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# Exchange Rate Pass-Through to inflation and its implications for monetary policy conduct in Mauritania

Yahya-Abou Ly

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# Exchange Rate Pass-Through to inflation and its implications for monetary policy conduct in Mauritania

Yahya-Abou Ly

► To cite this version:

Yahya-Abou Ly. Exchange Rate Pass-Through to inflation and its implications for monetary policy conduct in Mauritania. 2024. hal-04558143

HAL Id: hal-04558143

https://hal.science/hal-04558143v1

Preprint submitted on 29 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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Exchange Rate Pass-Through to inflation and its implications for monetary
policy conduct in Mauritania

Presented by:

Yahya-Abou LY, PhD<fnref n="1" />

**Abstract :**

This paper aims to estimate the exchange rate pass-through to prices in the long term in Mauritania. To do this, a VECM model and impulse responses using Impulse Responses by Local Projections Jordà's (2005) were employed on a sample of monthly data covering the period from January 2015 to December 2023. Our results indicate that the effects of ERPT transmission are more pronounced with the MRU exchange rate against the US dollar (USD). Specifically, a 1% depreciation of the ouguiya against the dollar results in a 0.42% increase in the Consumer Price Index (CPI), 0.34% with NEER, 0.35% on the food price index, and 0.20% against the euro. These findings suggest that exchange rate fluctuations remain a potentially significant source of inflation in the country.

**Keywords** : Exchange rate pass-through, Consumer Price Index, inflation, VECM, Local Projections.

**JEL Classification** : F31, E31, E51, C32, E52

<footnote n="1">¹ Head of the Economic Analysis Department at the Central Bank of Mauritania and Researcher at the Center for Research in Macroeconomics, Growth and Development (URMCD) yayaabouly@yahoo.fr / yahya.ly@bcm.mr/</footnote>
Exchange Rate Pass-Through to inflation and its implications for monetary
policy conduct in Mauritania

Presented by:

Yahya-Abou LY, PhD<fnref n="1" />

**Abstract :**

This paper aims to estimate the exchange rate pass-through to prices in the long term in Mauritania. To do this, a VECM model and impulse responses using Impulse Responses by Local Projections Jordà's (2005) were employed on a sample of monthly data covering the period from January 2015 to December 2023. Our results indicate that the effects of ERPT transmission are more pronounced with the MRU exchange rate against the US dollar (USD). Specifically, a 1% depreciation of the ouguiya against the dollar results in a 0.42% increase in the Consumer Price Index (CPI), 0.34% with NEER, 0.35% on the food price index, and 0.20% against the euro. These findings suggest that exchange rate fluctuations remain a potentially significant source of inflation in the country.

**Keywords** : Exchange rate pass-through, Consumer Price Index, inflation, VECM, Local Projections.

**JEL Classification** : F31, E31, E51. C32, E52

<footnote n="1">¹ Head of the Economic Analysis Department at the Central Bank of Mauritania and Researcher at the Center for Research in Macroeconomics, Growth and Development (URMCD) yayaabouly@yahoo.fr / yahya.ly@bcm.mr/</footnote>
## Introduction

The study of exchange rates and inflation is essential in the field of economics, as these two variables play a crucial role in the economic stability of a country. The exchange rate, representing the relative value of a currency compared to another, and inflation, measuring the general increase in prices of goods and services over a given period, are interconnected and directly influence economic decisions, monetary policy, and the daily lives of citizens. Inflation, when controlled, can stimulate economic growth by encouraging consumption. However, excessive inflation can lead to a loss of purchasing power, destabilize financial markets, and undermine consumer confidence. Central banks implement monetary policies, including interest rate adjustments, to maintain stable and predictable inflation. On the other hand, the exchange rate has major implications for international trade, foreign investments, and economic competitiveness (Burstein et Gopinath, 2014). Exchange rate fluctuations can influence exports and imports, thus affecting a country's trade balance. Businesse, investors, and governments closely monitor these fluctuations to anticipate economic trends and make informed decisions.

The study of exchange rate movements on domestic prices, known as exchange rate pass-through (ERPT), has been identified as one of the determinants of a country's competitiveness in the global market. Additionally, in a small economy, the exchange rate serves as an important channel for transmitting monetary policy, as exchange rate movements can affect the national inflation rate. The literature states that the nominal exchange rate affects variations in inflation or production.

This leads us to address the concept of the degree of transmission of exchange rate variations to prices, or "pass-through." The concept of exchange rate pass-through to inflation plays a crucial role in understanding economic mechanisms, especially in the context of today's globalized economy (Goldberg and Knetter, 1997). Pass-through reflects how international exchange rate variations are transmitted to domestic price levels, particularly through their influence on inflation. This dynamic is crucial as it directly impacts price stability, monetary policy, and consequently, the overall health of the economy.

In this perspective, we inquire about the impact of exchange rate fluctuations on domestic prices and on the conduct of monetary policy in Mauritania. To address this issue, the general objective is to analyze the impact of the exchange rate on domestic prices in Mauritania. More specifically, the study aims to :

* Estimate the degree of transmission of the nominal effective exchange rate to the consumer price index.

* Analyze the transmission effect of the nominal effective exchange rate, in US dollars (USD) and euros against the ouguiya, on the food price index.

* Evaluate the speed of response to an exchange rate shock on the money supply and the consumer price index.

Through this study, we seek to deepen our understanding of the nature of exchange rate pass-through to inflation by examining the underlying mechanisms and assessing the extent of these influences in the specific context of Mauritania. This exploration will help enlighten monetary policymakers about the potential implications of exchange rate fluctuations on price stability and the necessary adjustments within the national economy.

## Literature Review

The question of the exchange rate pass-through to domestic prices has been the subject of numerous studies and controversies at both theoretical and empirical levels. Regarding theoretical studies, the earliest ones were conducted at the microeconomic level and examined the extent of exchange rate pass-through to import prices in specific sectors using disaggregated data (Goldberg and Knetter, 1997 ; Menon, 1995).

Subsequently, in the early 2000s, theoretical literature initiated the question of the link between the extent of exchange rate pass-through to domestic prices and monetary policy. These studies differ in their approaches, with some based on macroeconomic models in an open economy and the controversial role of expenditure switching (changes in allocation) due to the exchange rate, and others based on the
**Introduction**

The study of exchange rates and inflation is essential in the field of economics, as these two variables play a crucial role in the economic stability of a country. The exchange rate, representing the relative value of a currency compared to another, and inflation, measuring the general increase in prices of goods and services over a given period, are interconnected and directly influence economic decisions, monetary policy, and the daily lives of citizens. Inflation, when controlled, can stimulate economic growth by encouraging consumption. However, excessive inflation can lead to a loss of purchasing power, destabilize financial markets, and undermine consumer confidence. Central banks implement monetary policies, including interest rate adjustments, to maintain stable and predictable inflation. On the other hand, the exchange rate has major implications for international trade, foreign investments, and economic competitiveness (Burstein et Gopinath, 2014). Exchange rate fluctuations can influence exports and imports, thus affecting a country's trade balance. Businesse, investors, and governments closely monitor these fluctuations to anticipate economic trends and make informed decisions.

The study of exchange rate movements on domestic prices, known as exchange rate pass-through (ERPT), has been identified as one of the determinants of a country's competitiveness in the global market. Additionally, in a small economy, the exchange rate serves as an important channel for transmitting monetary policy, as exchange rate movements can affect the national inflation rate. The literature states that the nominal exchange rate affects variations in inflation or production.

This leads us to address the concept of the degree of transmission of exchange rate variations to prices, or "pass-through." The concept of exchange rate pass-through to inflation plays a crucial role in understanding economic mechanisms, especially in the context of today's globalized economy (Goldberg and Knetter, 1997). Pass-through reflects how international exchange rate variations are transmitted to domestic price levels, particularly through their influence on inflation. This dynamic is crucial as it directly impacts price stability, monetary policy, and consequently, the overall health of the economy.

In this perspective, we inquire about the impact of exchange rate fluctuations on domestic prices and on the conduct of monetary policy in Mauritania. To address this issue, the general objective is to analyze the impact of the exchange rate on domestic prices in Mauritania. More specifically, the study aims to :

* Estimate the degree of transmission of the nominal effective exchange rate to the consumer price index.

* Analyze the transmission effect of the nominal effective exchange rate, in US dollars (USD) and euros against the ouguiya, on the food price index.

* Evaluate the speed of response to an exchange rate shock on the money supply and the consumer price index.

Through this study, we seek to deepen our understanding of the nature of exchange rate pass-through to inflation by examining the underlying mechanisms and assessing the extent of these influences in the specific context of Mauritania. This exploration will help enlighten monetary policymakers about the potential implications of exchange rate fluctuations on price stability and the necessary adjustments within the national economy.

**Literature Review**

The question of the exchange rate pass-through to domestic prices has been the subject of numerous studies and controversies at both theoretical and empirical levels. Regarding theoretical studies, the earliest ones were conducted at the microeconomic level and examined the extent of exchange rate pass-through to import prices in specific sectors using disaggregated data (Goldberg and Knetter, 1997 ; Menon, 1995).

Subsequently, in the early 2000s, theoretical literature initiated the question of the link between the extent of exchange rate pass-through to domestic prices and monetary policy. These studies differ in their approaches, with some based on macroeconomic models in an open economy and the controversial role of expenditure switching (changes in allocation) due to the exchange rate, and others based on the
Taylor model and its assumption of a decrease in the degree of exchange rate pass-through to domestic prices under credible monetary policy regimes, where low and stable inflation leads to low pass-through to prices, resulting in low inflation.

Furthermore, new macroeconomic models developed in open economies such as those by Obstfeld (2002) and Engel (2004), using fully specified dynamic general equilibrium models with wage and price rigidities and different price-setting assumptions for firms, show that assumptions about the extent of exchange rate pass-through significantly influence the effectiveness of monetary policy. These models suggest that the decrease in exchange rate pass-through is influenced by a high degree of trade integration, changes in market structure, and the weight of components in the consumer price index.

Regarding empirical studies, standard models assume linearity and stability of parameters. An almost uniform result of these empirical studies is that the pass-through is not instantaneous and occurs over a period. This finding has been confirmed for developed countries by Campa and Goldberg (2002; 2008), for emerging economies by Mihaljek and Klau (2001; 2008), and for developing countries by Akofio-Sowah (2009) and Razafimahefa (2012). The same finding has been confirmed in the long term by Barhoumi (2006) for emerging economies, particularly regarding the extent of exchange rate pass-through to trade prices (import prices and production prices).

Furthermore, it has been demonstrated that there is a positive relationship between average inflation and exchange rate pass-through. Thus, low inflation regimes are characterized by a low degree of pass-through. Finally, the more open an economy is, the higher the degree of pass-through it experiences.

For central banks, ERPT represents an important element in formulating monetary policy, both through its direct and indirect effects. Exchange rate fluctuations inevitably impact domestic prices and economic activity. For example, a depreciation of national currencies would lead to an increase in prices of imported goods, which could induce widespread inflationary pressure. Moreover, central banks are aware of the impact of their monetary decisions on the value of their national currency (exchange rate channel). In general, research conducted by central banks reveals a positive and bidirectional correlation between the degree of ERPT and inflation. These studies show that effective inflation control is associated with low exchange rate transmission due to effective inflation expectations anchoring. Specifically, low ERPT transmission leads to controlled inflation levels, facilitating the achievement of price stability objectives. When an inflation targeting regime is in place, the extent of ERPT seems to inform policymakers about the macroeconomic transmission degree of their monetary decisions through the exchange rate channel.

