present. In recent years, the sample has included around 30,000 articles per month, while the historical dataset contains about 10,000. Economies 2025,13, 185 7 of 68 The methodology employs a dictionary-based approach, selecting words that closely align with the concept of geopolitical risk. This selection process is informed by geopolitical textbooks, historical linguistic databases, and the analysis of high-frequency words appearing in news reports during periods of heightened geopolitical tension. Key terms include “war”, “threat”, “terror”, “blockade”, “invasion”, and “troops”, which are significantly more prevalent in media coverage during geopolitical crises. The analysis categorizes articles into eight thematic groups: threats of war, threats to peace, military buildups, nuclear threats, terror threats, war initiations, war escalations, and acts of terror. Two subindices are constructed from these categories: the Geopolitical Threats index, encompassing the first five categories, and the Geopolitical Acts index, covering the last three. As Caldara and Iacoviello (2022, p. 5) have put it, the dictionary used for constructing the index is developed based on a structured approach. First, it is designed to align with the definition of geopolitical risk established in this study, ensuring that selected terms accurately reflect relevant events. Second, references from geopolitical textbooks and the Corpus of Historical American English help identify key themes associated with geopolitical events, such as “war on terror” or “nuclear weapon”, as well as words commonly linked to conflict, like “declare”. Third, the selection process prioritizes high-frequency words and their synonyms that tend to appear more frequently in news reports during periods of heightened geopolitical tension. For example, terms such as “crisis”, “terror”, “blockade”, “invasion”, “troops”, and “war” are significantly more prevalent in media coverage during geopolitical crises. Then, an audit was conducted to ensure reliability, involving a human review of 16,000 newspaper articles and comparisons with external indicators, and confirming that the index effectively tracks geopolitical risk fluctuations. The primary GPR index, based on all ten newspapers, began in 1985. The countryspecific GPR indices are derived from automated text searches of newspaper archives. For 44 advanced and emerging economies, Caldara and Iacoviello (2022) constructed these indices by identifying, for the Recent Index, the monthly proportion of articles from 1985 onward that (1) qualify for inclusion in the general GPR index and (2) reference the specific country or its major cities. These indices are expressed as a percentage of total articles published each month, and provide insights into geopolitical risks as perceived from a US perspective for each country. We have gathered the global GPR and the local GPR related to Tunisia. However, we selected data from 1993 onwards, as data coverage of other variables is available starting from that year.1 Notable spikes in the index are observed during significant historical events, such as the aftermath of 9/11, the US–Iraq war, the Russia–Ukraine war, and the banking instability of Silicon Valley Bank (see Figure 1). Elevated geopolitical risk is linked to declines in investment (S. Wang et al.,2019), stock market performance (Agoraki et al.,2022), and employment levels (Ji et al.,2025). Others established a link with energy transition and environmental sustainability (Q. Wang et al.,2024;Cutcu et al.,2025). The GPR also correlates with an increased likelihood of economic crises and heightened downside risks for the global economy. Importantly, the GPR is associated with high inflation and uncertainty levels (Caldara et al.,2024). Economies 2025,13, 185 8 of 68 Figure 1. The evolution of the global geopolitical risk (GPR). (1) 01/1993: START II Treaty signed between the United States and Russia to reduce nuclear weapons. (2) 09/1993: The Oslo Accords are signed, marking a step toward peace between Israel and Palestine. (3) 04–07/1994: The Rwandan Genocide claims the lives of approximately 800,000 people. (4) 12/1994: Russia launches its first war in Chechnya. (5) 07/1995: Srebrenica massacre during the Bosnian War; over 8000 Bosniaks are killed. (6) 11/1995: End of the Bosnian War. (7) 03/1999: NATO intervenes in the Kosovo War through airstrikes against Yugoslavia. (8) 09/2001: Terrorist attacks in the US. (9) 03/2003: The US and allies invade Iraq, alleging weapons of mass destruction. (10) 08/2008: Russia–Georgia War over South Ossetia and Abkhazia. (11) 09/2008: Global Financial crisis with the collapse of Lehman Brothers. (12) 01/2011: The Arab Spring begins in Tunisia, spreading across the Arab world. (13) 03/2011: Syrian Civil War begins, becoming a prolonged and devastating conflict. (14) 02/2014: Russia annexes Crimea following unrest in Ukraine. (15) 06/2014: ISIS declares the establishment of a caliphate in Iraq and Syria. (16) 11/2015: Paris terrorist attack. (17) 06/2016: The UK votes to leave the European Union (Brexit). (18) 12/2019: COVID-19 is first detected in Wuhan, China, leading to a global pandemic, (19) 03/2020: COVID-19 lockdowns begin worldwide, leading to economic and social upheaval. (20) 02/2022: Russia invades Ukraine, triggering the largest European conflict since WWII. (21) 03/2023: Banking instability emerges following the collapse of Silicon Valley Bank. (22) 07/2023: Record-breaking global heatwaves intensify calls for climate action. (23) 10/2023: Hamas’s attack and Israel’s invasion of Gaza. Sources: Washington Watch, US Institute of Peace, Human Rights Watch, Carnegie Endowment. The local GPR was more pronounced during the Arab Spring, known as the 2011 revolution in Tunisia, and the multiple elections and parliaments following the fallout of the Ben Ali regime (see Figure 2). During these periods, Tunisia underwent a complicated political and economic transition, resulting in a prolonged economic decline over the last decade. Additionally, the country has been impacted by regional instability and conflict. Economies 2025,13, 185 15 of 68 first is the Augmented Dickey–Fuller (ADF) test, introduced by Dickey and Fuller (1979). The authors examine the following random walk. yt=c0+c1t+ρyt−1+∑k i=1φi∆yt−i+ϵt,t=1, 2, . . . , ϵt;N0, σ2(7) Dickey and Fuller (1979) utilized the maximum likelihood approach to develop an estimator for ρ . Additionally, the Phillips–Perron (PP) test introduced by Perron and Phillips (1987) proposes a minor adjustment to Equation (7), and uses a semi-parametric approach to estimate ρ; yt=c0+c1t+ρyt−1+ϵt,t=1, 2, . . . , ϵt;N0, σ2(8) The hypothesis test is given by (H0:ρ=1(ytis nonstationary) H1:ρ<1(ytis stationary) There is also the Dickey–Fuller test for a unit root, in which the series has been transformed by a generalized least-squares regression (DF-GLS) introduced by Elliott et al. (1996). There are two possible alternative hypotheses, as follows: yt is stationary around a linear trend, or yt is stationary with no linear time trend. Under the first alternative hypothesis, the DF-GLS test is performed by first estimating the intercept and trend via GLS. The GLS estimation is performed by generating the new variables, ∼ yt, where ∼ yt=δ01−13.5 T+δ1t−13.5 T(t−1)+ϵt, An OLS regression is performed on the Equation above. Then, we generate the following: y∗ t=yt−b δ0+ˆ δ1t Finally, we perform ADF on the transformed model, y∗ t=a0+a1t+ρy∗ t−1+∑k i=1ζi∆y∗ t−i+ϵt,t=1, 2, . . . , ϵt;N0, σ2 Table A1 in Appendix A.1 presents the unit root findings from ADF and PP for various assumptions about the intercept and trend. If Prob is lower than the significance level, then we reject the null hypothesis of a unit root in the series. All variables are stationary after taking the first difference. They are integrated into order 1 (I(1)), except LNIPIT, LNGPR, and