The Impact of Exchange Rate Volatility on Macroeconomic Performance: The Case of Liberia

CHAPTER 1

INTRODUCTION

1.1         Background of the Study

Every responsible government strives to maintain equilibrium among macroeconomic factors reflecting the economy’s performance. However, the primary macroeconomic objective of any nation is to attain sustainable growth and a relatively stable price level (Conteh, 2010). The dissolution of the Bretton Woods system in 1973 is widely recognized as having led to the instability of exchange rates in many countries over time (Bello et al., 2021). This exchange rate instability has been identified as a significant hindrance to macroeconomic performance (Mahjoub, 2014). As a result, more interest has been focused on understanding the exchange rates due to their crucial role as a fundamental price linking global and domestic markets for goods and assets. Moreover, exchange rates indicate the competitiveness of a country’s currency on the global market. Instances of uncontrollable exchange rate fluctuations have precipitated currency crises in financial markets, impacting output, trade patterns, and investment and disrupting macroeconomic performance (Bello et al., 2021). The exchange rate is one of the factors significantly influencing international trade (Alam, 2022). It has also been recognized as a vital element affecting export and import dynamics (Nteegeh and Mansi, 2018).

Many empirical studies have acknowledged the impact of the exchange rate on international trade, but they have also studied it for purposes other than trade (Ali, 2014). A change in the real exchange rate impacts key macroeconomic factors (Bello et al., 2021). Some studies specifically evaluated the exchange rate impact on foreign direct investment (Ramdam, 2021; Dagume, 2022; Hniya et al., 2021), while others looked at how it affects inflation (Washima, 2022), economic growth (Idris, 2019; Mbuyi et al., 2022).

The dynamic nature of the impact of the exchange rate on the macroeconomy has sparked debates among policymakers, academics, and economic agents. As a relative price, the exchange rate significantly impacts domestically produced goods’ external competitiveness. This indicates its critical role in influencing various macroeconomic variables. A persistent challenge for policymakers lies in determining the optimal exchange rate system for integrating the domestic and global economies. However, studies acknowledge that the impact of the real exchange rate on macroeconomic variables remains inconclusive, as indicated in Section 2.4 of this paper.

Liberia, a resource-intensive nation (ADB, 2023) with approximately 5.5 million people, is no exception to the exchange rate dynamics. Since its independence, the Liberian dollar has undergone a series of transformations, reflecting the country’s economic path. Liberia’s economy has historically depended on exporting natural resources and imports a significant portion of its consumer goods. The country’s manufacturing and agriculture sectors remain underdeveloped, compelling households and businesses to rely heavily on imports. This high-level dependence on international trade exposes the economy to real exchange rate shocks. Against this background, this project evaluates the impact of the exchange rate volatility on selected macroeconomic indicators in Liberia.

1.1.1        Evolution of the Exchange Rate in Liberia

Before the arrival of formerly enslaved people in 1821, the indigenous inhabitants of Liberia engaged in trade using various items as money, including shells, beads, and locally scarce items. Liberia’s currency can be traced back to the late 19th century. During this period, the Kissi money, also known as the Kissi penny, served as a means of transaction. For decades, it was used alongside the British and French currencies in a fixed exchange rate system of 1:1 across Liberia, Guinea, and Sierra Leone. In 1940, the British government abolished the use of Kissi money for transactions, following the lead of the French government. The main reason behind this decision was to address tax-related concerns. The Open Door Policy under President William V.S. Tubman brought about a notable shift. As part of this policy, the Kissi money was officially replaced by the US dollar as the legal tender in Liberia. According to Kraaij (1983), the British pound was prohibited that year, and the US dollar became the only legal currency in circulation.

Until the 1960s, the US dollar was Liberia’s only legally accepted currency. Later, Liberia established a dual-currency system by introducing its coins alongside the US dollar. The exchange rate was fixed at L$1:US$1—a fixed exchange rate regime. During this time, Liberia experienced remarkable economic growth, with its GDP reaching levels comparable to Japan’s in the 1950s and 1960s.

In the late 1970s, economic prosperity in Liberia stopped when President William R. Tolbert’s elected government was overthrown by a military coup known as the People Redemption Council (PRC). After the 1980 coup d’état, poor economic management resulted in a gradual deterioration of the economy, which in turn caused capital flight. In response to these challenges, the military government introduced a new currency, the Doe’s coin. Despite this change, the fixed exchange rate system remained intact. In 1987, the coin was replaced by L$5 notes known as the JJ Robert banknotes.

The fixed exchange rate regime remained in place until the civil war 1989. Amidst the chaos, banks were targeted, businesses were ransacked, and people sought safety in neighboring nations. It became evident that the domestic JJ Robert Bank notes experienced a sudden decrease in value compared to the US dollar. This happened because individuals were eager to exchange more of the local currency for the US dollar to exit the country, as the local currency held little worth beyond Liberia’s borders. The conflict severely impacted economic activity, leading to a significant decline in GDP. Between 1989 and 1995, Liberia experienced one of the most severe contractions in its history, with a staggering 90% decline. In 1991, during the civil war, the government introduced the Liberty banknote to replace the JJ Robert banknotes. However, the Liberty banknotes only circulated in areas controlled by the peacekeeping forces (ECOMOG), and the JJ Robert banknotes continued to circulate in rebel-controlled areas, creating two separate currency zones (Sumo, 2017).