Nevertheless, the analysis of the extent of exchange rate pass-through to domestic prices has sparked growing interest in Mauritania but remains less studied. Such a limitation justifies, among other things, further research in this area. Estimating the degree of exchange rate transmission and tracking it over time is of paramount importance for the Central Bank of Mauritania, which is committed to an orderly and gradual transition to a flexible exchange rate regime. This transition involves a shift from monetary targeting to a rate-based monetary policy regime, aiming to develop a forward-looking monetary policy focused on exchange rate flexibility, with the ultimate goal of price stability<fnref n="2" />.

To achieve these objectives, the study relies on three research hypotheses:

* The influence of the exchange rate on prices is not instantaneous but rather long-term.

* The variation of the exchange rate in US dollars against the ouguiya significantly influences food prices, unlike other exchange rates.

* An exchange rate shock impacts both the money supply and domestic prices.

## Stylized Facts

IMF (2020), Annual Report on Exchange Arrangements and Exchange Restrictions<fnref n="3" />, Mauritania's exchange rate regime is classified as a managed float, a category that the report places under "flexible arrangements." This exchange rate regime has certainly contributed to inflation stability and maintained

<footnote n="2">² https://www.bcm.mr/IMG/pdf/qa_2.pdf</footnote>

<footnote n="3">³ Rapport annuel sur les accords de change et les restrictions de change 2020 (imf.org)</footnote>
Taylor model and its assumption of a decrease in the degree of exchange rate pass-through to domestic prices under credible monetary policy regimes, where low and stable inflation leads to low pass-through to prices, resulting in low inflation.

Furthermore, new macroeconomic models developed in open economies such as those by Obstfeld (2002) and Engel (2004), using fully specified dynamic general equilibrium models with wage and price rigidities and different price-setting assumptions for firms, show that assumptions about the extent of exchange rate pass-through significantly influence the effectiveness of monetary policy. These models suggest that the decrease in exchange rate pass-through is influenced by a high degree of trade integration, changes in market structure, and the weight of components in the consumer price index.

Regarding empirical studies, standard models assume linearity and stability of parameters. An almost uniform result of these empirical studies is that the pass-through is not instantaneous and occurs over a period. This finding has been confirmed for developed countries by Campa and Goldberg (2002; 2008), for emerging economies by Mihaljek and Klau (2001; 2008), and for developing countries by Akofio-Sowah (2009) and Razafimahefa (2012). The same finding has been confirmed in the long term by Barhoumi (2006) for emerging economies, particularly regarding the extent of exchange rate pass-through to trade prices (import prices and production prices).

Furthermore, it has been demonstrated that there is a positive relationship between average inflation and exchange rate pass-through. Thus, low inflation regimes are characterized by a low degree of pass-through. Finally, the more open an economy is, the higher the degree of pass-through it experiences.

For central banks, ERPT represents an important element in formulating monetary policy, both through its direct and indirect effects. Exchange rate fluctuations inevitably impact domestic prices and economic activity. For example, a depreciation of national currencies would lead to an increase in prices of imported goods, which could induce widespread inflationary pressure. Moreover, central banks are aware of the impact of their monetary decisions on the value of their national currency (exchange rate channel). In general, research conducted by central banks reveals a positive and bidirectional correlation between the degree of ERPT and inflation. These studies show that effective inflation control is associated with low exchange rate transmission due to effective inflation expectations anchoring. Specifically, low ERPT transmission leads to controlled inflation levels, facilitating the achievement of price stability objectives. When an inflation targeting regime is in place, the extent of ERPT seems to inform policymakers about the macroeconomic transmission degree of their monetary decisions through the exchange rate channel.

Nevertheless, the analysis of the extent of exchange rate pass-through to domestic prices has sparked growing interest in Mauritania but remains less studied. Such a limitation justifies, among other things, further research in this area. Estimating the degree of exchange rate transmission and tracking it over time is of paramount importance for the Central Bank of Mauritania, which is committed to an orderly and gradual transition to a flexible exchange rate regime. This transition involves a shift from monetary targeting to a rate-based monetary policy regime, aiming to develop a forward-looking monetary policy focused on exchange rate flexibility, with the ultimate goal of price stability<fnref n="2" />.

To achieve these objectives, the study relies on three research hypotheses:

* The influence of the exchange rate on prices is not instantaneous but rather long-term.

* The variation of the exchange rate in US dollars against the ouguiya significantly influences food prices, unlike other exchange rates.

* An exchange rate shock impacts both the money supply and domestic prices.

## Stylized Facts

IMF (2020), Annual Report on Exchange Arrangements and Exchange Restrictions<fnref n="3" />, Mauritania's exchange rate regime is classified as a managed float, a category that the report places under "flexible arrangements." This exchange rate regime has certainly contributed to inflation stability and maintained

<footnote n="2">² https://www.bcm.mr/IMG/pdf/qa_2.pdf</footnote>

<footnote n="3">³ Rapport annuel sur les accords de change et les restrictions de change 2020 (imf.org)</footnote>
the country's economic stability. However, in a context of increasing international openness, which exposes the Mauritanian economy to external shocks, and being a net exporter of commodities, it is vulnerable to fluctuations in the terms of trade. Current account fluctuations are a source of macroeconomic and financial instability that requires exchange rate adjustments.

It's in this context that authorities decided to undertake a gradual migration from a managed float exchange rate regime to a flexible exchange rate regime in 2023. It is estimated that a more flexible exchange rate regime would help absorb real shocks and mitigate economic and financial volatility while preserving external maneuverability and better aligning the exchange rate with the economy's fundamentals. To achieve this, the new exchange rate regime is determined within a fluctuation band of +/-5% relative to a reference rate set by the Central Bank of Mauritania (BCM) based on a currency basket. This currency basket consists of the US dollar and the euro, accounting for 80% and 20%, respectively.

Recognized as an exporter of raw materials such as iron ore, gold, and copper, it is exposed to fluctuations in the terms of trade. Despite the recorded economic growth, households remain vulnerable, primarily due to heavy reliance on extractive industries and fishing, as well as, to a lesser extent, agriculture and livestock. The prospect of exploiting new resources such as oil and gas in 2024 adds to this dynamic.

The demand side, national economic activity is primarily supported by consumption, exports, and investments. Indeed, in 2023, exports and national imports accounted for 10.2% and 3.3% of GDP, respectively, while investments constituted 32.1% of GDP. However, the current account balance is generally in deficit due to external dependency, particularly the worsening trade deficit. Fluctuations in the current account generate macroeconomic and financial instability, necessitating adjustments to the exchange rate.

<figure><img src="image_2.png" /><figcaption>**Figure 1 : Évolutions du taux de change et du taux d'inflation en Mauritanie (2016-2023)**</figcaption></figure>

*Source : Author based on data from the IMF and the Central Bank of Mauritania*

The graph in Figure 1 illustrates that both the nominal effective exchange rate (NEER) and the real effective exchange rate (REER) follow the same trend as the price index. An increase in NEER and REER indicates depreciation. Conversely, a decrease in USD_MRU and EURO_MRU signifies a bilateral depreciation of the local currency. Figure 1 depicts episodes of exchange rate depreciation tending to coincide with periods of inflationary pressure, notably during the period from 2020 to 2021 (table 1). During this time, the Central Bank of Mauritania (BCM) actively adjusted its policy rate to steer monetary policy, with successive decreases until the end of 2021.

In 2022, the graph indicates an increase in inflation due to intensified inflationary pressures caused by rising prices of energy and food products in international markets. The national consumer price index thus recorded an average increase of 9.6% over 12 months and 11% year-on-year in 2022, compared to respective declines of 4.9% and 1.6% in 2023. In response to this inflation, the BCM strengthened its cautious stance in the face of escalating inflationary pressures and risks related to the persistence of
the country's economic stability. However, in a context of increasing international openness, which exposes the Mauritanian economy to external shocks, and being a net exporter of commodities, it is vulnerable to fluctuations in the terms of trade. Current account fluctuations are a source of macroeconomic and financial instability that requires exchange rate adjustments.

It's in this context that authorities decided to undertake a gradual migration from a managed float exchange rate regime to a flexible exchange rate regime in 2023. It is estimated that a more flexible exchange rate regime would help absorb real shocks and mitigate economic and financial volatility while preserving external maneuverability and better aligning the exchange rate with the economy's fundamentals. To achieve this, the new exchange rate regime is determined within a fluctuation band of +/-5% relative to a reference rate set by the Central Bank of Mauritania (BCM) based on a currency basket. This currency basket consists of the US dollar and the euro, accounting for 80% and 20%, respectively.

Recognized as an exporter of raw materials such as iron ore, gold, and copper, it is exposed to fluctuations in the terms of trade. Despite the recorded economic growth, households remain vulnerable, primarily due to heavy reliance on extractive industries and fishing, as well as, to a lesser extent, agriculture and livestock. The prospect of exploiting new resources such as oil and gas in 2024 adds to this dynamic.

The demand side, national economic activity is primarily supported by consumption, exports, and investments. Indeed, in 2023, exports and national imports accounted for 10.2% and 3.3% of GDP, respectively, while investments constituted 32.1% of GDP. However, the current account balance is generally in deficit due to external dependency, particularly the worsening trade deficit. Fluctuations in the current account generate macroeconomic and financial instability, necessitating adjustments to the exchange rate.

<figure><img src="image_1.png" /><figcaption>**Figure 1 : Évolutions du taux de change et du taux d'inflation en Mauritanie (2016-2023)**</figcaption></figure>

*Source : Author based on data from the IMF and the Central Bank of Mauritania*

The graph in Figure 1 illustrates that both the nominal effective exchange rate (NEER) and the real effective exchange rate (REER) follow the same trend as the price index. An increase in NEER and REER indicates depreciation. Conversely, a decrease in USD_MRU and EURO_MRU signifies a bilateral depreciation of the local currency. Figure 1 depicts episodes of exchange rate depreciation tending to coincide with periods of inflationary pressure, notably during the period from 2020 to 2021 (table 1). During this time, the Central Bank of Mauritania (BCM) actively adjusted its policy rate to steer monetary policy, with successive decreases until the end of 2021.

In 2022, the graph indicates an increase in inflation due to intensified inflationary pressures caused by rising prices of energy and food products in international markets. The national consumer price index thus recorded an average increase of 9.6% over 12 months and 11% year-on-year in 2022, compared to respective declines of 4.9% and 1.6% in 2023. In response to this inflation, the BCM strengthened its cautious stance in the face of escalating inflationary pressures and risks related to the persistence of
tensions in prices of imported basic goods. It decided to raise the central bank's policy rate, first by 200 basis points in August 2022, and then a second time by 100 basis points in December 2022, thereby bringing it successively to 7% and 8%. In July 2022, the depreciation accelerated somewhat. The bilateral exchange rate against the euro followed the evolution of the dollar exchange rate.