LNGPR_TUN, which are I(0) and I(1) at the same time. We ensure the credibility of those results in three ways. First, we conduct the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test for the same set of variables. The results in Table A2 of Appendix A.1 reveal that all variables are integrated in the order of 1(I(1)), except LNCPI. Second, we conduct the DF-GLS for each variable in level and first difference by considering continuous samples. The results are available in Table 4, and reveal that all variables are I(1), except LNGPR and LNGPR_TUN, which are I(0) and I(1) at the same time. Taking all the findings together, we consider that all variables are I(1). Economies 2025,13, 185 16 of 68 Table 4. DF-GLS of ERS unit root test results. Null Hypothesis: The Variable Is Nonstationary With Intercept With Trend and Intercept Variables t-Statistic Critical Values t-Statistic Critical 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 Note: NS “nonstationary”, S “stationary”. LNIPIT: Industrial production (in log). LNCPI: Consumer price index (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR: Global geopolitical risk (in log). LNGPR_TUN: Local geopolitical risk (in log). Economies 2025,13, 185 17 of 68 Next, we conduct a unit root test that accounts for structural breaks. We utilize the Zivot–Andrews method for this analysis. The Zivot and Andrews (2002) test permits the identification of breakpoints that occur endogenously, and evaluates the null hypothesis of a unit root in contrast to the alternative hypothesis. The variable yt is stationary with a structural break in intercept, in trend, or in both trend and intercept. Equation (9) is identical to Equation (7) , but includes two dummies DUt= 1 if t>TBand 0 otherwise , DTt=t−TBif t>TBand 0 otherwise; yt=c0+c1t+θDUt+ιDTt+ρyt−1+∑k i=1φi∆yt−i+ϵt,t=1, 2, . . . , ϵt;N0, σ2(9) The results are available in the Supplementary File (see Table S1) and confirm again the order of integration of the variables. 4.2. Connection/Disconnection Episodes of the Interest Rate: Time-Varying Granger Causality Results We follow Ivanov and Kilian (2005) and use the Akaike Information Criterion (AIC) to select the optimal lag (see Tables S2–S5), as this gives more accurate estimations for monthly data. There are four VAR systems that we consider. Model 1 includes the industrial production (LNIPIT), the money supply (LNM2TND), the money market rate (TMM), the real effective exchange rate (LNTCER), and the global geopolitical risk index (LNGPR) as endogenous variables. Model 2 is similar to Model 1, but we consider the consumer price index (LNCPI) instead of LNIPIT. Model 3 replicates Model 1 with the local geopolitical risk index (LNGPR_TUN). Model 4 substitutes LNIPIT with LNCPI in Model 3. Table 5 presents the results of the Max–Wald statistic using three methods: forward, rolling, and recursive. We reject the null hypothesis of no causality from all variables to LNIPIT. In particular, TMM does Granger cause LNIPIT at a 1% level of significance according to rolling and recursive expanding methods, while the null hypothesis is not rejected under the forward expanding method. Table 5. Time-varying Granger causality results. Dependent variable—LNIPIT, globalg geopolitical risk—LNGPR. H0: LNIPIT Is Not Granger Caused by Max_Wald_Forward Max_Wald_Rolling Max_Wald_Recursive 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] Note: *, **, *** significant at 10%, 5%, and 1% levels, respectively. The 90th percentiles are between parentheses, 95th percentiles are between braces, and 99th percentiles are between brackets. LNIPIT: Industrial production (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR: Global geopolitical risk (in log). Economies 2025,13, 185 18 of 68 Now focusing on the plots of Figure 3, the null hypothesis of no causality is rejected whenever the test statistic portrayed by the solid line exceeds the critical values (dotted lines). According to the forward-expanding Wald test, the test statistic gradually declines and remains below the critical values (90th and 95th percentiles) for most of the period, suggesting a weaker or non-existent causal relationship between TMM and industrial production during these times. The declining trend could signify a gradual disconnection effect, potentially related to geopolitical risks disrupting the transmission mechanism. The rolling Wald test shows a fluctuating causal relationship, with the test statistic frequently exceeding the critical thresholds in certain periods (e.g., around 2009 and briefly after 2015). These periods of connection suggest that TMM might have influenced output during times of lower geopolitical risks or relative economic stability. The intermittent disconnections (test statistics falling below critical values) align with heightened geopolitical risks, potentially dampening the effectiveness of monetary policy on industrial production. The recursive method highlights persistent peaks (e.g., around 2009 and after 2015), with the test statistic exceeding the critical values during these periods. These sustained connections might indicate moments when geopolitical risks were less disruptive, allowing monetary policy to impact industrial production. However, the sharp declines in other periods (e.g., 2009–2015) suggest significant disconnections, possibly due to heightened geopolitical risk. Overall, the results indicate alternating periods of connection and disconnection between TMM and industrial production in Tunisia. Global geopolitical risks likely played a role in weakening the causal relationship by disrupting financial markets and economic stability. Periods of disconnection correspond to heightened risk, reducing the effectiveness of monetary policy transmission mechanisms. Conversely, connections emerge during more stable periods, allowing monetary policy to influence production. (a) Forward Figure 3. Cont. Economies 2025,13, 185 19 of 68 (b) Rolling (c) Recursive 0 20 40 60 2000m1 2005m1 2010m1 2015m1 2020m1 with 90th (--) and 95th (-) percentiles of bootstrapped test statistics Rolling Wald test for lnipit G-caused by tmm, 1994m1 - 2019m4 0 20 40 2000m1 2005m1 2010m1 2015m1 2020m1 Recursive expanding Wald test for lnipit G-caused by tmm, 1994m1 - 2019m4 with 90th (--) and 95th (-) percentiles of bootstrapped test statistics 60 Figure 3. The forward, rolling, and recursive evolving test results with lag augmentation d = 1 are displayed in panels (a–c) with a minimum window size = 72. The regression model 1 is (LNIPIT LNM2TND TMM LNTCER LNGPR)’, allowing for heteroscedastic errors. The empirical size is 5%, controlled over a 1-year period and obtained from bootstrapping. Lag orders are assumed to be constant and selected using AIC(=3) with a maximum length of 8 for the whole sample period. LNIPIT: Industrial production (in log). LNCPI: Consumer price index (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR: Global geopolitical risk (in log). The empirical findings indicate that geopolitical risks significantly influence the effectiveness of monetary policy in Tunisia. This relationship is mediated through several Economies 2025,13, 185 20 of 68 critical transmission channels, which ultimately affect the connection between the Tunisian money market (TMM) and industrial production. One primary mechanism involves disruptions in financial markets, where heightened geopolitical risks increase uncertainty, leading to capital flight, exchange rate volatility, and liquidity shortages within domestic financial markets (see Zhao,2024). These factors undermine the transmission of monetary policy by reducing banks’ willingness to extend credit and altering investors’ risk perceptions. Consequently, adjustments to the policy rate by CBT may not effectively translate into changes in credit conditions or decisions regarding industrial investment. Another significant channel relates