In 1998, the Liberian government (GoL) officially changed its exchange rate system, moving from a fixed exchange rate regime to a flexible one. As a result, the value of the Liberian dollar was pegged to the US dollar at L$43/US$1 (Menkulasi et al., 2009). Although it became official in 1998, the exchange was not fixed from 1993 to 1997 due to the civil unrest, as many were willing to give more Liberian dollars for US dollars to flee the country. In 1999, the Central Bank of Liberia (CBL) was established to replace the National Bank of Liberia, leading to the introduction of new banknotes in 2001. These banknotes are circulating alongside the Liberian economy’s US dollar. Given the implementation of the flexible exchange rate system and the subsequent depreciation of Liberia dollars, it is evident that the value of the Liberian dollar has consistently decreased in relation to the US dollar (CBL, 2022).

Figure 1.1 displays the official exchange rate pegged against the US dollar from 1980 to 2023 in Liberia.

Figure 1.1: Official Exchange Rate

Source: World Development Indicators

The exchange rate in Liberia remained relatively stable between 1993 and 1997. However, from 1998 to 2000, the Liberian dollar appreciated relative to the previous years. This short-term appreciation resulted from the shift in the exchange rate regime. With the intensity of the civil war between 2001 and 2003, the exchange rate experienced a depreciation. This resulted from the increased demand for the US dollar, as most of the residents exchanged their Liberia dollars to seek refuge in neighboring countries. With the end of the civil war in late 2003, the CBL maintained a flexible exchange rate, resulting in a gradual depreciation of the Liberian dollar until 2017. The infusion of a substantial amount of Liberian dollars into the economy following the 2017 election profoundly affected the depreciation of the exchange rate, a trend that persisted until 2020. In 2020, the exchange rate experienced a significant depreciation due to the residual effects of the pandemic. As observed between 2021 and 2022, the exchange rate has experienced a considerable appreciation due to the CBL’s open market operation policy.

1.1.2        Macroeconomy of Liberia

Liberia’s post-war recovery has experienced many challenges. Despite having an average growth rate of 7.3 percent between 2006 and 2013, making it one of Africa’s fastest-growing economies, real GDP per capita remains stagnant at a third of its pre-war level. In 2021, it stood at $680, ranking 35th out of 43 Sub-Saharan countries and far below the continent’s average of $1,600. Similarly, the poverty rate remained stubbornly high at 51 percent, exceeding the regional average of 44 percent, indicating why raising living standards through sustainable and inclusive growth topped Liberia’s economic agenda (IMF, 2022).

The outbreak of the Ebola pandemic in the region in 2014 abruptly stopped the recovery progress, resulting in zero economic growth. In response, the Economic Stabilization and Recovery Plan (ESRP) was launched in 2015 to revive growth. While the ESRP helped mitigate the immediate impact of the epidemic, it only achieved 0.4 percent average growth between 2014 and 2017. In 2018, which marks Liberia’s first peaceful political transition since the 1980s, citizens from home and abroad harbored high hopes for economic revitalization (MFPD, 2021).

The presidential election in 2017 triggered a brief loss of macroeconomic control. Budgetary pressures and a shift towards central bank financing fueled inflation to 30 percent and a 40 percent depreciation of the Liberian dollar in 2019. With the assistance of the IMF in restoring stability and rectifying structural reform failings, as well as the increase in iron ores prices on the international market, which Liberia exports massively, the economy gained momentum in early 2020; however, the residual effect of the unanticipated pandemic plunged Liberia back into economic hardship. While economic activity bounced back in 2021 as the pandemic’s impact waned, it barely compensated for the significant losses in the preceding three years (IMF, 2022).

During these periods, the dynamic of Liberia’s macroeconomic environment was mainly reflected in key indicators like gross domestic product, foreign direct investment, inflation, the balance of payments, and the real exchange rate. For instance, after reaching 5 percent in 2021, GDP growth fell to an estimated 4 percent in 2022. Mining and construction drove growth on the supply side, while infrastructure spending boosted demand. However, the Ukraine war dampened economic activity with higher commodity prices and fiscal constraints. Despite remaining relatively high, inflation moderated from 7.9 percent in 2021 to 7.6 percent in 2022 due to the fall in domestic food prices (CBL, 2022). Increased spending on infrastructure and wages pushed the fiscal deficit to 4.8 percent of GDP in 2022 from 2.4 percent the previous year. Public debt also climbed to 54.6 percent of GDP due to the government’s additional borrowing. The current account deficit narrowed slightly to 17.7 percent in 2022, down from 17.8 percent in 2021, due to the improvement in the trade deficit driven by higher gold exports. Despite the robustness of FDI, funding the current account deficit was challenged by the decrease in loans and grants. International reserves dipped slightly, while the Liberian dollar appreciated against the US dollar due to strong remittance inflows and export earnings (ADB, 2022).

