Blaise Gnimassoun

The importance of the exchange rate regime in limiting current account imbalances in sub-Saharan African countries *

Keywords: JEL Classification: F32, F33, C11 Current account imbalances, Exchange rate regime, Bayesian model averaging, Sub-Saharan Africa

One of the major current concerns of economic policy in developing countries is the choice of the appropriate exchange rate regime to consolidate and accelerate the pace of economic growth. This paper aims to investigate whether the choice of a country's exchange rate regime may affect current account imbalances for sub-Saharan African economies. To this end, we first use Bayesian model averaging (BMA) to address concerns about model uncertainty and identify the key determinants (fundamentals) of external balances. Then, estimating current account imbalances over the 1980-2012 period, we show that flexible exchange rate regimes are more effective in preventing such disequilibria. Consequently, candidates for membership of monetary unions should discuss widely the possible adjustment mechanisms before forming such unions; one potential measure being the sharing of external risks at regional level.

Introduction

The choice of exchange rate regime and its impact on macroeconomic performance is undoubtedly one of the most controversial topics in macroeconomic policy [START_REF] Levy-Yeyati | To float or to fix: evidence on the impact of exchange rate regimes on growth[END_REF]. If the debate on the relative merits and demerits of alternative exchange rate regimes is longstanding, it remains topical for developing countries, especially for sub-Saharan African countries that are seeking an appropriate regime to consolidate and strengthen their economic impetus [START_REF] Yagci | Choice of exchange rate regimes for developing countries[END_REF][START_REF] Masson | Currency unions in Africa: is the trade effect substantial enough to justify their formation[END_REF][START_REF] Bird | Exchange rate regimes in developing and emerging economies and the incidence of IMF programs[END_REF][START_REF] Qureshi | Hard or soft pegs? Choice of exchange rate regime and trade in Africa[END_REF]. Several internal and external factors justify the attention paid to this issue: the economic performances achieved in sub-Saharan Africa during the 2000s, as well as the unexplored potential economic growth, the gradual opening of some countries to international financial flows, the increasing global imbalances and their implications, the need to strengthen regional trade, etc. While the average economic growth of sub-Saharan Africa stagnated in the 1980s and 1990s (2.6% and 2.2% respectively), it has doubled in the 2000s to stand at 5.5%, with predictions that remain strong according to the International Monetary Fund (IMF). 1 With the aim of enhancing this economic trend in the long term and stimulating sub-regional markets, the enthusiasm for monetary unions is increasingly apparent in sub-Saharan Africa. 2 Several reasons could justify the formation of monetary unions or the choice of a fixed exchange rate regime for developing countries,3 namely the intensification of regional trade, the level of which remains low given the high specialization of these countries in commodity exports, the credibility of economic policy-mainly fiscal policy-the management of which is usually much more rigorous in countries with fixed exchange rate regimes, and the legibility of monetary policy that is favourable to foreign capital and investment. Contrasting with these arguments, it is well known that the adoption of such schemes involves the abandonment of the exchange rate as an instrument for adjusting external imbalances. 4 Regarding the case of the euro area, there is some evidence that external imbalances have increased since the introduction of the common currency (see for instance, [START_REF] Berger | The euro's effect on trade imbalances[END_REF][START_REF] Holmes | On the stationarity of current account deficits in the European Union[END_REF][START_REF] Proaño | Are the current account imbalances between EMU countries sustainable? Working Paper 90-2012[END_REF][START_REF] Körner | On the brink? Intra-euro area imbalances and the sustainability of foreign debt[END_REF]. The adjustment of these imbalances, which is still ongoing, has been particularly painful in economic terms (falling wages, rising unemployment, labour market reforms, popular protests) partly because member countries can no longer individually use the exchange rate as an adjustment instrument. In a context in which some sub-Saharan African countries are tempted by other monetary policies, especially other exchange rate policies, the question of the link between the exchange rate regime and external imbalances is thus particularly acute.

While the increase in global imbalances during the 2000s has renewed interest in the literature on sustainability and the adjustment of current accounts, little attention has been paid to the link between current account imbalances and the exchange rate regime. 5 In addition, to the best of our knowledge, no study has addressed the case of developing countries. The focus 1 See World Economic Outlook (WEO), April 2014. 2 In addition to the project of a monetary union for West Africa, five East African countries (Burundi, Kenya, Rwanda, Tanzania and Uganda) signed on November 30, 2013 a protocol agreement establishing a monetary union. The project of In addition to the monetary union project for West Africa, five East African countries (Burundi, Kenya, Rwanda, Tanzania and Uganda) signed a protocol agreement on 30 November 2013 establishing a monetary union. The West African project aims to merge the West African Monetary Zone (WAMZ), consisting of Gambia, Ghana, Guinea, Nigeria and Sierra Leone, with the West African Monetary Union (WAMU), including Benin, Burkina Faso, Guinea Bissau, Ivory Coast, Mali, Niger, Senegal and Togo. The WAMZ is a monetary union undergoing training, whereas the WAMU is an existing monetary union with the CFA franc as its currency. The economic bloc of the Southern African Development Community (SADC), consisting of Angola, Botswana, the Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Swaziland, South Africa, Tanzania, Zambia and Zimbabwe, also plans to form a monetary union. Some coordination of monetary and exchange rate policies already takes place between South Africa, Lesotho and Swaziland, although the relationship is closer to that of a currency board.

on industrialized countries is justified in part by the fact that their external imbalances also represent a challenge to the stability of the global economy. However, the case of developing economies-sub-Saharan African countries in particular-is also of great interest as external imbalances represent a challenge for their own development.