to inflationary pressures and risk premiums, as geopolitical shocks often lead to supply chain disruptions, increase import costs, and trigger inflationary surges. In response to rising inflationary expectations, the CBT may adopt contractionary measures, such as raising interest rates, to stabilize prices. However, during periods marked by geopolitical turbulence, such measures may inadvertently worsen economic slowdowns rather than promote growth, thereby reducing the expected impact of monetary policy on industrial production. Furthermore, the credibility of policy and the interventions of the central bank are crucial in determining the strength of the relationship between monetary policy and industrial production. In times of heightened geopolitical risk, the central bank may resort to unconventional measures, such as liquidity injections or foreign exchange interventions, to stabilize financial conditions (Ben Youssef,2024). While these interventions can alleviate short-term instability, they may also lead to episodes of policy disconnection, during which traditional transmission mechanisms become less effective due to shifts in investor behavior and economic uncertainty. Conversely, during more stable periods, when financial markets operate efficiently, the transmission of monetary policy is reinforced, enabling the transmission mechanism to exert a more predictable influence on industrial production. The cyclical effectiveness of monetary policy in Tunisia reflects the interplay of external shocks, policy responses, and economic conditions, strengthening when geopolitical risks subside and weakening amid heightened uncertainty. When considering Model 2, we observe the same causality pattern for all variables related to LNCPI. Particularly, TMM does Granger LNCPI over the sample period for rolling and recursive expanding methods at the 1% significance level (see Table 6). Nevertheless, we identify episodes of connection and disconnection when examining the graphs of Figure 4. Except for the forward method, both rolling and recursive expanding methods show a fluctuating pattern, with high statistical significance depicted for the period (2005– 2009) and then around 2012–2013, before showing up again with a fair impact during 2017–2018. These periods coincide with the low values of global GPR (LNGPR). Table 6. Time-varying Granger causality results. Dependent variable—LNCPI; global geopolitical risk—LNGPR. H0: LNCPI Is Not Granger Caused by Max_Wald_Forward Max_Wald_Rolling Max_Wald_Recursive 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 of 68 Table 6. Cont. H0: LNCPI Is Not Granger Caused by Max_Wald_Forward Max_Wald_Rolling Max_Wald_Recursive 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] Note: *, **, *** significant at 10%, 5%, and 1% levels, respectively. The 90th percentiles are between parentheses, 95th percentiles are between braces, and 99th percentiles are between brackets. LNCPI: Consumer price index (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR: Global geopolitical risk (in log). (a) Forward (b) Rolling 0 5 10 2000m1 2005m1 2010m1 2015m1 2020m1 2025m1 Forward expanding Wald test for lncpi G-caused by tmm, 1994m1 - 2023m12 with 90th (--) and 95th (-) percentiles of bootstrapped test statistics 15 0 10 20 30 2000m1 2005m1 2010m1 2015m1 2020m1 2025m1 with 90th (--) and 95th (-) percentiles of bootstrapped test statistics Rolling Wald test for lncpi G-caused by tmm, 1994m1 - 2023m12 Figure 4. Cont. Economies 2025,13, 185 22 of 68 (c) Recursive 0 2000m1 2005m1 2010m1 2015m1 2020m1 2025m1 Recursive expanding Wald test for lncpi G-caused by tmm, 1994m1 - 2023m12 with 90th (--) and 95th (-) percentiles of bootstrapped test statistics 40 30 20 10 Figure 4. The forward, rolling, and recursive evolving test results with lag augmentation d = 1 are displayed in panels (a–c) with a minimum window size = 72. The regression model 2 is (LNCPI LNM2TND TMM LNTCER LNGPR)’, allowing for heteroscedastic errors. The empirical size is 5%, controlled over 1 year and obtained from bootstrapping. Lag orders are assumed to be constant and selected using AIC(=2), with a maximum length of 8 for the whole sample period. LNCPI: Consumer price index (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR: Global geopolitical risk (in log). We replace the global GPR with the local GPR (LNGPR_TUN). All macroeconomic variables significantly impact production (LNIPIT) throughout the period, as shown by expanding the Wald methods (Table 7). Figure 5illustrates a fluctuating relationship between TMM and LNIPIT, especially in rolling and recursive methods, with notable spikes around 2009 and 2017–2018, linked to low local geopolitical risk. High geopolitical risk correlates with ineffective monetary transmission. Table 7. Time-varying Granger causality results. Dependent variable—LNIPIT; local geopolitical risk—LNGPR_TUN. H0: LNIPIT Is Not Granger Caused by Max_Wald_Forward Max_Wald_Rolling Max_Wald_Recursive 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 of 68 Table 7. Cont. H0: LNIPIT Is Not Granger Caused by Max_Wald_Forward Max_Wald_Rolling Max_Wald_Recursive 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] Note: *, **, *** significant at 10%, 5%, and 1% levels, respectively. The 90th percentiles are between parentheses, 95th percentiles are between braces, and 99th percentiles are between brackets. LNIPIT: Industrial production (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR_TUN: Local geopolitical risk (in log). (a) Forward (b) Rolling Figure 5. Cont. Economies 2025,13, 185 24 of 68 (c)Recursive 0 2000m1 2005m1 2010m1 2015m1 2020m1 Recursive expanding Wald test for lnipit G-caused by tmm, 1994m1 - 2019m4 with 90th (--) and 95th (-) percentiles of bootstrapped test statistics 50 40 30 20 10 Figure 5. The forward, rolling, and recursive evolving test results with lag augmentation d = 1 are displayed in panels (a–c) with a minimum window size = 72. The regression model 3 is (LNIPIT LNM2TND TMM LNTCER LNGPR_TUN)’, allowing for heteroscedastic errors. The empirical size is 5%, controlled over 1 year and obtained from bootstrapping. Lag orders are assumed to be constant and selected using AIC(=4) with a maximum length of 8 for the whole sample period. LNIPIT: Industrial production (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR_TUN: Local geopolitical risk (in log). Macroeconomic variables significantly affect consumer prices at the 5% level (see Table 8). The forward expanding method does not reject the null hypothesis of no causality to LNCPI, indicating its lower reliability. In contrast, rolling and recursive expanding methods show brief connections between TMM and LNCPI, revealing the money market rate’s ineffectiveness during other periods (see Figure 6). These periods align with notable political unrest in Tunisia, including the 2011 revolution, parliamentary elections, COVID-19 lockdowns, and governance reform discussions. These observations join those found in Tables 5and 6, and align with concrete facts. After the 2011 revolution, CBT initially responded to the depreciation of the dinar by intervening in the foreign exchange market while sterilizing these interventions through liquidity injections. Although these actions aimed to stabilize the currency, they inadvertently disrupted the transmission of monetary policy by altering domestic liquidity conditions and diminishing the effectiveness of interest rate adjustments. Following the terrorist attacks in 2015, the authorities shifted their focus toward maintaining credit growth, injecting additional liquidity to support lending activities. However, this response undermined inflation-targeting efforts, resulting in a period of disconnection in monetary policy, during which interest rate adjustments had a limited impact on inflation and industrial production. It was not until 2018, with a shift toward tighter