Table 1.1 displays the selected macroeconomic variables this research intends to examine in the Liberian economy. These variables are averaged across five-year intervals, excluding the periods affected by the pandemic. This approach allows us to account for the residual impacts of the pandemic.

Table 1.1: Macroeconomic Indicators, 2000-2022

Source: WDI and Author’s Calculation

Table 1.1 shows an increase in FDI inflow, with the highest average observed in the five years from 2010 to 2014. However, there was a significant drop from 2016 to 2019. Due to the risks associated with the presidential election, which created instability at the time, investors took a cautious approach, which caused this decline. In the pandemic and post-pandemic periods, on average, FDI inflow rose compared to the prior five-year period.

The current account has maintained a persistent deficit since 2000, and 2010–2014 had the highest deficit. On average, however, the government made substantial progress in reducing the deficit, especially throughout the pandemic and post-pandemic periods. The country’s continuous current account deficit is caused by its significant reliance on imports for essential commodities and the absence of added value to its exportable commodities, which predominantly consist of primary natural resources (CBL, 2022).

The inflation rate has seen significant fluctuations between the years 2000 and 2022. On average, the inflation rate peaked 16% from 2016 to 2019. During this period, the headline inflation rate rose from 12.4 percent in 2017 to 23.4 percent in 2018 and 27 percent in 2019. A worsening trade balance put downward pressure on the Liberian dollar. The average inflation rate decreased to 10.97 percent during the pandemic and post-pandemic periods; however, the decline occurred between 2021 and 2022. The headline inflation rate declined from 7.9 percent in 2021 to 7.6 percent in 2022. The decrease was primarily caused by the CBL’s open market operation using Treasury bills. Since 2000, the GDP has had a substantial and consistent growth rate, with the highest recorded between 2020 and 2022, on average.

1.2         Statement of the Problem

Many Sub-Saharan African nations, including Liberia, have recently grappled with intense exchange rate pressures mainly driven by external forces (IMF, 2023). Since adopting a flexible exchange rate system, the Liberian dollar has depreciated by approximately 268%, reaching an annual average of LRD$152/USD1 by the end of 2022 (WDI, 2024). Furthermore, the nation has encountered unfavorable macroeconomic conditions, marked by persistent current account deficits, volatility in prices and outputs, and sluggish economic growth, which have lowered foreign direct investment inflows and worsened external competitiveness (IMF, 2023).

While it is acknowledged that Liberia’s economy has demonstrated moderate growth except during the civil unrest period, the country has been relatively underperforming since adopting the flexible exchange rate system. Despite efforts by the Government of Liberia (GoL), tangible improvements in the macroeconomy remain vague (CBL, 2022), attributed partly to under consideration of the significant impact of exchange rate dynamics on various policy tools (CBL, 2019). This indicates that the absence of comprehensive understanding regarding the effective utilization of monetary and fiscal policies in light of exchange rate fluctuations impedes economic stability and growth.

A fundamental obstacle is the lack of empirical evidence supporting the efficacy of GoL interventions in mitigating the adverse effects of exchange rate volatility on the macroeconomy. Establishing an optimal macroeconomic stabilization strategy to bolster Liberia’s macroeconomic performance would be straightforward if policymakers thoroughly understood how macroeconomic factors interact with exchange rate dynamics. Recognizing these dynamics is essential for formulating and executing targeted policies fostering macroeconomic growth and stability. It is, therefore, understandable that it is imperative to investigate the impact of exchange rate volatility on macroeconomic variables to prioritize policies that enhance economic performance. This study endeavors to address this gap.

1.3         Research Questions

To study the volatile nature of the exchange rate volatility on macroeconomic performance in Liberia to provide optimal policy measures, this study will answer the questions below:

1)      How does the exchange rate volatility impact GDP, FDI, inflation, and current account in Liberia?

2)      Is there a long-run relationship between GDP, FDI, inflation, and current account with exchange rate volatility?

1.4         Objective of the Study

The main objective of this study is to analyze the impact of exchange rate volatility on macroeconomic performance in Liberia. The specific objectives are:

• To determine the impact of exchange rate volatility on GDP, FDI, current account, and inflation.

2)      To test the existence of GDP, FDI, inflation, and current account.

3)      To suggest areas for policy implication to enhance macroeconomic performance.

1.5         Significance of the Study

The Liberian dollar has depreciated sustainably since adopting the flexible exchange rate system (CBL, 2003-2022). This depreciation poses a challenge to achieving sustainable macroeconomic growth and stability. This indicates that policymakers need to consider the dynamic fluctuations of the exchange rate when crafting policies to improve the country’s economic performance. Understanding the precise relationship between exchange rates and macroeconomic factors is crucial. Such information can help policymakers avoid missteps and formulate more effective economic strategies. Therefore, this study aims to establish a clear empirical link between exchange rate volatility and the macroeconomy, providing valuable insights to guide policy decisions.