<table><caption>Table 1: Correlation between exchange rates and inflation in Mauritania</caption><thead><tr><th></th><th>Inflation, average</th><th>Inflation, end of period</th><th>Inflation on food products</th><th>NEER</th><th>REER</th><th>USD_MRU</th><th>EURO_MRU</th></tr></thead><tbody><tr><td>Inflation, average</td><td>1</td><td>0.73</td><td>0.99</td><td>0.88</td><td>0.96</td><td>0.06</td><td>-0.63</td></tr><tr><td>Inflation, end of period</td><td>0.73</td><td>1</td><td>0.8</td><td>0.69</td><td>0.76</td><td>0.04</td><td>-0.66</td></tr><tr><td>Inflation on food products</td><td>0.99</td><td>0.8</td><td>1</td><td>0.89</td><td>0.96</td><td>-0.02</td><td>-0.68</td></tr><tr><td>NEER</td><td>0.88</td><td>0.69</td><td>0.89</td><td>1</td><td>0.95</td><td>-0.14</td><td>-0.86</td></tr><tr><td>REER</td><td>0.96</td><td>0.76</td><td>0.96</td><td>0.95</td><td>1</td><td>0.05</td><td>-0.74</td></tr><tr><td>USD_MRU</td><td>0.06</td><td>0.04</td><td>-0.02</td><td>-0.14</td><td>0.05</td><td>1</td><td>0.14</td></tr><tr><td>EURO_MRU</td><td>-0.63</td><td>-0.66</td><td>-0.68</td><td>-0.86</td><td>-0.74</td><td>0.14</td><td>1</td></tr></tbody></table>

Source: Author

## Empirical Approach

The empirical specification adopted in this study is based on a reduced-form equation commonly used for the empirical analysis of the extent of exchange rate pass-through to domestic prices. The appropriate econometric methodology would involve estimating an Error Correction Model (ECM), which belongs to the family of models associated with non-stationary but cointegrated time series. An alternative approach would be the Vector Error Correction Model (VECM), which has the property of determining the long- and short-term relationships between variables. The VECM is a vector autoregressive model (VAR) comprising stationary variables and error correction equations with non-stationary level variables.

However, in cases where the inflation variable and the exchange rate are cointegrated, dynamic lag regressions might be misspecified. To account for cointegration, priority would be given to a Vector Error Correction Model (VECM). Burstein and Gopinath (2014) provide a formulation of a VECM with estimation using maximum likelihood method, described as follows: Consider a $VECM(p)$ for $Y_t$ as a vector of integrated variables of order $n$:

$$ \Delta Y_t = \alpha + \beta Y_{t-1} + \sum_{j=1}^{p-1} \gamma_j \Delta Y_{t-j} + \Theta E_t + \epsilon_t \quad (1) $$

Where $\Delta Y_t$ is the first difference of $Y_t$; $\alpha$ is the constant term; $\beta$ is a matrix of long-term coefficient of the cointegration relation; $\gamma_j$ is a matrix of short-term coefficient of $\Delta Y_{t-j}$; $\Theta$ is a matrix of coefficients of a vector of deterministic terms $E_t$, and $\epsilon_t$ is the error term. Indeed, the estimation of the VECM model begins with a test for the existence of cointegration relationship between the variables, using Johansen's (1991) maximum likelihood method. This process decomposes into three distinct steps. The first step involves estimating VAR(P) models incorporating our variables with different orders (p). Next, the optimal order is determined by minimizing the Akaike and Schwarz criteria. The second step involves applying the Johansen likelihood ratio test to determine the number of cointegration relationships. Finally, in the third step, the VECM model is estimated, taking into account the cointegration relationships identified earlier. This three-step approach allows for the evaluation and confirmation of the presence of cointegration relationships between the variables before proceeding with the estimation of the VECM model.

Given the nature of the exchange rate regime in Mauritania and the findings of the empirical literature, we have identified several internal and external determinants of the Consumer Price
tensions in prices of imported basic goods. It decided to raise the central bank's policy rate, first by 200 basis points in August 2022, and then a second time by 100 basis points in December 2022, thereby bringing it successively to 7% and 8%. In July 2022, the depreciation accelerated somewhat. The bilateral exchange rate against the euro followed the evolution of the dollar exchange rate.

<table><caption>Table 1: Correlation between exchange rates and inflation in Mauritania</caption><thead><tr><th></th><th>Inflation, average</th><th>Inflation, end of period</th><th>Inflation on food products</th><th>NEER</th><th>REER</th><th>USD_MRU</th><th>EURO_MRU</th></tr></thead><tbody><tr><td>Inflation, average</td><td>1</td><td>0.73</td><td>0.99</td><td>0.88</td><td>0.96</td><td>0.06</td><td>-0.63</td></tr><tr><td>Inflation, end of period</td><td>0.73</td><td>1</td><td>0.8</td><td>0.69</td><td>0.76</td><td>0.04</td><td>-0.66</td></tr><tr><td>Inflation on food products</td><td>0.99</td><td>0.8</td><td>1</td><td>0.89</td><td>0.96</td><td>-0.02</td><td>-0.68</td></tr><tr><td>NEER</td><td>0.88</td><td>0.69</td><td>0.89</td><td>1</td><td>0.95</td><td>-0.14</td><td>-0.86</td></tr><tr><td>REER</td><td>0.96</td><td>0.76</td><td>0.96</td><td>0.95</td><td>1</td><td>0.05</td><td>-0.74</td></tr><tr><td>USD_MRU</td><td>0.06</td><td>0.04</td><td>-0.02</td><td>-0.14</td><td>0.05</td><td>1</td><td>0.14</td></tr><tr><td>EURO_MRU</td><td>-0.63</td><td>-0.66</td><td>-0.68</td><td>-0.86</td><td>-0.74</td><td>0.14</td><td>1</td></tr></tbody></table>

Source: Author

## Empirical Approach

The empirical specification adopted in this study is based on a reduced-form equation commonly used for the empirical analysis of the extent of exchange rate pass-through to domestic prices. The appropriate econometric methodology would involve estimating an Error Correction Model (ECM), which belongs to the family of models associated with non-stationary but cointegrated time series. An alternative approach would be the Vector Error Correction Model (VECM), which has the property of determining the long- and short-term relationships between variables. The VECM is a vector autoregressive model (VAR) comprising stationary variables and error correction equations with non-stationary level variables.

However, in cases where the inflation variable and the exchange rate are cointegrated, dynamic lag regressions might be misspecified. To account for cointegration, priority would be given to a Vector Error Correction Model (VECM). Burstein and Gopinath (2014) provide a formulation of a VECM with estimation using maximum likelihood method, described as follows: Consider a $VECM(p)$ for $Y_t$ as a vector of integrated variables of order $n$:

$$ \Delta Y_t = \alpha + \beta Y_{t-1} + \sum_{j=1}^{p-1} \gamma_j \Delta Y_{t-j} + \Theta E_t + \epsilon_t \quad (1) $$

Where $\Delta Y_t$ is the first difference of $Y_t$; $\alpha$ is the constant term; $\beta$ is a matrix of long-term coefficient of the cointegration relation; $\gamma_j$ is a matrix of short-term coefficient of $\Delta Y_{t-j}$; $\Theta$ is a matrix of coefficients of a vector of deterministic terms $E_t$, and $\epsilon_t$ is the error term. Indeed, the estimation of the VECM model begins with a test for the existence of cointegration relationship between the variables, using Johansen's (1991) maximum likelihood method. This process decomposes into three distinct steps. The first step involves estimating VAR(P) models incorporating our variables with different orders (p). Next, the optimal order is determined by minimizing the Akaike and Schwarz criteria. The second step involves applying the Johansen likelihood ratio test to determine the number of cointegration relationships. Finally, in the third step, the VECM model is estimated, taking into account the cointegration relationships identified earlier. This three-step approach allows for the evaluation and confirmation of the presence of cointegration relationships between the variables before proceeding with the estimation of the VECM model.

Given the nature of the exchange rate regime in Mauritania and the findings of the empirical literature, we have identified several internal and external determinants of the Consumer Price
Index (CPI). Among the internal determinants, we have included the Nominal Effective Exchange Rate (NEER) and the Money Supply. For external determinants, we have considered the prices of food commodities in the international market (FPI), as well as the prices of gold, crude oil (Brent), copper, and iron ore internationally. Additionally, to reduce variance effects and minimize temporal influence on the series, all variables were subjected to logarithmic transformation. This method also aims to simplify the process of stationary series while preserving the integrity of the initial values. To capture seasonal variations, seasonal nominal variables in the form of dummy variables have been incorporated into the model. The specification of our model is as follows:

$$ 
\Delta \ln \text{INPC}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{NEER} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{NEER}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Gold} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (2)
$$

Where $\beta_0$ represents the constant term; $\alpha_1$ and $\alpha_2$ represent the long-term elasticities; $\rho$ is the error correction coefficient; $\beta_i$, $\gamma_i$ et $\varphi_i$ correspond to the short-term elasticities of the explanatory variables; and $\delta_1$, $\delta_2$, $\delta_3$, $\delta_4$ and $\theta_i$ are the exogenous variables of the model.

Furthermore, given that the level of exchange rate pass-through to prices could be affected by the dynamics of volatile price products as well as the exchange rate measure, we have expanded the previous VECM model into two additional variants. Consistent with the literature, these variants include either the consumer price index for the component of food and non-alcoholic beverages, which represents 50.3% of the household basket in the calculation of the CPI in Mauritania. We introduce either the bilateral exchange rates of the Ouguiya (MRU) against the currencies in the anchor basket as explanatory variables instead of the NEER, according to the following schemes:

$$ 
\Delta \ln \text{INPC\_PA}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{NEER} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{NEER}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Copper} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (3)
$$

Where $\ln \text{INPC\_PA}_t$ represents the natural logarithm of the index of food and non-alcoholic beverages.

$$ 
\Delta \ln \text{INPC\_PA}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{USD/MRU} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{USD/MRU}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Gold} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (4)
$$

$$ 
\Delta \ln \text{INPC\_PA}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{EURO/MRU} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{EURO/MRU}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Gold} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (5)
$$

Where $\ln \text{USD/MRU}$ represents the natural logarithm of the bilateral exchange rate of the Ouguiya with respect to the US dollar, and $\ln \text{EURO/MRU}$ represents the natural logarithm of the bilateral exchange rate of the Ouguiya with respect to the Euro.

Subsequently, we will employ the local projection method developed by Jorda (2005) to robustly assess the impact of exchange rate fluctuations on inflation. Jorda (2005) introduced an alternative method to compute impulse response functions (IRFs) based on local projections that do not require the specification and estimation of the unknown multivariate dynamic
Index (CPI). Among the internal determinants, we have included the Nominal Effective Exchange Rate (NEER) and the Money Supply. For external determinants, we have considered the prices of food commodities in the international market (FPI), as well as the prices of gold, crude oil (Brent), copper, and iron ore internationally. Additionally, to reduce variance effects and minimize temporal influence on the series, all variables were subjected to logarithmic transformation. This method also aims to simplify the process of stationary series while preserving the integrity of the initial values. To capture seasonal variations, seasonal nominal variables in the form of dummy variables have been incorporated into the model. The specification of our model is as follows:

$$ 
\Delta \ln \text{INPC}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{NEER} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{NEER}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Gold} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (2)
$$

Where $\beta_0$ represents the constant term; $\alpha_1$ and $\alpha_2$ represent the long-term elasticities; $\rho$ is the error correction coefficient; $\beta_i$, $\gamma_i$ et $\varphi_i$ correspond to the short-term elasticities of the explanatory variables; and $\delta_1$, $\delta_2$, $\delta_3$, $\delta_4$ and $\theta_i$ are the exogenous variables of the model.