monetary policies (including increases in policy rates and macroprudential tightening), that the central bank restored its ability to influence inflation and economic activity more effectively, marking a renewed period of connection in monetary policy (see Ben Youssef,2024). Furthermore, the surge in international prices accompanied by a depreciation of the dinar explains inflationary pressures in the aftermath of COVID-19. Economies 2025,13, 185 31 of 68 Figure 8. Wavelet Transform Coherence: money market rate (TMM) versus consumer price index (LNCPI). Notes: The regions where the spectrum is significant against red noise at the 5% level are indicated by the black outline. The cone of influence restricts the autocorrelation of wavelet power at each scale, and is distinguished by the lighter shading that marks regions of high power. Time (from 01/1994 to 05/2017) and scale ranges (monthly frequency) are represented by the horizontal and vertical axes, respectively. A positive (negative) relationship between the variables is indicated by arrows pointing left (right), which also show that the variables are out of phase. The first variable x 1 (TMM) is the driver (or follower) when arrows move to the right and up (or down). Conversely, the variable x2(LNCPI) is leading (or lagging) if the arrows move left and up (down). 4.5. Connection/Disconnection and Effectiveness of Monetary Policy: Results of Interacted LPs We examine the response of macroeconomic variables to a shock in monetary policy while interacting with global (then local) geopolitical risk. We use the new command “locproj” available in Stata 18. It calculates both linear and non-linear IRFs using the local projections approach initially introduced by Jordà(2005). This method facilitates the straightforward application of various options found in the expanding body of research on local projections. The optimal lag order selection of VAR models used in interacted LPs is available in the Supplementary File (see Tables S6–S9). Initially, the response hovers around zero, showing no clear trend after the shock, with broad confidence intervals indicating high uncertainty (see Figure 9). From months 2 to 18, responses fluctuate significantly between positive and negative values, suggesting instability in industrial production due to puzzling interactions between monetary policy and global geopolitical risk. By month 20, the response sharply rises, but wide confidence intervals may signal increased risk or substantial impacts. Overall, the erratic response implies that the money market rate may influence industrial production inconsistently, depending on geopolitical risk levels. Preliminary evidence suggests that interest rates may not effectively transmit monetary policy. To draw a conclusive view, we need to compare and quantify the impact of the money market rate on production during lowand high-geopolitical-risk periods. Economies 2025,13, 185 32 of 68 Figure 9. Response of industrial production (IPITLOGDIFF) to a shock in money market rate (TMMDIFF) and allowing for interaction with the global geopolitical risk (GPRLOGDIFF) based on interacting local projections. Sample: 01/1993–08/2024. Control variables include money supply (M2TNDLOGDIFF) and real effective exchange rate (TCERLOGDIFF). 95% confidence bands are shown. Figure 10 shows that the response starts with a small, positive deviation that quickly turns slightly negative, indicating minor, short-lived inflationary effects resulting from an initial shock in the money market rate. Between months 10 and 11, the response fluctuates around zero with small amplitudes and wide confidence intervals, suggesting limited effects on consumer prices. After month 18, the response stabilizes near zero, indicating that the effects of monetary shocks on consumer prices are transitory and dissipate over time. The brief responses of consumer prices indicate that the interaction between money market rates and geopolitical risk has a minimal, temporary effect on inflation dynamics. Figure 11 shows that the response starts with a small but positive deviation that quickly turns slightly negative, indicating small effects of the money market rate. The response of industrial production still fluctuates between positive and negative values in the remaining periods (months). The effect converges towards zero in longer horizons. The money market rate seems to exert short and brief effects on production, and does not act effectively as a monetary transmitter mechanism, especially in times of high local geopolitical risk. We can infer from Figure 12 that the impact of the money market rate on consumer prices is frequently positive in many periods (months). A shock in the money market rate decreases consumer prices around months 6–8 and in month 17, before restoring its positive effect in longer horizons, underlining the ineffectiveness of the interest rate to counter inflationary prices. Overall, the interacted LPs display volatile IRFs, and can be explained by the dynamic and heterogeneous responses of macroeconomic variables to global and local geopolitical risk shocks over time. Economies 2025,13, 185 33 of 68 Figure 10. Response of the consumer price index (CPILOGDIFF) to a shock in money market rate (TMMDIFF) and allowing for the interaction with the global geopolitical risk (GPRLOGDIFF) based on interacted local projections. Sample: 01/1993–08/2024. Control variables include money supply (M2TNDLOGDIFF) and real effective exchange rate (TCERLOGDIFF). The 95% confidence bands are shown. Figure 11. Response of industrial production (IPITLOGDIFF) to a shock in money market rate (TMMDIFF) and allowing for interaction with the local geopolitical risk (GPR_TUNLOGDIFF) based on interacted local projections. Sample: 01/1993–08/2024. Control variables include money supply (M2TNDLOGDIFF) and real effective exchange rate (TCERLOGDIFF). The 95% confidence bands are shown. Economies 2025,13, 185 34 of 68 Figure 12. Response of the consumer price index (CPILOGDIFF) to a shock in money market rate (TMMDIFF) and allowing for interaction with the local geopolitical risk (GPR_TUNLOGDIFF) based on interacting local projections. Sample: 01/1993–08/2024. Control variables include money supply (M2TNDLOGDIFF) and real effective exchange rate (TCERLOGDIFF). The 95% confidence bands are shown. In the short term, the responses of industrial production and inflation are relatively muted in Figures 9and 10. This reflects the economy’s initial rigidity or the lagged effects of global shocks, as it takes time for economic agents to adjust their behaviors or for policy measures to take effect. The stability may also indicate a limited immediate transmission of global and spillover shocks, as domestic factors (e.g., contracts, price stickiness, or existing inventories) buffer the economy against rapid changes. Over the medium and long term, the responses become more pronounced and variable. This can be attributed to the economy’s gradual adjustment to the shock as expectations, prices, and policies adapt. Volatility could arise as businesses adjust their production levels to shifts in global demand or supply chain disruptions caused by the shock. Changes in global commodity prices, exchange rate pass-through, or heightened uncertainty can lead to volatile inflationary pressures. Central banks might alter interest rates dynamically in response to economic conditions, creating volatility in monetary policy variables. These fluctuations reflect economic uncertainty and the varying effectiveness of policy responses over time. The IRFs of LPs in Figures 11 and 12 suggest that the central bank reacts with a tightening monetary policy that has a temporary desirable impact on production in the short term (around horizon 1) and on inflation over the long term only (around horizon 19). These findings suggest the limited efficiency or ineffectiveness of monetary policy as a transmitting mechanism. 