CHAPTER 2

Literature Review

2.1         Introduction

This chapter delves into the theories of this research topic and is followed by a comprehensive review of prior empirical studies. The studies reviewed below explicitly focused on the impact of exchange on inflation, GDP, current account, and foreign direct investment inflow. It summarizes the methodologies and research findings identified in these studies.

2.2         Theoretical Review

Studies have used several approaches to understand the complexity of the exchange rate. This section focused on purchasing power parity and the Mundell-Fleming framework. These theories are particularly relevant as they link the exchange rate to the macroeconomy.

2.2.1        Purchasing Power Parity

The purchasing power parity (PPP) links the exchange rate to the price level in domestic and foreign countries. The PPP hypothesis is rooted in the law of one price (LOOP). The LOOP principle is that homogenous goods should have equal prices in domestic and foreign markets when converted using the market exchange rate (Obstfeld & Rogoff, 1995). If the LOOP is maintained, exchange rates equalize the prices of homogenous goods in different countries, reflecting the relative strength of a country’s currency against another. According to absolute purchasing power parity (APPP), the nominal exchange rate is the price level ratio. A country with a high aggregate price level will likely face substantial inflation compared to its trading partners. Similar to the LOOP, arbitrage serves as the mechanism ensuring the validity of APPP, making it generally considered a long-run relationship. Another way of thinking of PPP is in terms of the real exchange rate (RER). Thus, if APPP holds, the real effective exchange rate ought to be unity. However, as many markets are characterized by arbitrage for profit motives, the volatility in RER is expected to be consistent for economies involved in international trade.

2.2.2        Mundell-Fleming Model

Mundell-Fleming model was introduced by Mundell and Fleming in 1962, however, Dornbusch (1976) extended the model by integrating the IS-LM with the balance of payments, focusing on exchange rates and international transactions called the IS-LM-BP model. This theoretical framework of the model explains that the relationship between exchange rate fluctuations and macroeconomic performance in a small open economic incapable of influencing the international market demand and supply.

According to IS-LM-BP model, exchange rate shifts can affect economic growth through multiple channels. Firstly, currency fluctuations impact trade balances, thereby influencing economic expansion. A depreciating domestic currency can enhance export competitiveness, stimulating production and employment in export sectors. Conversely, an appreciating currency may hinder growth by making exports pricier in global markets. Secondly, exchange rate movements influence capital flows and investment decisions. In the model, a domestic interest rate increase spurred by currency appreciation can attract capital inflows, bolstering investment and improve economic performance. Conversely, a currency depreciation may lead to capital outflows, potentially dampening investment and slowing growth. The model underscores the role of fiscal policy in shaping the link between exchange rates and growth. Expansionary fiscal policies like increased government spending can stimulate domestic demand, propelling growth. However, the impact of fiscal measures on growth can be modulated by exchange rate shifts. For instance, increase in government expenditure can depreciate currency elevating import prices, offsetting the positive effects of fiscal stimulus on growth. The model also indicates the role of monetary policy in shaping the link between exchange rates and macroeconomy. Expansionary monetary policy like increased in money supply under a flexible exchange rate system, depreciate the domestic currency, implicitly increasing inflation as this will increase prices. Also, the increase in the money supply reduces domestic interest rates in the money market. Lower domestic interest rates relative to foreign interest rate leads to capital outflow. However, exchange rate fluctuations influence macroeconomic performance through their impacts on production, prices, current account, capital flows, and interaction with fiscal policy and monetary policy, as outlined in the Mundell-Fleming model. The specific outcomes depend on the direction and nature of exchange rate movements and the prevailing economic conditions within the country.

The Elasticity Approach

The elasticities approach explains how changes in the real exchange rate affect a country’s trade balance. A real exchange rate appreciation makes imports cheaper and exports more expensive. This should lead to higher imports and lower exports, but the impact depends on how sensitive these flows are to price changes. The elasticity measures how much something changes, like import volume, in response to a price change, like a stronger currency. High elasticity means a more significant change. A weaker currency might not boost exports much if a country relies on essential imports without good substitutes. Devaluation’s success in improving the trade balance depends on how sensitive import and export demand is to price changes. If import and export demand are elastic, then devaluation can significantly improve the trade balance.

2.3         The Production Flexibility

The impact of exchange rate volatility on FDI was argued using production flexibility and risk aversion arguments. Goldberg and Kolstad (1995) proposed the Production Flexibility Argument, which asserts that exchange rate fluctuations encourage FDI. The argument revolves around the notion that foreign businesses possess more flexibility in manufacturing procedures than domestic enterprises. Foreign manufacturers can adjust their production inputs in response to fluctuating exchange rates. This flexibility allows them to make investments in foreign markets. Conversely, the risk aversion argument holds that fluctuations in exchange rates discourage FDI. Increased volatility in exchange rates poses challenges for companies in projecting future earnings. This uncertainty reduces their willingness to participate in foreign markets, reducing FDI. They further argued that the choice of these hypotheses depends on the timeframe. In the short term, production factors are generally inflexible, and enterprises have little capacity to adjust to fluctuations in exchange rates. Thus, the case for risk aversion may be more compelling if volatility impedes investment by creating uncertainty about future earnings. However, companies have more freedom to adjust production in the long run. They can adjust the use of puts or use economic scale shift manufacturing sites to benefit from economies of scale. In this situation, the case for production flexibility becomes more significant as volatility presents chances for optimizing costs, which attracts FDI.