Furthermore, given that the level of exchange rate pass-through to prices could be affected by the dynamics of volatile price products as well as the exchange rate measure, we have expanded the previous VECM model into two additional variants. Consistent with the literature, these variants include either the consumer price index for the component of food and non-alcoholic beverages, which represents 50.3% of the household basket in the calculation of the CPI in Mauritania. We introduce either the bilateral exchange rates of the Ouguiya (MRU) against the currencies in the anchor basket as explanatory variables instead of the NEER, according to the following schemes:

$$ 
\Delta \ln \text{INPC\_PA}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{NEER} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{NEER}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Copper} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (3)
$$

Where $\ln \text{INPC\_PA}_t$ represents the natural logarithm of the index of food and non-alcoholic beverages.

$$ 
\Delta \ln \text{INPC\_PA}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{USD/MRU} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{USD/MRU}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Gold} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (4)
$$

$$ 
\Delta \ln \text{INPC\_PA}_t = \beta_0 + \alpha_1 \ln \text{M2} + \alpha_2 \ln \text{EURO/MRU} + \rho \text{COINT} + \sum_{i=1}^{p} \beta_i \Delta \ln \text{INPC}_{t-i} + \\ 
\sum_{i=0}^{q} \gamma_i \Delta \ln \text{EURO/MRU}_{t-i} + \sum_{i=0}^{q} \varphi_i \Delta \ln \text{M2}_{t-i} + \delta_1 \ln \text{FPI} + \delta_2 \ln \text{Gold} + \delta_3 \ln \text{Brent} + \\ 
\delta_4 \ln \text{IRON} + \sum_{i=1}^{r} \theta_i \text{Dummy} + \epsilon_t \quad (5)
$$

Where $\ln \text{USD/MRU}$ represents the natural logarithm of the bilateral exchange rate of the Ouguiya with respect to the US dollar, and $\ln \text{EURO/MRU}$ represents the natural logarithm of the bilateral exchange rate of the Ouguiya with respect to the Euro.

Subsequently, we will employ the local projection method developed by Jorda (2005) to robustly assess the impact of exchange rate fluctuations on inflation. Jorda (2005) introduced an alternative method to compute impulse response functions (IRFs) based on local projections that do not require the specification and estimation of the unknown multivariate dynamic
system itself. This approach offers the advantage of flexibility as it can easily incorporate non-linear phenomena or dependent effects, which are often challenging for classical vector autoregressive models (Auerbach and Gorodnichenko, 2013). It is sufficiently flexible to robustly handle endogeneity issues, particularly when the shock variable – in this case, the exchange rate – is not necessarily exogenous.

## Data

The study relies on the analysis of monthly data covering the period from January 2015 to December 2023, during which seven macroeconomic variables were selected for estimating the degree of exchange rate pass-through to prices within Mauritania. Various data sources were utilized, including ANSADE for the Consumer Price Index (CPI) and the index of prices on food products (INPC_PA), the money supply, the nominal and real effective exchange rates (NEER/REER), as well as the average monthly exchange rates in Ouguiya with respect to the US dollar (USD/MRU) and the Euro (EUR/MRU), sourced from the database of the Central Bank of Mauritania. Indeed, the money supply (M2) will be integrated into the model to reflect the monetary policy response to the Consumer Price Index, in reaction to potential significant fluctuations in the exchange rate. Additionally, commodity price indices such as gold, iron, Brent, and copper were extracted from the World Bank's Commodity Markets data. As for the Food Price Index (FPI), it was obtained from the FAO database. Descriptive statistics of these variables are presented in the table 2.

<table><caption>Table 2: Variable descriptions of the model</caption><thead><tr><th></th><th>Mean</th><th>Maximum</th><th>Minimum</th><th>Std. Dev.</th><th>Observations</th></tr></thead><tbody><tr><td>INPC</td><td>102.6</td><td>123.4</td><td>89.6</td><td>10.1</td><td>108</td></tr><tr><td>INPC_PA</td><td>104.1</td><td>133.6</td><td>86.4</td><td>14.5</td><td>108</td></tr><tr><td>M2</td><td>84898.7</td><td>133884.1</td><td>49924.3</td><td>25768.7</td><td>108</td></tr><tr><td>NEER</td><td>86.3</td><td>98.9</td><td>80.3</td><td>4.5</td><td>108</td></tr><tr><td>REER</td><td>100.1</td><td>121.1</td><td>90.8</td><td>7.2</td><td>108</td></tr><tr><td>USD_MRU</td><td>35.9</td><td>39.6</td><td>31.3</td><td>1.6</td><td>108</td></tr><tr><td>EURO_MRU</td><td>40.3</td><td>44.3</td><td>34.4</td><td>2.4</td><td>108</td></tr><tr><td>FPI</td><td>107.3</td><td>159.7</td><td>84.9</td><td>18.5</td><td>108</td></tr><tr><td>IRON</td><td>95.8</td><td>214.4</td><td>40.5</td><td>37.8</td><td>108</td></tr><tr><td>Brent</td><td>64.6</td><td>120.1</td><td>23.3</td><td>19.4</td><td>108</td></tr><tr><td>Gold</td><td>1515.8</td><td>2026.2</td><td>1075.7</td><td>299.1</td><td>108</td></tr><tr><td>Copper</td><td>6876.9</td><td>10230.9</td><td>4471.8</td><td>1605.3</td><td>108</td></tr></tbody></table>

Source: Author

## Results

The results have shown that the variables are non-stationary at level (Appendix Table A.1), and the optimal number of lags retained is 1 (Appendix Table A.2), according to the Akaike Information Criterion (AIC), but Jorda's method (2005) suggests 4 lags. This number indicates that time plays a predominant role in explaining the exchange rate pass-through phenomenon. The study also tested the cointegration relationship between the variables. The test results show that the series are cointegrated since the calculated F statistic exceeds the upper limit of critical values at all significance levels (Appendix Table A.3). The study then estimated the VECM model to obtain the coefficients of short and long-term transmission. Table 4 presents the results for the different equations. These results indicate that the error correction coefficient of the INPC variable is negative and significantly different from zero (Appendix Table A.7). All equations in the study were subjected to usual diagnostic tests such as residual tests normality (Appendix Table A.4), autocorrelation (Appendix Table A.5), and heteroskedasticity (Appendix Table A.6). Overall, the model errors proved to be normal, uncorrelated, and homoskedastic for the EURO/MRU exchange rate.

Table 3 illustrates the results of the impact of ERPT on inflation as follows: NEER (Nominal Effective Exchange Rate); columns (2): Dependent variable: Consumer Price Index (CPI) for food products;
system itself. This approach offers the advantage of flexibility as it can easily incorporate non-linear phenomena or dependent effects, which are often challenging for classical vector autoregressive models (Auerbach and Gorodnichenko, 2013). It is sufficiently flexible to robustly handle endogeneity issues, particularly when the shock variable – in this case, the exchange rate – is not necessarily exogenous.

## Data

The study relies on the analysis of monthly data covering the period from January 2015 to December 2023, during which seven macroeconomic variables were selected for estimating the degree of exchange rate pass-through to prices within Mauritania. Various data sources were utilized, including ANSADE for the Consumer Price Index (CPI) and the index of prices on food products (INPC_PA), the money supply, the nominal and real effective exchange rates (NEER/REER), as well as the average monthly exchange rates in Ouguiya with respect to the US dollar (USD/MRU) and the Euro (EUR/MRU), sourced from the database of the Central Bank of Mauritania. Indeed, the money supply (M2) will be integrated into the model to reflect the monetary policy response to the Consumer Price Index, in reaction to potential significant fluctuations in the exchange rate. Additionally, commodity price indices such as gold, iron, Brent, and copper were extracted from the World Bank's Commodity Markets data. As for the Food Price Index (FPI), it was obtained from the FAO database. Descriptive statistics of these variables are presented in the table 2.

<table><caption>Table 2: Variable descriptions of the model</caption><thead><tr><th></th><th>Mean</th><th>Maximum</th><th>Minimum</th><th>Std. Dev.</th><th>Observations</th></tr></thead><tbody><tr><td>INPC</td><td>102.6</td><td>123.4</td><td>89.6</td><td>10.1</td><td>108</td></tr><tr><td>INPC_PA</td><td>104.1</td><td>133.6</td><td>86.4</td><td>14.5</td><td>108</td></tr><tr><td>M2</td><td>84898.7</td><td>133884.1</td><td>49924.3</td><td>25768.7</td><td>108</td></tr><tr><td>NEER</td><td>86.3</td><td>98.9</td><td>80.3</td><td>4.5</td><td>108</td></tr><tr><td>REER</td><td>100.1</td><td>121.1</td><td>90.8</td><td>7.2</td><td>108</td></tr><tr><td>USD_MRU</td><td>35.9</td><td>39.6</td><td>31.3</td><td>1.6</td><td>108</td></tr><tr><td>EURO_MRU</td><td>40.3</td><td>44.3</td><td>34.4</td><td>2.4</td><td>108</td></tr><tr><td>FPI</td><td>107.3</td><td>159.7</td><td>84.9</td><td>18.5</td><td>108</td></tr><tr><td>IRON</td><td>95.8</td><td>214.4</td><td>40.5</td><td>37.8</td><td>108</td></tr><tr><td>Brent</td><td>64.6</td><td>120.1</td><td>23.3</td><td>19.4</td><td>108</td></tr><tr><td>Gold</td><td>1515.8</td><td>2026.2</td><td>1075.7</td><td>299.1</td><td>108</td></tr><tr><td>Copper</td><td>6876.9</td><td>10230.9</td><td>4471.8</td><td>1605.3</td><td>108</td></tr></tbody></table>

Source: Author

## Results

The results have shown that the variables are non-stationary at level (Appendix Table A.1), and the optimal number of lags retained is 1 (Appendix Table A.2), according to the Akaike Information Criterion (AIC), but Jorda's method (2005) suggests 4 lags. This number indicates that time plays a predominant role in explaining the exchange rate pass-through phenomenon. The study also tested the cointegration relationship between the variables. The test results show that the series are cointegrated since the calculated F statistic exceeds the upper limit of critical values at all significance levels (Appendix Table A.3). The study then estimated the VECM model to obtain the coefficients of short and long-term transmission. Table 4 presents the results for the different equations. These results indicate that the error correction coefficient of the INPC variable is negative and significantly different from zero (Appendix Table A.7). All equations in the study were subjected to usual diagnostic tests such as residual tests normality (Appendix Table A.4), autocorrelation (Appendix Table A.5), and heteroskedasticity (Appendix Table A.6). Overall, the model errors proved to be normal, uncorrelated, and homoskedastic for the EURO/MRU exchange rate.

Table 3 illustrates the results of the impact of ERPT on inflation as follows: NEER (Nominal Effective Exchange Rate); columns (2): Dependent variable: Consumer Price Index (CPI) for food products;
columns (3): MRU exchange rate (Mauritanian Ouguiya) against the US dollar (USD); and columns (4): MRU exchange rate (Mauritanian Ouguiya) against the Euro. Overall, the estimations indicate that the exchange rate pass-through to inflation in Mauritania is not statistically significant in the short term but holds significant importance in the long term.

The results also reveal that the effects of ERPT transmission are more pronounced with the MRU exchange rate against the US dollar (USD). This implies that a 1% depreciation of the ouguiya results in a 0.42% increase in the Consumer Price Index (CPI). The first column of the table indicates a long-term pass-through rate of 0.34%, the second column indicates an ERPT rate of 0.35%, and the fourth column indicates an ERPT rate of 0.20%. These findings are consistent with other research, such as that of Mariam et al. (2023), which also observed a lack of statistical significance for instantaneous transmission but emphasized significant long-term importance with a coefficient of 0.46 in Mauritania.