4.6. Effectiveness of Monetary Policy Under Test: Results of the Long-Run Dynamics of VECM The previous developments emphasize the nonlinear effects of monetary shocks on industrial production and consumer prices. The sensitivity of these results can be assessed to quantify the significance and the magnitude of monetary policy during episodes of Economies 2025,13, 185 35 of 68 low and high geopolitical risk. This is possible via the Vector Error Correction Model (VECM), which accommodates I(1) variables that are cointegrated. VAR lag order selection for VECM is performed, and the related results are available in the Supplementary File (see Tables S10–S19). The Johansen cointegration test results, including trace statistic and maximum eigenvalue, are readable from Tables S20–S29, confirming the use of VECM. The general form of VECM (p−1) is given by ∆yt=Γ(yt−1−α−Xt−1β)+∑p−1 i=1Θi∆yt−i+∑p−1 i=1Y′i∆Xt−i+µt(12) We augment Equation (12) to let the money market rate (TMM) interact with global geopolitical risk (LNGPR) and local geopolitical risk (LNGPR-TUN), respectively. ∆yt=Γ(yt−1−α−Xt−1β−τTMMt−1×LNGPRt−1) | {z } ECTt−1 + p−1 ∑ i=1 Θi∆yt−i+ p−1 ∑ i=1 Y′i∆Xt−i+ p−1 ∑ i=1 πi∆TMM ×LNGPR)t−i+θ+µt (13) where y is the dependent variable and X is a vector of control variables, encompassing LNM2TND and LNTCER, in addition to the variable of interest TMM and the geopolitical risk measures (LNGPR, LNGPR_TUN); Γ is the coefficient associated to ECTt−1 , α , β are the intercept and the long-run coefficients, Θi is the short-run coefficients of the lagged ∆yt , Y′i is a vector of the short-run coefficients of the lagged ∆Xt , p is the optimal lag in the VAR model according to AIC, and µtis a white noise process. The normalized long-run equation is written as follows: yt−1=α+Xt−1β+τTMMt−1×LNGPRt−1+ECTt−1 Thus, the coefficients in the cointegrating equation of the VECM results should be sign-reversed (Johansen,1995). 4.6.1. VECM with an Interaction Term of the Money Market Rate and Global Geopolitical Risk Specifications differ according to assumptions about the intercept and data trend. Choices based on the log-likelihood, Akaike, and Schwarz are unreliable because the estimations differ in the numbers of parameters. Our focal point is the sign and the statistical significance of CointEq1 (ECT). Banerjee et al. (1998) said it should be negative and statistically significant. Thus, only models that satisfy this condition are selected. The speed of adjustment of the industrial production (LNIPIT) to its equilibrium is about 11.9% (see Table 10). Taking the first model, we can write the cointegrating equation as follows: CointEq1 | {z } ECTt−1 =LNIPIT_{t−1} | {z } yt−1 −5.917379 | {z } α−0.139080 | {z } β1 ×LNM2TND_{t−1} | {z } x1t−1 −(−0.687007) | {z } β2 ×TMM_{t−1} | {z } x2t−1 −0.166795 | {z } β3 ×LNTCER_{t−1} | {z } x3t−1 −(−0.775811) | {z } β4 ×LNGPR_{t−1} | {z } x4t−1 −0.147131 | {z } τ×TMM_GPR_{t−1} | {z } TMMt−1×LNGPRt−1 We can arrange it as: LNIPIT_{t−1} = 0.139080 ×LNM2TND_{t−1} −0.687007 ×TMM_{t−1} + 0.166795 ×LNTCER_{t−1} − 0.775811 ×LNGPR_{t−1} + 0.147131 ×TMM_GPR_{t−1} + 5.917379 + CointEq1 The full VECM equation is given by: Economies 2025,13, 185 36 of 68 ∆LNIPIT_t | {z } ∆yt −0.119696 ×CointEq1_{t−1} | {z } ΓECTt−1 −0.540895 ×∆LNIPIT_{t−1} | {z } θ1∆yt−1 −0.275815 ×∆LNIPIT_{t−2} | {z } θ2∆yt−2 +0.052799 ×∆LNIPIT_{t−3} | {z } θ3∆yt−3 −0.736189 ×∆LNM2TND_{t−1} | {z } γ11∆x1t−1 −0.843190 ×∆LNM2TND_{t−2} | {z } γ12∆x1t−2 +0.378443 ×∆LNM2TND_{t−3} | {z } γ13∆x1t−3 +0.075635 ×∆TMM_{t−1} | {z } γ21∆x2t−1 +0.018123 ×∆TMM_{t−2} | {z } γ22∆x2t−2 +0.060180 ×∆TMM_{t−3} | {z } γ23∆x2t−3 +0.072316 ×∆LNTCER_{t−1} | {z } γ31∆x3t−1 +0.038912 ×∆LNTCER_{t−2} | {z } γ32∆x3t−2 +0.193727 ×∆LNTCER_{t−3} | {z } γ33∆x3t−3 +0.135994 ×∆LNGPR_{t−1} | {z } γ41∆x4t−1 +0.036086 ×∆LNGPR_{t−2} | {z } γ42∆x4t−2 +0.052291 ×∆LNGPR_{t−3} | {z } γ43∆x4t−3 −0.020789 ×∆TMM_GPR_{t−1} | {z } π1∆(TMM×LNGPR)t−1 −0.006963 ×∆TMM_GPR_{t−2} | {z } π2∆(TMM×LNGPR)t−2 −0.009701 ×∆TMM_GPR_{t−3} | {z } π3∆(TMM×LNGPR)t−3 +0.012483 | {z } θ The remaining columns of Table 10 can be inferred in a similar way. Both the money market rate (D(TMM)) and the real effective exchange rate (D(LNTCER)) have insignificant effects on short-term production. The money supply D(LNM2TND) has an unstable coefficient on D(LNIPT), confirming ineffective monetary targeting through the money supply (End et al.,2020). Turning to the long-run coefficients, we observe further interesting results. Importantly, the coefficient of the interaction term (TMM_GPR) is positive and statistically significant. Under a low global GPR regime (10th percentile), the marginal effect of monetary policy is about a 0.07% decrease in industrial production, while it pertains to a 0.04% increase under a high global GPR regime (90th percentile). The magnitude is small under both regimes, and this suggests that there are some benefits over global geopolitical tensions. This result may be explained by the green energy transition plan of Tunisia in the following years. Tightening monetary policy encourages some firms to invest in renewable energy as an alternative to fossil fuels. However, we should note that this result applies to industrial production at the aggregate level. By establishing a sectoral analysis, we consider manufacturing, mines, and energy sectors. We re-estimate the VECM for the LNIPIT of each sector. We find that production improves in the case of tightening interest rates under a high global GPR regime in the manufacturing sector, while the effect is insignificant for the mines sector, and dampened in the case of the energy sector, consistent with the irreversibility theory. Investors’ confidence diminishes and then delays investment until getting additional information. For the manufacturing sector, tight monetary policy can curb exchange rate volatility by attracting foreign investment or preventing capital flight, reducing the cost of imports for manufacturers, and supporting production. Long-term benefits are rendered with strategies leveraged toward sustainable economic growth (Udeaja et al.,2024). Nevertheless, the impact of TMM is asymmetric, and this suggests that central bank information might be fragmented between economic agents (Trabelsi,2024b). While some prefer basic information, others prefer detailed information. Some understand the message, while others get confused (Filardo,2004). Economies 2025,13, 185 37 of 68 Table 10. VECM long-run and short-run dynamics. Dependent variable: D(LNIPIT), model 1. Model (1) (2) (3) (4.1) (4.2) (4.3) (4.4) Sector Aggregate Manufacturing Mines Energy Energy Energy Energy Assumption Intercept (no trend) in CE and VAR Intercept (no trend) in CE and VAR Intercept (no trend) in CE and VAR No Intercept (no trend) in CE, no intercept in VAR in CE or VAR Intercept (no trend) in CE and VAR Intercept and trend in CE, (no trend) in VAR Intercept and trend in CE, linear trend in VAR Cointegrating Eq: CointEq1 CointEq1 CointEq1 CointEq1 CointEq1 CointEq1 CointEq1 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 Error Correction: D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT) D(LNIPIT) CointEq1 −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 of 68 Table 10. Cont. 