2.4         Empirical Review

Many studies have examined the impact of exchange rate variation on various macroeconomic factors in recent decades. This section concentrates on empirical studies that studied the impact of exchange rates on GDP, current account, FDI, and inflation.

2.4.1        Exchange Rate Volatility and Economic Growth

The impact of exchange rate volatility on economic growth has been a topic of interest in recent years, with research providing mixed results. For example, Schnabl (2008) conducted a panel study on small open economies in the European Monetary Unity (EMU) region using both GMM and GLS and found that volatile exchange rates have a negative impact on economic growth. Similarly, Umaru et al. (2018) examined English-speaking countries in West Africa using a fixed and random effects model and noted that volatile exchange rates hinder growth in these countries. Barguellil (2018) studied Middle Eastern and North African countries using an ARDL panel model and found similar results. Toroitich et al. (2022) analyzed the Kenyan economy using a VEC model. They found a negative impact, while Mbuyi et al. (2022) employed a VAR model on the Congolese economy and observed a similar result. Dagume (2022) also found a negative result South African economies using the OLS method. However, Polodoo (2012) studied small island developing states using the OLS method and Z-score for volatility calculation and found a positive impact on economic growth. Mahmood (2011) studied the Pakistani economy, using GARCH and OLS for estimation, and found that the exchange rate positively affects GDP. Rapetti (2020) surveyed past studies on the impact of the real exchange rate on economic growth and found a positive impact of the exchange rate on growth in most studies. Touitou et al. (2019) also observed a positive impact using a VAR model on the Algerian economy. However, Idris (2019) and Mahjoub (2014) acknowledged that the exchange rate has no significant impact on economic growth in Nigeria and Sudan, respectively, using the ARDL and VAR models.

2.4.2        Exchange Rate Volatility and Current Account Balance

Like its effect on economic growth, volatile exchange rates on the current account remain debated. Studies have not reached a consensus, with evidence pointing towards positive, negative, and insignificant relationships. Shlaymoon et al. (2022) found an inverse link to the current account in a panel study of Malaysia, Egypt, and Iraq. Similarly, Etienne (2023) observed a negative effect in Rwanda using a VEC model. Also, Polodoo (2012) studied small island developing states using the OLS method and Z-score for volatility calculation and found a positive impact of exchange rate volatility on the current account. Conversely, other research suggests a positive influence. Purwono et al. (2018) identified a positive effect on Indonesia’s current account using a simultaneous model. Additionally, some studies have not found a statistically significant relationship. Etienne (2023) also found no short-run impact on Rwanda’s current account. Al-Hamdi et al. (2023) reported similar results for Jordan using the ordinary least squares (OLS) method, and Mahjoub (2014) found similar results from Sudense’s economy.

2.4.3        Exchange Rate Volatility and FDI

Similar to the theoretical arguments, the empirical evidence on this topic is mixed. Dagume (2022) and Thujiyanthan (2021) found a positive impact of exchange rate volatility on FDI inflows. Their reasoning centers on the concept of FDI as “export substitution,” when exchange rates become volatile in a host country, multinational firms may find it more attractive to invest directly by setting up local production facilities rather than exporting goods. This allows them to avoid the risks associated with fluctuating currency exchange rates. However, other studies like Ramzam (2021), Moraghen et al. (2021), Mahjoub (2014), Mahmood (2011), and Hniya et al. (2021) reported differently. They found that high exchange rate volatility creates significant currency risk for investors. This risk discourages them from investing in countries with volatile exchange rates and incentivizes them to shift their investments to more stable economies. In addition, many of these studies used the GARCH to calculate exchange rate volatility (Ramzam, 2021; Thujiyanthan, 2021; Dagume (2022). Also,  Hniya et al. (2021), Ramzam (2021), and Thujiyanthan (2021) used the ARDL estimation approach, Moraghen (2021) used the VEC model for estimation, and Dagume (2022) used the OLS approach.

2.4.4        Exchange Rate Volatility and Inflation

The impact of the volatility of exchange rates on inflation has been evaluated using different methods. Using the VEC model, Nuhu (2021), Washima (2022), and Njoku and Nwaimo (2019) discovered that exchange rate volatility leads to higher inflation rates. Similarly, Kara, Dede (2023), Dagume (2022), and Harnphattananusorn (2023) discovered a positive correlation between the exchange rate and inflation using the ARDL model. On the other hand, Lowe (2019) noted no long-term connection between the exchange rate and inflation in the Gambia. This was determined using the Johansen co-integration method. Additionally, these studies employed the GARCH method to calculate volatility in the exchange rate.

2.5         Overview of the Literature Review

The review has shown that the implications of exchange rate volatility on macroeconomic factors such as economic growth, current account, FDI, and inflation remain inconclusive. In addition, these studies used various statistical methods, models, and sampling across different countries and timeframes, which justifies their different findings. To the knowledge of this study, there is a gap in empirical studies on such issues in Liberia. As such, this study would add to the empirical literature on this issue from the perspective of the Liberian economy.