<table><caption>Table 3: Results of VECM Model Estimations</caption><thead><tr><th>Variables</th><th>Modèle (1) NEER</th><th>Modèle (2) INPC_P_A</th><th>Modèle (3) USD_MRU</th><th>Modèle (4) EURO_MRU</th></tr></thead><tbody><tr><td>NEER</td><td>0.344736***<br>[2.11452]</td><td>0.350<br>[1.81338]</td><td></td><td></td></tr><tr><td>M2</td><td>-0.5624<br>[-6.27407]</td><td>-0.5381<br>[-6.56683]</td><td>-0.6072<br>[-6.44807]</td><td>-0.5218<br>[-8.59907]</td></tr><tr><td>USD_MRU</td><td></td><td></td><td>0.424<br>[3.69527]</td><td></td></tr><tr><td>EURO_MRU</td><td></td><td></td><td></td><td>0.200<br>[3.76126]</td></tr><tr><td>R 2/R2 adjusted</td><td>56.3</td><td>54.4</td><td>50.5</td><td>53.8</td></tr></tbody></table>

Source: Author, Standard deviations are in parentheses.

Regarding the short-term dynamics (Appendix Table A.7), the estimations show a lack of statistical significance for the exchange rate pass-through to inflation in Mauritania. Control variables, including seasonality, the price of iron, and gold, have demonstrated their significance in the short term. However, the variable for oil prices does not show significance, which could be attributed to the fuel price subsidy. As for the food price index variable, it proves to be significant due to the country's high dependence on food imports. The food price index alone represents more than 22% of Mauritania's total imports.

Regarding the impulse response function of the local projection method defined by Jordà (2005), the results show the cumulative percentage change in the CPI level over 26 months in advance, attributable to a 1% depreciation of the nominal effective exchange rate (NEER), along with the 95% confidence band. The graph in Appendix A.1 shows that the impact of the exchange rate shock on domestic prices is gradual (it took about fifteen months to reach its full effect). The numbers from the impulse response function tables show that the immediate effect of a one-standard deviation structural shock on the exchange rate is 0.035 (or 3.5%) with long-term persistence. This effect demonstrates the central bank's reaction through controlling the money supply, proceeding with a gradual adjustment. The figures indicate that the temporary and unexpected depreciation of the exchange rate was followed by a tightening of monetary policy, peaking at the fifteenth month. After that, the central bank loosened its response but in an unstable manner, with control over the money supply.
columns (3): MRU exchange rate (Mauritanian Ouguiya) against the US dollar (USD); and columns (4): MRU exchange rate (Mauritanian Ouguiya) against the Euro. Overall, the estimations indicate that the exchange rate pass-through to inflation in Mauritania is not statistically significant in the short term but holds significant importance in the long term.

The results also reveal that the effects of ERPT transmission are more pronounced with the MRU exchange rate against the US dollar (USD). This implies that a 1% depreciation of the ouguiya results in a 0.42% increase in the Consumer Price Index (CPI). The first column of the table indicates a long-term pass-through rate of 0.34%, the second column indicates an ERPT rate of 0.35%, and the fourth column indicates an ERPT rate of 0.20%. These findings are consistent with other research, such as that of Mariam et al. (2023), which also observed a lack of statistical significance for instantaneous transmission but emphasized significant long-term importance with a coefficient of 0.46 in Mauritania.

<table><caption>Table 3: Results of VECM Model Estimations</caption><thead><tr><th>Variables</th><th>Modèle (1) NEER</th><th>Modèle (2) INPC_P_A</th><th>Modèle (3) USD_MRU</th><th>Modèle (4) EURO_MRU</th></tr></thead><tbody><tr><td>NEER</td><td>0.344736***<br>[2.11452]</td><td>0.350<br>[1.81338]</td><td></td><td></td></tr><tr><td>M2</td><td>-0.5624<br>[-6.27407]</td><td>-0.5381<br>[-6.56683]</td><td>-0.6072<br>[-6.44807]</td><td>-0.5218<br>[-8.59907]</td></tr><tr><td>USD_MRU</td><td></td><td></td><td>0.424<br>[3.69527]</td><td></td></tr><tr><td>EURO_MRU</td><td></td><td></td><td></td><td>0.200<br>[3.76126]</td></tr><tr><td>R 2/R2 adjusted</td><td>56.3</td><td>54.4</td><td>50.5</td><td>53.8</td></tr></tbody></table>

Source: Author, Standard deviations are in parentheses.

Regarding the short-term dynamics (Appendix Table A.7), the estimations show a lack of statistical significance for the exchange rate pass-through to inflation in Mauritania. Control variables, including seasonality, the price of iron, and gold, have demonstrated their significance in the short term. However, the variable for oil prices does not show significance, which could be attributed to the fuel price subsidy. As for the food price index variable, it proves to be significant due to the country's high dependence on food imports. The food price index alone represents more than 22% of Mauritania's total imports.

Regarding the impulse response function of the local projection method defined by Jordà (2005), the results show the cumulative percentage change in the CPI level over 26 months in advance, attributable to a 1% depreciation of the nominal effective exchange rate (NEER), along with the 95% confidence band. The graph in Appendix A.1 shows that the impact of the exchange rate shock on domestic prices is gradual (it took about fifteen months to reach its full effect). The numbers from the impulse response function tables show that the immediate effect of a one-standard deviation structural shock on the exchange rate is 0.035 (or 3.5%) with long-term persistence. This effect demonstrates the central bank's reaction through controlling the money supply, proceeding with a gradual adjustment. The figures indicate that the temporary and unexpected depreciation of the exchange rate was followed by a tightening of monetary policy, peaking at the fifteenth month. After that, the central bank loosened its response but in an unstable manner, with control over the money supply.
<table><caption>Tableau 4 : Variance Decomposition of LOG(INPC)</caption><thead><tr><td>Période</td><td>S.E.</td><td>LOG(INPC)</td><td>LOG(M2)</td><td>LOG(NEER)</td></tr></thead><tbody><tr><td>1</td><td>0.00</td><td>88.31</td><td>10.37</td><td>1.32</td></tr><tr><td>2</td><td>0.01</td><td>83.28</td><td>14.09</td><td>2.63</td></tr><tr><td>3</td><td>0.01</td><td>80.05</td><td>16.17</td><td>3.78</td></tr><tr><td>4</td><td>0.01</td><td>81.01</td><td>14.15</td><td>4.84</td></tr><tr><td>5</td><td>0.01</td><td>82.31</td><td>12.23</td><td>5.47</td></tr><tr><td>6</td><td>0.01</td><td>82.96</td><td>11.11</td><td>5.93</td></tr><tr><td>7</td><td>0.01</td><td>82.46</td><td>11.06</td><td>6.48</td></tr><tr><td>8</td><td>0.01</td><td>80.54</td><td>12.26</td><td>7.19</td></tr><tr><td>9</td><td>0.01</td><td>77.40</td><td>14.56</td><td>8.05</td></tr><tr><td>10</td><td>0.01</td><td>73.27</td><td>17.71</td><td>9.02</td></tr></tbody></table>

Source : Author

In the Table 4, the variance decomposition revealed that exchange rate shocks modestly contributed to inflation volatility, while inflation was mainly caused by its own shocks, especially in the short and medium term. Specifically, volatility due to exchange rate shocks represented between 6% and 9% respectively from the sixth to the ninth month, while that due to inflation's own shocks accounted for approximately 82% to 77% over the same period. The variance decomposition also showed that monetary shocks contributed significantly to inflation volatility, accounting for between 16% and 17% respectively from the first to the tenth month.

## Conclusion

This study aimed to estimate the rate of transmission of exchange rate variations to prices in the long term in Mauritania. To do so, a VECM model was used on a sample of monthly data covering the period from January 2015 to December 2023. The results showed that the exchange rate, money supply, and consumer price index formed a long-term stationary relationship in Mauritania. Regarding short-term dynamics, the estimations showed a lack of statistical significance for the transmission of the exchange rate to inflation in Mauritania. Once again, these results are consistent with those already reported in the literature. Control variables, including seasonality, the price of iron, and gold, demonstrated their significance in the short term. However, the variable for oil prices did not show significance, which could be attributed to fuel price subsidies. As for the food price index variable, it proved to be significant due to the country's high dependence on food imports.

In conclusion, the dynamic elasticity of exchange rate pass-through and, consequently, inflation has proven to be persistent in Mauritania. This suggests that exchange rate fluctuations constitute a potentially significant source of inflation in the country. The policy implication is that monetary authorities must be vigilant about exchange rate fluctuations, take swift monetary policy measures, and focus on exchange interventions capable of curbing inflationary pressures from abroad. Furthermore, the pass-through of the exchange rate to inflation seems to stem from a heightened dependence on imports, particularly on basic commodities. Therefore, policymakers in the country must create an environment conducive to economic diversification and the promotion of domestic industries.
<table><caption>Tableau 4 : Variance Decomposition of LOG(INPC)</caption><thead><tr><td>Période</td><td>S.E.</td><td>LOG(INPC)</td><td>LOG(M2)</td><td>LOG(NEER)</td></tr></thead><tbody><tr><td>1</td><td>0.00</td><td>88.31</td><td>10.37</td><td>1.32</td></tr><tr><td>2</td><td>0.01</td><td>83.28</td><td>14.09</td><td>2.63</td></tr><tr><td>3</td><td>0.01</td><td>80.05</td><td>16.17</td><td>3.78</td></tr><tr><td>4</td><td>0.01</td><td>81.01</td><td>14.15</td><td>4.84</td></tr><tr><td>5</td><td>0.01</td><td>82.31</td><td>12.23</td><td>5.47</td></tr><tr><td>6</td><td>0.01</td><td>82.96</td><td>11.11</td><td>5.93</td></tr><tr><td>7</td><td>0.01</td><td>82.46</td><td>11.06</td><td>6.48</td></tr><tr><td>8</td><td>0.01</td><td>80.54</td><td>12.26</td><td>7.19</td></tr><tr><td>9</td><td>0.01</td><td>77.40</td><td>14.56</td><td>8.05</td></tr><tr><td>10</td><td>0.01</td><td>73.27</td><td>17.71</td><td>9.02</td></tr></tbody></table>

Source: Author

In the Table 4, the variance decomposition revealed that exchange rate shocks modestly contributed to inflation volatility, while inflation was mainly caused by its own shocks, especially in the short and medium term. Specifically, volatility due to exchange rate shocks represented between 6% and 9% respectively from the sixth to the ninth month, while that due to inflation's own shocks accounted for approximately 82% to 77% over the same period. The variance decomposition also showed that monetary shocks contributed significantly to inflation volatility, accounting for between 16% and 17% respectively from the first to the tenth month.

## Conclusion

This study aimed to estimate the rate of transmission of exchange rate variations to prices in the long term in Mauritania. To do so, a VECM model was used on a sample of monthly data covering the period from January 2015 to December 2023. The results showed that the exchange rate, money supply, and consumer price index formed a long-term stationary relationship in Mauritania. Regarding short-term dynamics, the estimations showed a lack of statistical significance for the transmission of the exchange rate to inflation in Mauritania. Once again, these results are consistent with those already reported in the literature. Control variables, including seasonality, the price of iron, and gold, demonstrated their significance in the short term. However, the variable for oil prices did not show significance, which could be attributed to fuel price subsidies. As for the food price index variable, it proved to be significant due to the country's high dependence on food imports.

In conclusion, the dynamic elasticity of exchange rate pass-through and, consequently, inflation has proven to be persistent in Mauritania. This suggests that exchange rate fluctuations constitute a potentially significant source of inflation in the country. The policy implication is that monetary authorities must be vigilant about exchange rate fluctuations, take swift monetary policy measures, and focus on exchange interventions capable of curbing inflationary pressures from abroad. Furthermore, the pass-through of the exchange rate to inflation seems to stem from a heightened dependence on imports, particularly on basic commodities. Therefore, policymakers in the country must create an environment conducive to economic diversification and the promotion of domestic industries.
## References

Adrian, T., D. Laxton, and M. Obstfeld (eds). 2018. "Advancing the Frontiers of Monetary Policy" Washington, DC: *International Monetary Fund*.