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-squared 0.442153 0.455397 0.178527 0.093404 0.170169 0.145902 0.145995 Adj. R-squared 0.404299 0.416202 0.139120 0.073908 0.149274 0.124396 0.121331 Sum sq. resids 0.586395 1.139237 25.20230 1.624630 1.487067 1.530554 1.530387 S.E. equation 0.045763 0.065691 0.304955 0.076309 0.073138 0.074200 0.074329 F-statistic 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 Schwarz SC −3.019414 −2.282923 0.689989 −2.194401 −2.263099 −2.234276 −2.214609 Mean dependent 0.000976 0.001075 −0.002806 −0.001039 −0.001039 −0.001039 −0.001039 S.D. dependent 0.059293 0.085975 0.328673 0.079295 0.079295 0.079295 0.079295 Notes: Standard errors are between parentheses, t-statistics are between brackets. *, ** and *** denote the statistical significance at 10%, 5%, and 1%, respectively. Critical values of t-Student: 1% (2.56), 5% (1.96), 10% (1.645). @Trend(93M01) represents a linear time trend starting in the first month of 1993. C is a constant. ( − 1) corresponds to the one-lagged period (variable observed at t − 1). ( − 2) corresponds to the two-lagged periods (variable observed at t − 2)). ( − 3) corresponds to the three-lagged periods (variable observed at t − 3). D is the first-difference operator. LNIPIT: Industrial production (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR: Global geopolitical risk (in log). Results related to consumer prices are available in Table 11. Two specifications among the five assumptions about the intercept and data trend meet the condition related to CointEq1. The coefficient is negative and statistically significant at least at the 10% level. Table 11. VECM long-run and short-run dynamics. Dependent variable: D(LNCPI), model 2. Model (1) (2) Assumption Intercept and trend in CE, (no trend) in VAR Intercept and trend in CE, linear trend in VAR Cointegrating Eq: CointEq1 CointEq1 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 of 68 Table 11. Cont. 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 Error Correction: D(LNCPI) D(LNCPI) CointEq1 −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 of 68 Table 11. Cont. 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-squared 0.156242 0.216031 Adj. R-squared 0.136015 0.194479 Sum sq. resids 0.002577 0.002395 S.E. equation 0.002971 0.002869 F-statistic 7.724405 10.02353 Log likelihood 1324.041 1335.066 Akaike AIC −8.773609 −8.840439 Schwarz SC −8.674841 −8.729325 Mean dependent 0.003646 0.003646 S.D. dependent 0.003196 0.003196 Notes: Standard errors are between parentheses, t-statistics are between brackets. *, ** and *** denote the statistical significance at 10%, 5%, and 1%, respectively. Critical values of t-Student: 1% (2.56), 5% (1.96), 10% (1.645). @Trend(93M01) represents a linear time trend starting in the first month of 1993. C is a constant. ( − 1) corresponds to the one-lagged period (variable observed at t − 1). D is the first-difference operator. LNCPI: Consumer price index (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR: Global geopolitical risk (in log). Money supply D(LNM2TND) and the real effective exchange rate have no significant impact on D(LNCPI) in the short run. There is a temporary inflationary improvement under high global geopolitical risk when tightening monetary policy, as testified by the negative coefficient of the interaction term and a positive coefficient of D(TMM). Tightening monetary policy (e.g., raising interest rates or reducing money supply) reduces aggregate demand by increasing borrowing costs and discouraging consumption. In the short run, this lower demand helps reduce inflationary pressures. In the long run, higher money supply (LNM2TND) decreases consumer prices (LNCPI). The result contradicts the quantity theory of money (M. Friedman,1961,1968,1970) and suggests the ineffectiveness of monetary targeting (End et al.,2020). The statistical significance of LNTCER is not stable across specifications, highlighting an ambiguous role of the real effective exchange rate (Agénor & Montiel,2007). For the interaction term (TMM_GPR), the coefficient is negative and statistically significant, in addition to a negative coefficient associated with TMM. Under low global geopolitical risk, increasing the money market rate leads to a decrease by 0.59% in consumer prices against an increase of 0.53% under high global geopolitical risk. High global geopolitical risk tends to increase the volatility of international commodity prices (e.g., energy and food) and disrupt global supply chains. Over time, these fluctuations can negate the initial benefits of monetary tightening, particularly if Tunisia’s monetary policy cannot address supply-side shocks. If fiscal imbalances persist, the inflationary pressures of tightening monetary policy could outweigh its intended stabilizing effects. Meanwhile, higher interest rates might push firms to adopt more efficient production methods or invest in technologies that improve productivity, leading to long-term industrial growth. The paradoxical monetary transmission mechanism signals differences in how economic agents Economies 2025,13, 185 47 of 68 Table 12. Cont. 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 of 68 Table 12. Cont. Model (1) (2.1) (2.2) (3.1) (3.2) (3.3) (4.1) (4.2) R-squared 0.475361 0.443073 0.443126 0.303078 0.304132 0.305105 0.190371 0.191801 Adj. R-squared 0.429407 0.373736 0.371271 0.194275 0.195494 0.193215 0.132102 0.130341 Sum sq. resids 0.551347 1.163108 1.162997 21.35113 21.31884 21.28902 1.434947 1.432412 S.E. equation 0.044858 0.068346 0.068480 0.300149 0.299921 0.300346 0.073725 0.073800 F-statistic 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 Schwarz SC −2.981331 −2.007292 −1.987322 1.058346 1.056833 1.075857 −2.052154 −2.034032 Mean dependent 0.001031 0.000907 0.000907 −0.003196 −0.003196 −0.003196 −0.000791 −0.000791 S.D. dependent 0.059385 0.086364 0.086364 0.334382 0.334382 0.334382 0.079137 0.079137 Notes: Standard errors are between parentheses, t-statistics are between brackets. *, ** and *** denote the statistical significance at 10%, 5%, and 1%, respectively. Critical values of t-Student: 1% (2.56), 5% (1.96), 10% (1.645). @Trend(93M01) represents a linear time trend starting in the first month of 1993. C is a constant. ( − 1) corresponds to the one-lagged period (variable observed at t − 1). ( − 2) corresponds to the two-lagged periods (variable observed at t − 2)). ( − 3) corresponds to the three-lagged periods (variable observed at t − 3). ( − 4) corresponds to the four-lagged periods (variable observed at t − 4). ( − 5) corresponds to the five-lagged periods (variable observed at t − 5). ( − 6) corresponds to the six-lagged periods (variable observed at t − 6). D is the first-difference operator. LNIPIT: Industrial production (in log). LNM2TND: Monetary aggregates in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log). LNGPR_TUN: Local geopolitical risk (in log). Economies 2025,13, 185 49 of 68 Table 13. VECM long-run and short-run dynamics. Dependent variable: D(LNCPI). Model (1) Assumption Intercept and trend in CE, (no trend) in VAR Cointegrating Eq: CointEq1 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 Error Correction: D(LNCPI) CointEq1 −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 of 68 Table 13. Cont. 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 of 68 Table 13. Cont. 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-squared 0.242049 Adj. R-squared 0.172893 Sum sq. resids 0.002315 S.E. equation 0.002907 F-statistic 3.500033 Log likelihood 1340.128 Akaike AIC −8.760855 Schwarz SC −8.439861 Mean dependent 0.003646 S.D. dependent 0.003196 Notes: Standard errors are between parentheses, t-statistics are between brackets. *, ** and *** denote the statistical significance at 10%, 5%, and 1%, respectively. Critical values of t-Student: 1% (2.56), 5% (1.96), 10% (1.645). @Trend(93M01) represents a linear time trend starting in the first month of 1993. C is a constant. ( − 1) corresponds to the one-lagged period (variable observed at t − 1). ( − 2) corresponds to the two-lagged periods (variable observed at t − 2)). ( − 3) corresponds to the three-lagged periods (variable observed at t − 3). ( − 4) corresponds to the four-lagged periods (variable observed at t − 4). D is the first-difference operator. LNCPI: Consumer price index (in log). LNM2TND: Monetary Aggregates 2 in TND (in log). TMM: Money market rate. LNTCER: Real effective exchange rate (in log), LNGPR_TUN: Local geopolitical risk (in log). In conclusion, we have examined the serial correlation of the residuals and found no reason to reject the null hypothesis of no autocorrelation at a 5% level of significance, indicating that the VECM is applicable in all models, except Model 1.4.1 in Table 10 (colored in yellow) (see Tables S30–S39 in the Supplementary File). Economies 2025,13, 185 52 of 68 4.7. Discussion The VECM findings suggest a mixture of theories that interacted to contribute to the transmission mechanism of the money market rate. We provide key takeaways below. 