CHAPTER 3

RESEARCH METHODOLOGY

3.1         Introduction

This section outlines the methodology and procedures for data analysis. It explains the data sources and selected macroeconomic indicators and presents the conceptual and empirical models. The chapter concludes with the estimation methods utilized for analysis in this research.

3.2         Conceptual Framework

The research will employ GDP as a surrogate for economic growth, inflation as a direct representation of the price level, FDI inflow as an indicator for capital inflow, and the current account as a trade, service, and transfer component of the balance of payments. The following outlines the structure of this study’s independent, dependent, and control variables.

3.3         Empirical Model

This study will focus on four macroeconomic indicators to examine the impact of exchange rate volatility on macroeconomic performance in Liberia. These factors are assumed to reflect the performance of Liberia’s macroeconomic environment. As structured in the conceptual framework, each macroeconomic factor will be used as an endogenous variable, and the exchange rate volatility will be used as an exogenous variable. Other essential variables which are backed by empirical and theoretical literature to have influence on the endogenous variables, will be controlled for as illustrated in the matrix below:

In the GDP equation, the study will control for government expenditure, domestic credit, and trade openness. The selection of these variables is acknowledged in previous studies (Mahjoub, 2014; Toroitich et al., 2022).

In the IS-LM-BP model concept, the flow of capital is influence by domestic interest rate and foreign interest rate. However, since the study is focus on capital inflow, the FDII equation will control for domestic interest rate.

According to the elasticity approach to the balance of payments, import is a function of income and autonomous import and export is the function of the exchange rate and autonomous export. Therefore, in the  equation, the study will control for GDP a proxy for income.

In the IS-LM-BP model concept, money supply directly influences prices and government expenditure influence aggregate demand which influence prices. Therefore, the  equation will control for money supply and government expenditure.

3.4         Variable Definition and measurement

GDP: the total value of all finished goods and services produced in a year within a country’s borders. Following the work of Idris (2019), GDP is used here as a proxy for economic growth and it is in nominal form and is measure in United state Dollar.

Inflation: is the general changes in the price level in a given year (Rudd, 2022). Like the work of Nuhu (2021), the inflation rate is use as a surrogate for the changing price level. It will be measured as an average percentage change in consumer price level in a given period.

FDI Inflow: is the direct investment made by foreign agents in the domestic economy in a fiscal year (Jannat, 2020). In line with Ramzam (2021), it is measured as a capital inflow within a given year.

Current Account: is the net of all trade and transfer payments between a country and the rest of the world in a year (WDI, 2024). It is measured as the sum of net income from abroad in US Dollar.

Exchange Rate Volatility: is the average change in the value of a country’s current relative to another country’s currency with a specific period. The exchange rate volatility will be measured using the GARCH method as indicated in section 3.4.1. However, the study will collect data on the nominal exchange rate for one Liberian dollar for US dollar.

Trade Openness: is the ratio of merchandise trade to GDP in a given year. It will be measured as percentage to GDP in a given year.

Money Supply: is the sum of the currency in circulation, and demand, and saving deposits. It is used as a proxy for monetary policy that impacts inflation. For this study, it is measured in US dollars.

Domestic Credit: is the aggregate domestic loan in the economy, excluding banks, in a given year. It measures the total amount of loans commercial banks provide to economic entities, excluding loans given to commercial banks. For this study, it will be measured in US dollars.

Government Expenditure: refers to the government’s total capital and current spending in a fiscal year. The study measurements for this variable will be in US dollars.

3.4.1        Real Exchange Rate Volatility Measure

Quantifying exchange rate volatility is complex (Nyambariga, 2018); however, studies have used several methods to tackle it. Ramzam (2021) used the General Autoregressive Conditional Heteroscedasticity (GARCH) model to measure volatility clustering. In contrast, Mia and Rahman (2019) used the Autoregressive Conditional Heteroscedasticity (ARCH) model to consider historical volatility when predicting future volatility. Additionally, Nyambariga (2018) utilized the conditional variance of the first-order ARCH to analyze volatility patterns. This study will use the GARCH model to calculate the volatility of the US$/L$ exchange rate. The GARCH model presents the advantage of capturing both symmetric and asymmetric effects of past exchange rate movements on future volatility. The following equation represents the model:

………………………………………….. 3‑1

Volatility ………………………………………………… 3‑2

3.5         Estimation Technigue

The study will use a VAR or VEC model to study the impact of exchange rate volatility on the macroeconomy. The study will estimate the VEC model if there is a present of long run relationship, otherwise, the study will estimate the VAR model. Sims (1980) and Litterman (1986) proposed VAR as an alternative to simultaneous models. Its general acceptance is due to its forecasting accuracy compared to the simultaneous models. To mitigate endogeneity, the model considered all variables in the system as endogenous variables Litterman (1986). The VAR method, when applied with a maximum p-lag, fits the data as follows:

………………..……………….. 3‑3

is a vector of endogenous variables at a time  (GDP, FDI, CA, and Infl) for this study.  is the constant, and  -Are the parameters and  is the error term. The above equation can be rewritten in the VECM form as below:

……………………..……………… 3‑4

Where:  and

The selection of a precise lag length for an autoregressive model is important as it influences the findings of a time series study. (Liew, 2004). This study will use Akaike’s Information Criterion (AIC) for lag selection. This choice is based on suitability for datasets with less than 120 observations(Liew, 2004).