Akofio-Sowah, N. A. 2009. "Is there a link between exchange rate pass-through and the monetary regime: Evidence from Sub-Saharan Africa and Latin America". *International Advances in Economic Research*, 15:296–309.

Aron, J., G. Farrell, J. Muellbauer and P. Sinclair. 2014. "Exchange rate pass-through to import prices, and monetary policy in South Africa." *South African Reserve Bank Working Paper, WP/12/08*.

Auerbach, A., and Gorodnichenko, Y. (2013). "Output spillovers from fiscal policy ". *The American Economic Review*, 103(3), 141–146.

Barhoumi, K. 2006. "Differences in long-run ERPT into import prices in developing countries : An empirical investigation". *Economic Modelling*, 23: 926–951.

Burstein, A. and Gopinath, G. 2014. "Chapter 7 International Prices and Exchange Rates", Vol. 4 of *Handbook of International Economics*.

Campa, J. and L. Goldberg. 2002. "Exchange rate pass through into import prices: A macro or micro phenomenon?" *Federal Reserve Bank of New York (mimeo)*.

Engel, C. 2004. "On the relationship between pass-through and sticky nominal prices." *Hong Kong Institute for Monetary Research Working Paper 2004-11*.

Goldberg, P. and M. Knetter. 1997. "Goods prices and exchange rates: What have we learned?" *Journal of Economic Literature*, 35: 1243–1292.

Goldberg, P. and R. Hellerstein. 2008. "A structural approach to explaining incomplete exchange rate pass-through and pricing-to-market." *American Economic Review*, 98: 423–429.

Jorda, O. (2005). "Estimation and inference of impulse responses by local projections." *The American Economic Review*, 95(1): 161–182.

Mariam El Hamiani Khatat, Jarin Tasnim Nashin, Anta Ndoye et Eric M. Pondi. 2023. "Exchange Rate Transmission and Monetary Transmission ". *Report of IMF N° 23/74*.

Menon, J. 1995. "Exchange rate pass-through". *Journal of Economic Surveys*, 9: 197–231.

Mihaljek, D. and M. Klau. 2001. "A note on the pass-through from exchange rate and foreign price changes to inflation in selected emerging market economies." *BIS Papers*, 8: 69–81.

Mihaljek, D. and M. Klau. 2008. "Exchange rate pass-through in emerging market economies: What has changed and why"? *BIS Papers*, 35:103–130.

Obstfeld, M. 2002. "Exchange rates and adjustment: Perspectives from the new open-economy macro-models." *Keynote speech, Bank of Japan. Monetary and Economic Studies*, December 2002.

Razafimahefa, I.F. 2012. "Exchange rate pass-through in Sub-Saharan African economies and its determinants". Washington DC: IMF Working Paper WP/12/141.
## References

Adrian, T., D. Laxton, and M. Obstfeld (eds). 2018. "Advancing the Frontiers of Monetary Policy" Washington, DC: *International Monetary Fund*.

Akofio-Sowah, N. A. 2009. "Is there a link between exchange rate pass-through and the monetary regime: Evidence from Sub-Saharan Africa and Latin America". *International Advances in Economic Research*, 15:296–309.

Aron, J., G. Farrell, J. Muellbauer and P. Sinclair. 2014. "Exchange rate pass-through to import prices, and monetary policy in South Africa." *South African Reserve Bank Working Paper, WP/12/08*.

Auerbach, A., and Gorodnichenko, Y. (2013). "Output spillovers from fiscal policy ". *The American Economic Review*, 103(3), 141–146.

Barhoumi, K. 2006. "Differences in long-run ERPT into import prices in developing countries : An empirical investigation". *Economic Modelling*, 23: 926–951.

Burstein, A. and Gopinath, G. 2014. "Chapter 7 International Prices and Exchange Rates", Vol. 4 of *Handbook of International Economics*.

Campa, J. and L. Goldberg. 2002. "Exchange rate pass through into import prices: A macro or micro phenomenon?" *Federal Reserve Bank of New York (mimeo)*.

Engel, C. 2004. "On the relationship between pass-through and sticky nominal prices." *Hong Kong Institute for Monetary Research Working Paper 2004-11*.

Goldberg, P. and M. Knetter. 1997. "Goods prices and exchange rates: What have we learned?" *Journal of Economic Literature*, 35: 1243–1292.

Goldberg, P. and R. Hellerstein. 2008. "A structural approach to explaining incomplete exchange rate pass-through and pricing-to-market." *American Economic Review*, 98: 423–429.

Jorda, O. (2005). "Estimation and inference of impulse responses by local projections." *The American Economic Review*, 95(1): 161–182.

Mariam El Hamiani Khatat, Jarin Tasnim Nashin, Anta Ndoye et Eric M. Pondi. 2023. "Exchange Rate Transmission and Monetary Transmission ". *Report of IMF N° 23/74*.

Menon, J. 1995. "Exchange rate pass-through". *Journal of Economic Surveys*, 9: 197–231.

Mihaljek, D. and M. Klau. 2001. "A note on the pass-through from exchange rate and foreign price changes to inflation in selected emerging market economies." *BIS Papers*, 8: 69–81.

Mihaljek, D. and M. Klau. 2008. "Exchange rate pass-through in emerging market economies: What has changed and why"? *BIS Papers*, 35:103–130.

Obstfeld, M. 2002. "Exchange rates and adjustment: Perspectives from the new open-economy macro-models." *Keynote speech, Bank of Japan. Monetary and Economic Studies*, December 2002.

Razafimahefa, I.F. 2012. "Exchange rate pass-through in Sub-Saharan African economies and its determinants". Washington DC: IMF Working Paper WP/12/141.
# Appendix

<table><caption>## Appendix Table A.1: Results of the stationarity test for the series used in the study</caption><thead><tr><th>Level</th><th>INPC</th><th>INPC_PA</th><th>NEER</th><th>USD_MRU</th><th>Euro_MRU</th><th>M2</th><th>FPI</th><th>IRON</th><th>GOLD</th><th>BRENT</th><th>COPPER</th></tr></thead><tbody><tr><td>ADF test statistic</td><td>0.77</td><td>0.26</td><td>-2.45</td><td>-3.01</td><td>-2.38</td><td>2.15</td><td>-1.58</td><td>-2.16</td><td>-0.33</td><td>-2.68</td><td>-2.10</td></tr><tr><td>1% level</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td></tr><tr><td>5% level</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td></tr><tr><td>10% level</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td></tr><tr><td>Prob. *</td><td>0.99</td><td>0.98</td><td>0.13</td><td>0.13</td><td>0.15</td><td>1.00</td><td>0.49</td><td>0.22</td><td>0.92</td><td>0.08</td><td>0.24</td></tr><tr><th>Differences</th><th>INPC</th><th>INPC_PA</th><th>NEER</th><th>USD_MRU</th><th>Euro_MRU</th><th>M2</th><th>FPI</th><th>IRON</th><th>GOLD</th><th>BRENT</th><th>COPPER</th></tr><tr><td>ADF test statistic</td><td>-4.35</td><td>-4.32</td><td>-5.13</td><td>-3.41</td><td>-7.25</td><td>-8.15</td><td>-3.70</td><td>-6.69</td><td>-8.65</td><td>-7.12</td><td>-2.94</td></tr><tr><td>1% level</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td></tr><tr><td>5% level</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td></tr><tr><td>10% level</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td></tr><tr><td>Prob.*</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.04</td></tr></tbody></table>

<table><caption>## Appendix Table A.2: VAR Lag Order Selection Criteria</caption><thead><tr><th>Lag</th><th>LogL</th><th>LR</th><th>FPE</th><th>AIC</th><th>SC</th><th>HQ</th></tr></thead><tbody><tr><td>0</td><td>1244.558</td><td>NA</td><td>8.36e-16</td><td>-23.37738</td><td>-21.72028*</td><td>-22.70654</td></tr><tr><td>1</td><td>1273.312</td><td>46.12061*</td><td>6.56e-16*</td><td>-23.62994*</td><td>-21.55856</td><td>-22.79139*</td></tr><tr><td>2</td><td>1288.815</td><td>23.63766</td><td>6.73e-16</td><td>-23.62009</td><td>-21.13443</td><td>-22.61383</td></tr><tr><td>3</td><td>1303.021</td><td>20.53558</td><td>7.13e-16</td><td>-23.58457</td><td>-20.68463</td><td>-22.41059</td></tr><tr><td>4</td><td>1311.363</td><td>11.39872</td><td>8.56e-16</td><td>-23.43294</td><td>-20.11872</td><td>-22.09125</td></tr><tr><td>5</td><td>1329.406</td><td>23.22338</td><td>8.56e-16</td><td>-23.47339</td><td>-19.74490</td><td>-21.96399</td></tr><tr><td>6</td><td>1343.608</td><td>17.15524</td><td>9.35e-16</td><td>-23.43779</td><td>-19.29502</td><td>-21.76068</td></tr></tbody></table>

<caption>## Appendix Table A.3: Johansen cointegration test using the EViews software</caption>

<table><caption>### Unrestricted Cointegration Rank Test (Trace)</caption><thead><tr><th>Hypothesized<br>No. of CE(s)</th><th>Eigenvalue</th><th>Trace<br>Statistic</th><th>0.05<br>Critical Value</th><th>Prob.**<br>Critical Value</th></tr></thead><tbody><tr><td>None *</td><td>0.267508</td><td>48.73269</td><td>29.79707</td><td>0.0001</td></tr><tr><td>At most 1 *</td><td>0.125028</td><td>16.04596</td><td>15.49471</td><td>0.0413</td></tr><tr><td>At most 2</td><td>0.019071</td><td>2.021794</td><td>3.841465</td><td>0.1551</td></tr></tbody></table>

Trace test indicates 2 cointegrating equation(s) at the 0.05 level

<table><caption>### Unrestricted Cointegration Rank Test (Max-eigenvalue)</caption><thead><tr><th>Hypothesized<br>No. of CE(s)</th><th>Eigenvalue</th><th>Max-Eigen<br>Statistic</th><th>0.05<br>Critical<br>Value</th><th>Prob.**<br>Critical<br>Value</th></tr></thead><tbody><tr><td>None *</td><td>0.267508</td><td>32.68673</td><td>21.13162</td><td>0.0008</td></tr><tr><td>At most 1</td><td>0.125028</td><td>14.02416</td><td>14.26460</td><td>0.0545</td></tr><tr><td>At most 2</td><td>0.019071</td><td>2.021794</td><td>3.841465</td><td>0.1551</td></tr></tbody></table>

Max-eigenvalue test indicates 1 cointegrating equation(s) at the 0.05 level
# Appendix