4.7.1. Irreversibility Theory The irreversibility theory suggests that firms delay investment decisions when faced with uncertainty because of the high cost of reversing such investments. However, in the case of tightening interest rates in Tunisia, the following occurs: • Improvement in aggregate production—Higher interest rates may create a more stable macroeconomic environment by reducing speculative activity and inflationary pressures in the long run. This stability could encourage firms to invest in productive activities despite higher borrowing costs, especially if uncertainty is reduced; • Increased consumer prices—If firms pass on higher borrowing costs to consumers via higher prices, it reflects rigidity in pricing decisions. This aligns with the irreversibility theory, where firms, once committed to production and pricing strategies, find it difficult to reverse course quickly in response to changing monetary policy. 4.7.2. Credit Channel Theory The credit channel theory highlights how monetary policy affects the economy through its impact on credit availability and cost. It has two key mechanisms (Mateut,2005): • Bank lending channel—Tightening interest rates reduce bank reserves and loanable funds, increasing the cost of borrowing. This might initially constrain investment and production, but over time, firms that survive the credit squeeze become more efficient, leading to long-term improvements in aggregate production; • Balance sheet channel: Higher interest rates increase the cost of servicing debt, worsening the financial health of borrowers. This could lead firms to pass on these costs to consumers, contributing to higher consumer prices. Additionally, households might reduce consumption due to higher debt burdens, yet this impact seems overshadowed by inflationary dynamics. 4.8. Robustness Check: Investor Sentiment Sensitivity to Monetary Policy Changes Monetary policy aims to achieve two primary goals: maintaining price stability and fostering sustainable economic growth (Marschner & Ceretta,2021). These goals can be realized through the influence of monetary policy on financial markets, including stock markets (Kurov,2010). Furthermore, uncertainty and risk shocks can adversely affect businesses by deterring investment and production, and impact households by decreasing their consumption tendencies. The findings suggest that tracking investor sentiment regarding the market can provide insights into their financial decisions, making it a valuable indicator for predicting the trajectory of the Tunisian economy. For this purpose, we have collected monthly data on investor sentiment (IS1) using different sources. We construct a composite index using Principal Component Analysis (PCA) and the Softmax normalization technique. Data include the volatility of TUNINDEX stock prices derived from an ARMA(1,1)-GARCH(1,1) conditional variance modeling (downloadable from: https://fr.investing.com/indices/tunindex-historical-data (accessed on 24 January 2025)), price–earning ratio (extracted from: https://www.ceicdata.com/en/tunisia/tunis-stock -exchange-price-earnings-ratio/pe-ratio-tse), dividend yield (extracted from: https://www .ceicdata.com/en/tunisia/tunis-stock-exchange-dividend-yield (accessed on 24 January 2025)), market capitalization (extracted from: https://www.ceicdata.com/en/tunisia/ tunis-stock-exchange-market-capitalization/market-capitalization-tse), and the variation of the exchange rate (USD/TND) (downloadable from: https://www.bct.gov.tn/bct/ Economies 2025,13, 185 53 of 68 siteprod/tableau _ statistique.jsp?params=PL213010 (accessed on 24 January 2025)). Due to missing values, our sample boils down to 09/2007–08/2024. Softmax normalization, or the normalized exponential function, mitigates the influence of outliers while retaining them in the dataset. It preserves important data within one standard deviation of the mean through a nonlinear transformation using a sigmoidal function. The Softmax function normalizes a variable ( xit ) using exponential function, mean (x), and standard deviation (σ), as follows: zit =1 1−e−(xit−xi) σ xit are the variables i used to calculate the composite index and observed at time t from the set {Volatility of TUNINDEX, Price Earning Ratio, Dividend Yield, Market Capitalization, Exchange Rate}. The composite index is calculated as follows: IS1t= 5 ∑ i=1 wizit where wi are the first principal component loadings (from PCA). The Softmax normalization ensures that all variables are transformed into a comparable scale while preserving relative differences. PCA captures the most important common variation in these five variables, and the first three components are used as the composite index because they explain the highest proportion of variance. Furthermore, the validity of PCA is checked through Kaiser–Meyer–Olkin (KMO = 0.564 > 0.5) and Bartlett’s test of sphericity (Chi-Square = 261.411, p-value = 0.000). The resulting investor sentiment index IS1 is a time series reflecting the aggregated investor sentiment in Tunisia (see Figure 13). The evolution of IS1 in Tunisia reflects the impact of global and domestic events on market confidence. Sentiment plummeted during the 2007–2009 financial crisis, and showed a gradual recovery from 2010 to 2013, albeit with volatility linked to Tunisia’s political transition. From 2014 to 2018, IS1 displayed moderate stability with intermittent fluctuations, followed by a sharp decline in 2019–2020 due to the COVID-19 pandemic and domestic economic challenges. Sentiment remained historically low from 2021 to 2023, reflecting prolonged pessimism driven by political instability and economic stagnation. However, a notable recovery in 2024 suggests renewed optimism, potentially driven by improving economic conditions and policy interventions. We have checked the unit root tests through PP and ADF, and find that IS1 is integrated in the order of 1 (I(1)) (see Table A3 in Appendix A.2). VAR lag order selection using AIC and the results of the Johansen cointegration test are readable from Tables S40 and S41 in the Supplementary File, justifying the use of VECM again. The validity of VECM passes through the Portmanteau test of serial correlation in residuals (see Table S42 in the Supplementary File). The results in Table 14 indicate that the coefficient of the error correction term (CointEq1) is negative and statistically significant at 5%, confirming a long-run equilibrium relationship between the variables. The short-run findings indicate a persistence of investor sentiment, captured by the significant one-period lagged D(IS1), while other coefficients are statistically insignificant. In the long run, the impact of the money market rate on investor sentiment is asymmetric according to the geopolitical risk regime. When considering the global geopolitical risk (LNGPR) (model (1)), we find that a 1% increase in TMM increases IS1 by 0.05% (low regime), while it decreases IS1 by 1.18%. The magnitude of the marginal effect under high global geopolitical tensions is ultimately more important, Economies 2025,13, 185 54 of 68 emphasizing that monetary policy shifts investor sentiment during intense geopolitical episodes. Now focusing on model (2) in Table 14, we find that the marginal impact of TMM is about 0.22 units in the low-local-geopolitical-risk regime, compared to − 3.85 units in the high