3.6         Pre-estimation Test

To ensure the reliability of the study estimates and forecasts, this study will conduct a series of pretests, including normality tests, unit root tests, cointegration tests, and an analysis of descriptive statistics.

3.6.1        Normality Test

This test is essential for time series data as it shows the distributive property of the variables (Górecki et al., 2017). If a variable in econometric analysis deviates from normal distribution, its error term will also exhibit non-normal distribution, impacting the hypothesis decision. This study will use the Shapiro-Wilk W statistics developed by Shapiro and Wilk (1965). The Shapiro-Wilk W statistics for normality are primarily appropriate for observations less than 2000. This study will use the 5 percent level of significance.

Hypothesis:

H_o: Variable follow a normal distributed

H₁: Variable do not follow a normal distributed

3.6.2        Stationarity test

The unit root test is an important factor in econometric analysis for time-based variables. (Phillips, 1987). The presence of a unit root leads to spurious regression problems, leading to unreliable results. To test the stationarity level of the variables, the study will use the Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) unit root tests.

3.6.2.1       TheAugmentedDickey-FullerTest

The ADF test is a powerful tool for analyzing the stationarity of variable data. It is built upon the standard Dickey-Fuller (DF) test by addressing a key limitation of one lag. The ADF test overcomes this limitation by incorporating multiple lags of the differenced variable into the model. This allows it to account for more complex autocorrelation patterns and provide a more accurate assessment of stationarity. The equation for this is below:

Δ𝑦_𝑡 = 𝛼 + 𝛽𝑦_𝑡−1 + 𝛿𝑡 + ∑ ф_𝑗 ∆𝑦_𝑡−𝑗 + 𝑒_𝑡   ……………………………… 3‑5

The ADF test focuses on a null hypothesis that the variable has a unit root. The alternative hypothesis states that the variable is stationary.

3.6.2.2       The Phillips-Perron Test for Unit Roots

The PP test is an alternative to the ADF test for evaluating stationarity levels. While both tests share similar hypotheses, the PP test is robust to serial correlation. The PP test addresses this limitation by employing a non-parametric approach that makes it less sensitive to these correlations. This increases the test’s reliability, particularly when dealing with data contradicting the ADF test’s assumptions. Like the ADF, the PP holds the same hypothesis. The regression for the PP test is as follows:

𝑦_𝑡 = 𝛼 + 𝛽𝑦_𝑡−1 + 𝑒_𝑡 ……………………………………………………… 3‑6

3.6.2.3       Test for Structural Breaks

Given Liberia’s recent history, which includes a prolonged civil war, the Ebola crisis, the COVID-19 pandemic, and other political instabilities, this study acknowledges the potential for structural breaks. These events can significantly alter the relationships between variables, leading to misleading forecasts if such breaks are not considered. To address this concern, the study will employ the Bai and Perron (1998, 2003) unit root test for structural breaks. This test offers a distinct advantage over traditional unit root tests: its ability to identify multiple breaks within a time series. This is particularly relevant for this study, as these historical events may have caused multiple shifts in Liberia’s underlying economic relationships. The regression equation for this is as follows:

𝑌_𝑡 = 𝑋^′𝛽 + 𝑍^′𝛿_𝑗 + 𝜇_𝑡 ……………………………………………………… 3‑7

3.7         Cointegration Test

The model for this study will be chosen depending on whether cointegration exists among the variables of interest. Cointegration is the phenomenon where a linear combination of two non-stationary series results in a stationary series (Johansen, 1991). This indicates that even though the individual series exhibit trends or fluctuations over time, a specific combination maintains a long-term equilibrium relationship. This study will employ the Johansen (1995) test for cointegration. Compared to other tests (Engel- Granger test and ARDL-bound test), this test is efficient when dealing with multiple cointegrating equations, which might be the case for our analysis. If cointegration exists between the variables, VEC model is the preferred choice. With the VEC model, the study will capture the short-term and long-term dynamics between the variables. In the absence of cointegration, the VAR model will be used. This model focuses on the short-term dynamic interactions between the variables without imposing long-term dynamic constraints. The evaluation process will center around hypotheses, with the null hypothesis positing the absence of cointegrating relationships among the variables, while the alternative hypothesis suggests the presence of one or more cointegrating relationships. This evaluation relies on a trace statistic, calculated as:

………………………………………………. 3‑8

If the computed trace statistic exceeds the upper bound at the established 5% significance level, the research will reject the null hypothesis. Conversely, if the computed trace statistic falls below both the lower and upper bounds at the 5% level, the study will accept the null hypothesis. However, if the computed trace statistic exceeds the lower bound but falls below the upper bound, the research will remain undecided. In such a scenario, the data will undergo reevaluation from its source and be rerun.