<table><caption>## Appendix Table A.1: Results of the stationarity test for the series used in the study</caption><thead><tr><th>Level</th><th>INPC</th><th>INPC_PA</th><th>NEER</th><th>USD_MRU</th><th>Euro_MRU</th><th>M2</th><th>FPI</th><th>IRON</th><th>GOLD</th><th>BRENT</th><th>COPPER</th></tr></thead><tbody><tr><td>ADF test statistic</td><td>0.77</td><td>0.26</td><td>-2.45</td><td>-3.01</td><td>-2.38</td><td>2.15</td><td>-1.58</td><td>-2.16</td><td>-0.33</td><td>-2.68</td><td>-2.10</td></tr><tr><td>1% level</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td></tr><tr><td>5% level</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td></tr><tr><td>10% level</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td></tr><tr><td>Prob. *</td><td>0.99</td><td>0.98</td><td>0.13</td><td>0.13</td><td>0.15</td><td>1.00</td><td>0.49</td><td>0.22</td><td>0.92</td><td>0.08</td><td>0.24</td></tr><tr><th>Differences</th><th>INPC</th><th>INPC_PA</th><th>NEER</th><th>USD_MRU</th><th>Euro_MRU</th><th>M2</th><th>FPI</th><th>IRON</th><th>GOLD</th><th>BRENT</th><th>COPPER</th></tr><tr><td>ADF test statistic</td><td>-4.35</td><td>-4.32</td><td>-5.13</td><td>-3.41</td><td>-7.25</td><td>-8.15</td><td>-3.70</td><td>-6.69</td><td>-8.65</td><td>-7.12</td><td>-2.94</td></tr><tr><td>1% level</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td><td>-3.49</td></tr><tr><td>5% level</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td><td>-2.89</td></tr><tr><td>10% level</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td><td>-2.58</td></tr><tr><td>Prob.*</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.04</td></tr></tbody></table>

<table><caption>## Appendix Table A.2: VAR Lag Order Selection Criteria</caption><thead><tr><th>Lag</th><th>LogL</th><th>LR</th><th>FPE</th><th>AIC</th><th>SC</th><th>HQ</th></tr></thead><tbody><tr><td>0</td><td>1244.558</td><td>NA</td><td>8.36e-16</td><td>-23.37738</td><td>-21.72028*</td><td>-22.70654</td></tr><tr><td>1</td><td>1273.312</td><td>46.12061*</td><td>6.56e-16*</td><td>-23.62994*</td><td>-21.55856</td><td>-22.79139*</td></tr><tr><td>2</td><td>1288.815</td><td>23.63766</td><td>6.73e-16</td><td>-23.62009</td><td>-21.13443</td><td>-22.61383</td></tr><tr><td>3</td><td>1303.021</td><td>20.53558</td><td>7.13e-16</td><td>-23.58457</td><td>-20.68463</td><td>-22.41059</td></tr><tr><td>4</td><td>1311.363</td><td>11.39872</td><td>8.56e-16</td><td>-23.43294</td><td>-20.11872</td><td>-22.09125</td></tr><tr><td>5</td><td>1329.406</td><td>23.22338</td><td>8.56e-16</td><td>-23.47339</td><td>-19.74490</td><td>-21.96399</td></tr><tr><td>6</td><td>1343.608</td><td>17.15524</td><td>9.35e-16</td><td>-23.43779</td><td>-19.29502</td><td>-21.76068</td></tr></tbody></table>

<caption>## Appendix Table A.3: Johansen cointegration test using the EViews software</caption>

<table><caption>### Unrestricted Cointegration Rank Test (Trace)</caption><thead><tr><th>Hypothesized<br>No. of CE(s)</th><th>Eigenvalue</th><th>Trace<br>Statistic</th><th>0.05<br>Critical Value</th><th>Prob.**<br>Critical Value</th></tr></thead><tbody><tr><td>None *</td><td>0.267508</td><td>48.73269</td><td>29.79707</td><td>0.0001</td></tr><tr><td>At most 1 *</td><td>0.125028</td><td>16.04596</td><td>15.49471</td><td>0.0413</td></tr><tr><td>At most 2</td><td>0.019071</td><td>2.021794</td><td>3.841465</td><td>0.1551</td></tr></tbody></table>

Trace test indicates 2 cointegrating equation(s) at the 0.05 level

<table><caption>### Unrestricted Cointegration Rank Test (Max-eigenvalue)</caption><thead><tr><th>Hypothesized<br>No. of CE(s)</th><th>Eigenvalue</th><th>Max-Eigen<br>Statistic</th><th>0.05<br>Critical<br>Value</th><th>Prob.**<br>Critical<br>Value</th></tr></thead><tbody><tr><td>None *</td><td>0.267508</td><td>32.68673</td><td>21.13162</td><td>0.0008</td></tr><tr><td>At most 1</td><td>0.125028</td><td>14.02416</td><td>14.26460</td><td>0.0545</td></tr><tr><td>At most 2</td><td>0.019071</td><td>2.021794</td><td>3.841465</td><td>0.1551</td></tr></tbody></table>

Max-eigenvalue test indicates 1 cointegrating equation(s) at the 0.05 level
<table><caption>Appendix Table A.4 : Jargue-Bera Normality</caption><thead><tr><th>Component</th><th>Jarque-Bera</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>Equation 1</td><td>125.1716</td><td>6</td><td>0.0000</td></tr><tr><td>Equation 2</td><td>154.2350</td><td>6</td><td>0.0000</td></tr><tr><td>Equation 3</td><td>529.5339</td><td>6</td><td>0.0000</td></tr><tr><td>Equation 4</td><td>199.7138</td><td>6</td><td>0.0000</td></tr></tbody></table>

<caption>Appendix Table A.5 : Breusch-Godfrey Serial Correlation LM (4 lags) :</caption>

Equation 1

<table><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>13.65241</td><td>9</td><td>0.1352</td><td>1.541148</td><td>(9, 207.0)</td><td>0.1353</td></tr><tr><td>2</td><td>19.89707</td><td>18</td><td>0.3386</td><td>1.114075</td><td>(18, 232.4)</td><td>0.3392</td></tr><tr><td>3</td><td>32.60607</td><td>27</td><td>0.2104</td><td>1.225858</td><td>(27, 231.4)</td><td>0.2116</td></tr><tr><td>4</td><td>40.58085</td><td>36</td><td>0.2755</td><td>1.141044</td><td>(36, 225.3)</td><td>0.2783</td></tr></tbody></table>

Equation 2

<table><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>13.95435</td><td>9</td><td>0.1240</td><td>1.576374</td><td>(9, 207.0)</td><td>0.1240</td></tr><tr><td>2</td><td>23.25608</td><td>18</td><td>0.1809</td><td>1.311418</td><td>(18, 232.4)</td><td>0.1814</td></tr><tr><td>3</td><td>43.19495</td><td>27</td><td>0.0250</td><td>1.660534</td><td>(27, 231.4)</td><td>0.0253</td></tr><tr><td>4</td><td>50.07900</td><td>36</td><td>0.0596</td><td>1.436863</td><td>(36, 225.3)</td><td>0.0608</td></tr></tbody></table>

Equation 3

<table><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>29.72140</td><td>9</td><td>0.0005</td><td>3.485692</td><td>(9, 209.5)</td><td>0.0005</td></tr><tr><td>2</td><td>42.54184</td><td>18</td><td>0.0009</td><td>2.497762</td><td>(18, 235.2)</td><td>0.0009</td></tr><tr><td>3</td><td>54.97760</td><td>27</td><td>0.0012</td><td>2.165313</td><td>(27, 234.3)</td><td>0.0012</td></tr><tr><td>4</td><td>60.75460</td><td>36</td><td>0.0061</td><td>1.782308</td><td>(36, 228.2)</td><td>0.0063</td></tr></tbody></table>

Equation 4

<table><caption>Appendix Table A.6 : BreuschPagan-Godfrey Heteroskedasticity</caption><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>14.19980</td><td>9</td><td>0.1154</td><td>1.604732</td><td>(9, 209.5)</td><td>0.1155</td></tr><tr><td>2</td><td>20.74368</td><td>18</td><td>0.2925</td><td>1.163428</td><td>(18, 235.2)</td><td>0.2930</td></tr><tr><td>3</td><td>31.82364</td><td>27</td><td>0.2387</td><td>1.194303</td><td>(27, 234.3)</td><td>0.2399</td></tr><tr><td>4</td><td>39.69985</td><td>36</td><td>0.3086</td><td>1.114072</td><td>(36, 228.2)</td><td>0.3114</td></tr></tbody></table>

<table><thead><tr><th></th><th>Chi-sq</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>Equation 1</td><td>191.3468</td><td>150</td><td>0.0127</td></tr><tr><td>Equation 2</td><td>197.9872</td><td>150</td><td>0.0053</td></tr><tr><td>Equation 3</td><td>173.0345</td><td>150</td><td>0.0959</td></tr><tr><td>Equation 4</td><td>192.5233</td><td>150</td><td>0.0109</td></tr></tbody></table>
<table><caption>Appendix Table A.4 : Jargue-Bera Normality</caption><thead><tr><th>Component</th><th>Jarque-Bera</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>Equation 1</td><td>125.1716</td><td>6</td><td>0.0000</td></tr><tr><td>Equation 2</td><td>154.2350</td><td>6</td><td>0.0000</td></tr><tr><td>Equation 3</td><td>529.5339</td><td>6</td><td>0.0000</td></tr><tr><td>Equation 4</td><td>199.7138</td><td>6</td><td>0.0000</td></tr></tbody></table>

<caption>Appendix Table A.5 : Breusch-Godfrey Serial Correlation LM (4 lags) :</caption>

Equation 1

<table><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>13.65241</td><td>9</td><td>0.1352</td><td>1.541148</td><td>(9, 207.0)</td><td>0.1353</td></tr><tr><td>2</td><td>19.89707</td><td>18</td><td>0.3386</td><td>1.114075</td><td>(18, 232.4)</td><td>0.3392</td></tr><tr><td>3</td><td>32.60607</td><td>27</td><td>0.2104</td><td>1.225858</td><td>(27, 231.4)</td><td>0.2116</td></tr><tr><td>4</td><td>40.58085</td><td>36</td><td>0.2755</td><td>1.141044</td><td>(36, 225.3)</td><td>0.2783</td></tr></tbody></table>

Equation 2

<table><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>13.95435</td><td>9</td><td>0.1240</td><td>1.576374</td><td>(9, 207.0)</td><td>0.1240</td></tr><tr><td>2</td><td>23.25608</td><td>18</td><td>0.1809</td><td>1.311418</td><td>(18, 232.4)</td><td>0.1814</td></tr><tr><td>3</td><td>43.19495</td><td>27</td><td>0.0250</td><td>1.660534</td><td>(27, 231.4)</td><td>0.0253</td></tr><tr><td>4</td><td>50.07900</td><td>36</td><td>0.0596</td><td>1.436863</td><td>(36, 225.3)</td><td>0.0608</td></tr></tbody></table>

Equation 3

<table><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>29.72140</td><td>9</td><td>0.0005</td><td>3.485692</td><td>(9, 209.5)</td><td>0.0005</td></tr><tr><td>2</td><td>42.54184</td><td>18</td><td>0.0009</td><td>2.497762</td><td>(18, 235.2)</td><td>0.0009</td></tr><tr><td>3</td><td>54.97760</td><td>27</td><td>0.0012</td><td>2.165313</td><td>(27, 234.3)</td><td>0.0012</td></tr><tr><td>4</td><td>60.75460</td><td>36</td><td>0.0061</td><td>1.782308</td><td>(36, 228.2)</td><td>0.0063</td></tr></tbody></table>

Equation 4

<table><caption>Appendix Table A.6 : BreuschPagan-Godfrey Heteroskedasticity</caption><thead><tr><th>Lag</th><th>LRE* stat</th><th>df</th><th>Prob.</th><th>Rao F-stat</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>1</td><td>14.19980</td><td>9</td><td>0.1154</td><td>1.604732</td><td>(9, 209.5)</td><td>0.1155</td></tr><tr><td>2</td><td>20.74368</td><td>18</td><td>0.2925</td><td>1.163428</td><td>(18, 235.2)</td><td>0.2930</td></tr><tr><td>3</td><td>31.82364</td><td>27</td><td>0.2387</td><td>1.194303</td><td>(27, 234.3)</td><td>0.2399</td></tr><tr><td>4</td><td>39.69985</td><td>36</td><td>0.3086</td><td>1.114072</td><td>(36, 228.2)</td><td>0.3114</td></tr></tbody></table>