regime. Again, changes in investor sentiment are more pronounced during periods of turbulent local weakness. The magnitudes of the marginal effect of TMM (in the absolute value) are higher when we incorporate local geopolitical risk (LNGPR_TUN) into the model. The finding strengthens our previous results about delayed investment following pessimism, and that investment decisions are more sensitive to 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 Figure 13. Evolution of the composite index of IS1 using PCA and the Softmax normalization technique. Table 14. VECM longand short-run dynamics. Dependent variable: D(IS1). Model (1) Assumption No intercept or trend in CE or VAR Cointegrating Eq: CointEq1 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 of 68 Table 14. Cont. Model (1) Error Correction: D(IS1) CointEq1 −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-squared 0.037559 Adj. R-squared 0.016861 Sum sq. resids 31.67702 S.E. equation 0.412682 F-statistic 1.814633 Log likelihood −99.43415 Akaike AIC 1.093551 Schwarz SC 1.178689 Mean dependent −0.010673 S.D. dependent 0.416206 Notes: Standard errors are between parentheses, t-statistics are between brackets. *, ** and *** denote the statistical significance at 10%, 5%, and 1%, respectively. Critical values of t-Student: 1% (2.56), 5% (1.96), 10% (1.645). @Trend(93M01) represents a linear time trend starting in the first month of 1993. C is a constant. ( − 1) corresponds to the one-lagged period (variable observed at t − 1). D is the first-difference operator. IS1: Investor sentiment. TMM: Money market rate. LNGPR_TUN: Local geopolitical risk (in log). The findings suggest several policy recommendations. To improve investor sentiment in Tunisia, policymakers should focus on stabilizing financial markets by reducing stock market volatility, enhancing market capitalization, and promoting transparency and corporate governance. Measures to mitigate exchange rate volatility, such as maintaining adequate reserves and offering hedging instruments, are crucial. Strengthening earnings reliability, fostering consistent dividend policies, and supporting diversified investment portfolios can boost market confidence. Monetary policy should align with market dynamics, using interest rate adjustments and forward guidance to reduce uncertainty. Meanwhile, addressing geopolitical risks and enhancing investor protections will further attract local and foreign investment, fostering sustainable economic growth. Economies 2025,13, 185 56 of 68 5. Concluding Notes Central banks face risk and uncertainties in formulating monetary policy, particularly regarding its effectiveness during high-uncertainty periods. It is vital to examine the effects of interest rates on other variables under varying economic conditions, especially when geopolitical risk is high or low (Balcilar et al.,2022). Understanding the relationship between interest rates and macroeconomic variables is crucial for assessing the effectiveness of monetary policy. The literature is divided on whether risk diminishes this effectiveness; the irreversible theory suggests it does, while the credit transmission channel theory argues otherwise. In this paper, we provide meaningful insights into Tunisia’s transmission mechanism of the money market rate by applying a battery of advanced econometric techniques. The time-varying Granger causality reveals significant phases of connection and disconnection, highlighting the improper functioning of the interest rate, which is used as a primary tool of monetary policy by CBT. The Wavelet coherence analysis further proves the lagged effects of the money market rate. Both global and local geopolitical risks perturbed the effectiveness of monetary policy. The results reveal significant implications for the effectiveness of the money market rate as an economic policy instrument, particularly in the context of irreversibility theory and precautionary behavior. The observed oscillating and delayed responses for industrial production in LPs highlight a volatile and state-dependent relationship. This suggests that under heightened uncertainty, such as geopolitical risk, firms are hesitant to adjust production or make new investments. This aligns with irreversibility theory, which argues that firms adopt a cautious approach when faced with uncertainty, limiting the responsiveness of industrial production to monetary policy changes. The precautionary behavior is evident here, as firms delay decisions in a “wait-and-see” manner, further reducing the effectiveness of the money market rate as a tool to influence production dynamics. Consequently, monetary policy alone may not be sufficient to stimulate or constrain industrial activity during uncertain periods, necessitating complementary measures such as targeted fiscal interventions or risk-reduction strategies. For consumer prices, the response to monetary shocks is weak and short-lived, indicating a limited pass-through effect of the money market rate on inflation. This could be attributed to price stickiness, weak demand-side pressures, or supply-side dominance under conditions of geopolitical uncertainty. The precautionary behavior of firms and consumers may also contribute to this muted response, as pricing adjustments are likely conservative during uncertain times. These findings suggest that monetary policy relying on the money market rate may struggle to anchor inflation expectations effectively, particularly during periods of heightened risk. Alternative tools, such as exchange rate policies or direct market interventions, may be required to achieve desired inflation outcomes. The findings of VECM highlight the nonlinear nature of monetary policy effectiveness. The interaction with both global and local geopolitical risk emphasizes the asymmetric impact of monetary shocks, with the money market rate appearing less effective in stimulating production or controlling inflation during high-local risk periods. This limitation reinforces the need for policymakers to adopt adaptive and flexible frameworks that account for the broader macroeconomic environment. To strengthen these results, we have constructed a novel composite index of Tunisian investor sentiment through PCA. We find that tightening interest rates deteriorates investor sentiment sharply during heightened periods of risk, with a more pronounced impact under high levels of local geopolitical risk. Contractionary monetary policy increases pessimism and fear among firms, discouraging them from investing. We have evidence, however, of an effective monetary policy transmission on aggregate industrial production during periods of high global geopolitical risk. Examining industrial production at the sectoral level yields further insights into the puzzling relationship with Economies 2025,13, 185 63 of 68 Table A3. Unit root test results of IS1. UNIT ROOT TEST TABLE (PP) At Level IS1 With Constant t-Statistic −1.4953 Prob. 0.5342 no With Constant and Trend t-Statistic −3.3420 Prob. 0.0625 * Without Constant and Trend t-Statistic −1.5060 Prob. 0.1235 no At First Difference d(IS1) With Constant t-Statistic −16.1866 Prob. 0.0000 *** With Constant and Trend t-Statistic −16.2158 Prob. 0.0000 *** Without Constant and Trend t-Statistic −16.2182 Prob. 0.0000 *** UNIT ROOT TEST TABLE (ADF) At Level IS1 With Constant t-Statistic −1.6809 Prob. 0.4394 no With Constant and Trend t-Statistic −3.4282 Prob. 0.0505 * Without Constant and Trend t-Statistic −1.6896 Prob. 0.0862 * Economies 2025,13, 185 64 of 68 Table A3. Cont. At First Difference d(IS1) With Constant t-Statistic −16.1804 Prob. 0.0000 *** With Constant and Trend t-Statistic −16.2158 Prob. 0.0000 *** Without Constant and Trend t-Statistic −16.2123 Prob. 0.0000 *** Notes: (*) significant at 10%; (**) significant at 5%; (***) significant at 1% and (no) not significant. 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