3.8         Model Estimation

The model selection is contingent upon the cointegration test, as outlined in Section 3.7. If a long-run equilibrium relationship exists, a VECM will be estimated; otherwise, a VAR model will be used. VAR and VEC models may result in many estimated parameters since they incorporate lagged variables and regard all variables as endogenous, complicating the interpretation of the results. The Dynamic-Multiplier Function,  Granger Causality, Impulse Response Function (IRF), and Forecast Error Variance Decomposition (FEVD) have been employed for a more accurate and detailed interpretation of results (Conteh, 2010). This study will use Granger causality, IRF, and FEVD to explain its findings.

3.8.1        Granger Causality Test

Developed by Clive Granger in 1969, the Granger causality test is a valuable tool for investigating causal relationships between time series variables. This test is instrumental in establishing short-run relationships among variables. The Granger causality test can reveal three potential causal directions in a system with two variables. For instance, if past exchange rate values significantly explain future GDP, past GDP values do not explain future exchange rates. There is evidence of unidirectional causality from exchange rate to GDP. If past exchange rates and GDP values significantly influence each other’s future values, there is evidence of bidirectional causality. Moreover, if past values of neither variable significantly explain the future values of the other, then the Granger causality test suggests no statistically significant causal relationship in the short run. The Granger causality for this study will be based on the below hypothesis.

Hypothesis:

H_o: No causal relationship

H_A: Causal relationship

3.8.2        Impulse Response Function

The IRF shows how a variable deviation influences another variable and the duration of this effect (Agusalim, 2017). The impulse variable affects itself and propagates to other endogenous variables in a model. For instance, the IRF demonstrates how the GDP reacts to shocks in variables such as GDP, exchange rate, inflation, and domestic credit. This analytical approach will encompass all variables under consideration. If any variable experiences a shock, the IRF explains the resulting impact on the variable and other system variables.

3.8.3        Forecast-Error Variance Decomposition

The FEVD evaluates shock in one endogenous variable impact on others. (Agusalim, 2017). The statement measures the proportion of the forecast-error variability of an endogenous variable that may be attributed to shocks on itself or another variable in the system.

3.9         Post-Estimation Tests

This study will perform various post-estimation tests to confirm the veracity of the findings. These tests include the evaluation of the residuals for autocorrelation and model stability using the cumulative sum of recursive Residuals test and normality of the residuals suing the Jarque Bera normality test.

3.9.1        Normality Test

This study will use the Jarque-Bera test for normality to evaluate if the residuals for normal distribution. The Jarque-Bera test statistic is based on the sample skewness and kurtosis of the residuals. The test statistic JB follows a chi-squared distribution with 2 degrees of freedom under the null hypothesis. The decision for this study will be based on the below hypothesis:

Hypothesis:

H_o: Residuals follow a normal distributed

H_A: Residuals do not follow a normal distributed

If the calculated probability value exceeds the 5 percent significance level, the study will accept the null hypothesis. However, if the probability value falls below the 5 percent significance level, the study will reject the null hypothesis.

3.9.2        Serial Correlation Test

The study will conduct a serial correlation test to identify any potential autocorrelation within the residuals. Autocorrelation means the errors are not independent of each other, which can also affect the validity of our results. The Breusch-Godfrey LM test will be employed to assess this issue. The test statistic LM follows an approximate chi-squared distribution with the number of lags used in the test as its degrees of freedom. The decision will be based on the below hypothesis:

Hypothesis:

H_o: No serial correlation at the specified lag

H_A: Serial correlation exists at the specified lag

If the p-value associated with the Breusch-Godfrey LM test is greater than the 5 percent, the study will fail to reject the null hypothesis. Conversely, if the -value is less than or equal to the chosen significance level, the study will reject the null hypothesis in favor of the alternative hypothesis.

3.9.3        Stability Test

The stability test in this study will use the Cumulative Sum of Recursive Residuals (CUSUM) method. This test evaluates the stability of the model’s residuals over time. The decision for the test will be based on the hypothesis:

Hypothesis:

H_o: The model residuals exhibit stability over time

H_A: The model residuals do not exhibit stability over time.

If the CUSUM plots remain within the 5 percent boundaries, the study will fail to reject the null hypothesis, indicating that the model residuals maintain stability over time, however, if the CUSUM plots is greater than the 5 percent boundaries, the study will reject the null hypothesis, indicating that the model residuals do not exhibit stability over time.

3.10     Data Sources and Signs

This study will use annual data from 1990 to 2022 obtained from the World Bank’s World Development Indicators (WDI), the Ministry of Finance, Development, and Planning (MFDP), and the Central Bank of Liberia (CBL). Analyzing time series data allows for examining trends and patterns over this period, as proposed by Jalil and Ma (2008). This data type is useful for studying the impact of exchange rate volatility on macroeconomic variables. All variables will be transformed into logarithmic form for analysis. Table 3.1 provides the variables of interest, sources, symbols, and expected signs.

Table 3.1: Data Sources and Signs

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