<table><thead><tr><th></th><th>Chi-sq</th><th>df</th><th>Prob.</th></tr></thead><tbody><tr><td>Equation 1</td><td>191.3468</td><td>150</td><td>0.0127</td></tr><tr><td>Equation 2</td><td>197.9872</td><td>150</td><td>0.0053</td></tr><tr><td>Equation 3</td><td>173.0345</td><td>150</td><td>0.0959</td></tr><tr><td>Equation 4</td><td>192.5233</td><td>150</td><td>0.0109</td></tr></tbody></table>
<table><caption>Appendix Table A.7: Error Correction and Short-Term Coefficients of the VECM Model</caption><thead><tr><th></th><th>Modèle<br>(1)<br>NEER</th><th>Modèle<br>(2)<br>INPC_P_A</th><th>Modèle<br>(3)<br>USD_MRU</th><th>Modèle<br>(4)<br>EURO_MRU</th></tr></thead><tbody><tr><td>COINTEQ1</td><td>-0.040702<br>[-3.61774]</td><td>-0.068512<br>[-4.60823]</td><td>-0.078974<br>[-3.78960]</td><td>-0.119184<br>[-4.73566]</td></tr><tr><td>D(LOG(INPC(-1)))</td><td>0.242210<br>[2.50517]</td><td>0.208584<br>[2.23096]</td><td>0.288553<br>[3.06661]</td><td>0.273192<br>[3.02270]</td></tr><tr><td>D(LOG(INPC(-2)))</td><td>0.034842<br>[0.37051]</td><td>-0.024860<br>[-0.27633]</td><td>0.047799<br>[0.50505]</td><td>0.059035<br>[0.66036]</td></tr><tr><td>D(LOG(M2(-1)))</td><td>-0.054894<br>[-2.75926]</td><td>-0.038430<br>[-1.26583]</td><td>-0.039643<br>[-1.22552]</td><td>-0.061854<br>[-1.92261]</td></tr><tr><td>D(LOG(M2(-2)))</td><td>-0.046843<br>[-2.59125]</td><td>-0.041633<br>[-1.42924]</td><td>-0.045154<br>[-1.47439]</td><td>-0.068316<br>[-2.25235]</td></tr><tr><td>D(LOG(NEER(-1)))</td><td>-0.028442<br>[-1.10890]</td><td>-0.030247<br>[-0.73503]</td><td>0.076355<br>[0.95528]</td><td>0.069955<br>[1.92838]</td></tr><tr><td>D(LOG(NEER(-2)))</td><td>-0.005445<br>[-0.21990]</td><td>0.020727<br>[0.50360]</td><td>0.002181<br>[0.02582]</td><td>0.032389<br>[0.92556]</td></tr><tr><td>C</td><td>0.040847<br>[1.44664]</td><td>0.042352<br>[0.95479]</td><td></td><td></td></tr><tr><td>D_6</td><td>0.002713<br>[2.14438]</td><td>0.001652<br>[0.78754]</td><td>0.001502<br>[0.69148]</td><td>0.001087<br>[0.51739]</td></tr><tr><td>D_7</td><td>0.004008<br>[3.12160]</td><td>0.006388<br>[3.05480]</td><td>0.006281<br>[2.89501]</td><td>0.005694<br>[2.69888]</td></tr><tr><td>D_8</td><td>0.006814<br>[5.13222]</td><td>0.009217<br>[4.20113]</td><td>0.008510<br>[3.61459]</td><td>0.008940<br>[4.05926]</td></tr><tr><td>LOG(FPI)</td><td>0.017773<br>[3.01663]</td><td>0.048359<br>[4.43715]</td><td>0.024229<br>[2.52872]</td><td>0.027190<br>[2.88966]</td></tr><tr><td>LOG(GOLD)</td><td>-0.011638<br>[-2.31323]</td><td>-0.010238<br>[-1.52574]</td><td>-0.016882<br>[-3.25102]</td><td>-0.020830<br>[-3.99333]</td></tr><tr><td>LOG(IRON)</td><td>-0.004954<br>[-2.14668]</td><td>-0.007071<br>[-1.72591]</td><td>-0.006556<br>[-1.90183]</td><td>-0.007201<br>[-2.34767]</td></tr><tr><td>LOG(BRENT)</td><td>-0.003254<br>[-1.56475]</td><td></td><td>-0.003146<br>[-0.91274]</td><td>-0.000186<br>[-0.05530]</td></tr><tr><td>LOG(COPPER)</td><td></td><td>-0.017857<br>[-2.46699]</td><td></td><td></td></tr><tr><td>R-squared</td><td>0.563020</td><td>0.543659</td><td>0.504532</td><td>0.537888</td></tr><tr><td>Adj. R-squared</td><td>0.495046</td><td>0.472672</td><td>0.433751</td><td>0.471872</td></tr><tr><td>Sum sq. resid</td><td>0.001117</td><td>0.003068</td><td>0.003331</td><td>0.003107</td></tr><tr><td>S.E. equation</td><td>0.003523</td><td>0.005839</td><td>0.006050</td><td>0.005843</td></tr><tr><td>F-statistic</td><td>8.282806</td><td>7.658634</td><td>7.128060</td><td>8.147833</td></tr><tr><td>Log likelihood</td><td>452.1972</td><td>399.1426</td><td>394.8238</td><td>398.4828</td></tr><tr><td>Akaike AIC</td><td>-8.327567</td><td>-7.317002</td><td>-7.253787</td><td>-7.323482</td></tr><tr><td>Schwarz SC</td><td>-7.948429</td><td>-6.937864</td><td>-6.899926</td><td>-6.969621</td></tr><tr><td>Mean dependent</td><td>0.002850</td><td>0.003885</td><td>0.003885</td><td>0.003885</td></tr><tr><td>S.D. dependent</td><td>0.004957</td><td>0.008041</td><td>0.008041</td><td>0.008041</td></tr></tbody></table>

Source: Author, Standard deviations are in parentheses.
<table><caption>Appendix Table A.7: Error Correction and Short-Term Coefficients of the VECM Model</caption><thead><tr><th></th><th>Modèle (1)<br>NEER</th><th>Modèle (2)<br>INPC_P_A</th><th>Modèle (3)<br>USD_MRU</th><th>Modèle (4)<br>EURO_MRU</th></tr></thead><tbody><tr><td>COINTEQ1</td><td>-0.040702<br>[-3.61774]</td><td>-0.068512<br>[-4.60823]</td><td>-0.078974<br>[-3.78960]</td><td>-0.119184<br>[-4.73566]</td></tr><tr><td>D(LOG(INPC(-1)))</td><td>0.242210<br>[2.50517]</td><td>0.208584<br>[2.23096]</td><td>0.288553<br>[3.06661]</td><td>0.273192<br>[3.02270]</td></tr><tr><td>D(LOG(INPC(-2)))</td><td>0.034842<br>[0.37051]</td><td>-0.024860<br>[-0.27633]</td><td>0.047799<br>[0.50505]</td><td>0.059035<br>[0.66036]</td></tr><tr><td>D(LOG(M2(-1)))</td><td>-0.054894<br>[-2.75926]</td><td>-0.038430<br>[-1.26583]</td><td>-0.039643<br>[-1.22552]</td><td>-0.061854<br>[-1.92261]</td></tr><tr><td>D(LOG(M2(-2)))</td><td>-0.046843<br>[-2.59125]</td><td>-0.041633<br>[-1.42924]</td><td>-0.045154<br>[-1.47439]</td><td>-0.068316<br>[-2.25235]</td></tr><tr><td>D(LOG(NEER(-1)))</td><td>-0.028442<br>[-1.10890]</td><td>-0.030247<br>[-0.73503]</td><td>0.076355<br>[0.95528]</td><td>0.069955<br>[1.92838]</td></tr><tr><td>D(LOG(NEER(-2)))</td><td>-0.005445<br>[-0.21990]</td><td>0.020727<br>[0.50360]</td><td>0.002181<br>[0.02582]</td><td>0.032389<br>[0.92556]</td></tr><tr><td>C</td><td>0.040847<br>[1.44664]</td><td>0.042352<br>[0.95479]</td><td></td><td></td></tr><tr><td>D_6</td><td>0.002713<br>[2.14438]</td><td>0.001652<br>[0.78754]</td><td>0.001502<br>[0.69148]</td><td>0.001087<br>[0.51739]</td></tr><tr><td>D_7</td><td>0.004008<br>[3.12160]</td><td>0.006388<br>[3.05480]</td><td>0.006281<br>[2.89501]</td><td>0.005694<br>[2.69888]</td></tr><tr><td>D_8</td><td>0.006814<br>[5.13222]</td><td>0.009217<br>[4.20113]</td><td>0.008510<br>[3.61459]</td><td>0.008940<br>[4.05926]</td></tr><tr><td>LOG(FPI)</td><td>0.017773<br>[3.01663]</td><td>0.048359<br>[4.43715]</td><td>0.024229<br>[2.52872]</td><td>0.027190<br>[2.88966]</td></tr><tr><td>LOG(GOLD)</td><td>-0.011638<br>[-2.31323]</td><td>-0.010238<br>[-1.52574]</td><td>-0.016882<br>[-3.25102]</td><td>-0.020830<br>[-3.99333]</td></tr><tr><td>LOG(IRON)</td><td>-0.004954<br>[-2.14668]</td><td>-0.007071<br>[-1.72591]</td><td>-0.006556<br>[-1.90183]</td><td>-0.007201<br>[-2.34767]</td></tr><tr><td>LOG(BRENT)</td><td>-0.003254<br>[-1.56475]</td><td></td><td>-0.003146<br>[-0.91274]</td><td>-0.000186<br>[-0.05530]</td></tr><tr><td>LOG(COPPER)</td><td></td><td>-0.017857<br>[-2.46699]</td><td></td><td></td></tr><tr><td>R-squared</td><td>0.563020</td><td>0.543659</td><td>0.504532</td><td>0.537888</td></tr><tr><td>Adj. R-squared</td><td>0.495046</td><td>0.472672</td><td>0.433751</td><td>0.471872</td></tr><tr><td>Sum sq. resid</td><td>0.001117</td><td>0.003068</td><td>0.003331</td><td>0.003107</td></tr><tr><td>S.E. equation</td><td>0.003523</td><td>0.005839</td><td>0.006050</td><td>0.005843</td></tr><tr><td>F-statistic</td><td>8.282806</td><td>7.658634</td><td>7.128060</td><td>8.147833</td></tr><tr><td>Log likelihood</td><td>452.1972</td><td>399.1426</td><td>394.8238</td><td>398.4828</td></tr><tr><td>Akaike AIC</td><td>-8.327567</td><td>-7.317002</td><td>-7.253787</td><td>-7.323482</td></tr><tr><td>Schwarz SC</td><td>-7.948429</td><td>-6.937864</td><td>-6.899926</td><td>-6.969621</td></tr><tr><td>Mean dependent</td><td>0.002850</td><td>0.003885</td><td>0.003885</td><td>0.003885</td></tr><tr><td>S.D. dependent</td><td>0.004957</td><td>0.008041</td><td>0.008041</td><td>0.008041</td></tr></tbody></table>

Source: Author, Standard deviations are in parentheses.
Appendix Graph A.1: Impulse Response Function using the Local Projection Method by Jorda (2005)

<figure><img src="image_3.png" /></figure>

Source: Author
Appendix Graph A.1: Impulse Response Function using the Local Projection Method by Jorda (2005)

<figure><img src="image_2.png" /></figure>

Source: Author