Within this context, marked by the debate on the choice of exchange rate regime in sub-Saharan Africa, this paper aims to examine the relationship between current account imbalances and the exchange rate regime, investigating whether the latter plays a significant role in limiting the former. This investigation is particularly interesting with regard to the growth and development of sub-Saharan African countries that depend heavily on the performance and stability of their open sector due to their strong reliance on export earnings.

Turning to methodological issues, data regarding current account imbalances deserve some comments. In the existing literature, external imbalances are usually viewed simply as current account deficits and surpluses. However, it is well known that relatively large deficits are natural in an intertemporal framework, typically when a country begins its development process, strengthening domestic investment by importing capital [START_REF] Sachs | The current account and macroeconomic adjustment in the 1970s[END_REF][START_REF] Obstfeld | The intertemporal approach to the current account[END_REF][START_REF] Chinn | Current account balances, financial development and institutions: assaying the world "saving glut[END_REF][START_REF] Bussière | Methodological advances in the assessment of equilibrium exchange rates[END_REF]. It is thus more appropriate to refer to external imbalances when the current account deviates from the level determined by its fundamentals as proposed by [START_REF] Lane | External adjustment and the global crisis[END_REF] and [START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF]. 6 In other words, instead of considering current account deficits and surpluses, we measure external imbalances as the difference between the observed current account and its medium-term estimated value. To this end, the first challenge we face is to identify the main determinants of current account for sub-Saharan African countries in the context of the absence of clear theoretical guidance and the presence of model uncertainty emerging from empirical studies.

To identify the main determinants of current account positions while addressing model uncertainty, we employ the Bayesian model averaging (BMA) technique. 7 This procedure allows us to avoid problems of degrees of freedom due to the potentially large number of determinants, especially for developing countries for which data are generally limited. Furthermore, its flexibility allows us to take into account not only the conventional current account fundamentals, but also other less well-known potential determinants that might be specific to the countries considered. In addition, this approach has the advantage of avoiding the arbitrary choice of a current account model and providing accurate estimates of the medium-term current account and thence external imbalances.

Our paper contributes to the literature in several ways. First, we address for the first time a topical and far-reaching issue in terms of economic policy for sub-Saharan African countries, namely the potential influence of the exchange rate regime on external imbalances. Second, 6 See also the IMF studies on the methodology of the assessment of the current account and exchange rate, including [START_REF] Isard | Exchange rate assessment: extensions of the macroeconomic balance approach[END_REF], [START_REF] Isard | Methodology for current account and exchange rate assessments[END_REF] and the IMF (2013). See also [START_REF] Bussière | Methodological advances in the assessment of equilibrium exchange rates[END_REF] for the European Central Bank.

7 Bayesian techniques are often used to choose a model of economic growth among millions of potential specifications derived from theory and empirical studies (see [START_REF] Sala-I-Martin | I just ran two million regressions[END_REF][START_REF] Fernandez | Model uncertainty in cross-country growth regressions[END_REF]Salai-Martin et al., 2004;among others). It was recently used for the first time in a study on the current account by [START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF].

we draw on the BMA approach to address model uncertainty and robustly identify the current account fundamentals without ignoring the structural characteristics of the countries under study. Third, we specifically account for the medium-term determinants of the current account position by estimating a relationship linking the current account to its fundamentals. This allows us to derive more reliable current account equilibrium values than those based on the usual Hodrick-Prescott filtered series and also go further than studies that simply consider the deficits and surpluses of current account as the external imbalances.

Our findings show that the current account position in sub-Saharan Africa is mainly determined by foreign direct investment (FDI), financial deepening, fiscal balance, initial net foreign assets, oil balance, migrants' remittances, official development assistance (ODA) and stage of development. Studying the link between current account imbalances and the exchange rate regime, we show that a flexible exchange rate regime is most effective in preventing absolute external imbalances.

The rest of the paper is organized as follows. Section 2 reviews the potential current account determinants. In Section 3, we briefly present the methodology and the results of the BMA approach. Section 4 is devoted to the assessment of external imbalances and their relationship with the exchange rate regime. Finally, Section 5 concludes the paper.

Potential current account determinants for Sub-Saharan African countries

The fundamentals presented in this section are derived from the predictions of theoretical models and also from previous empirical studies, in particular those related to medium-term determinants of current account [START_REF] Debelle | What determines the current account? A cross-sectional and panel approach[END_REF][START_REF] Calderon | Determinants of current account in developing countries[END_REF][START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF][START_REF] Lane | External adjustment and the global crisis[END_REF]. Whereas [START_REF] Debelle | What determines the current account? A cross-sectional and panel approach[END_REF] and [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF] focus on both developing and industrialized countries and employ cross-sectional and panel approaches, [START_REF] Calderon | Determinants of current account in developing countries[END_REF] consider only developing countries in a panel framework. In addition to the traditional determinants presented in these papers, we also propose other less known potential fundamentals, which are linked to specific factors related to the countries under study.

Macroeconomic policy stance, macroeconomic performance and life cycle

Fiscal balance. The link between current account and fiscal balance is generally shown to be positive, giving rise to the well-known "twin deficits hypothesis". This positive relationship is consistent with the predictions of several theoretical models. [START_REF] Blanchard | Debt, deficits and finite horizons[END_REF] finite-horizon model and the overlapping generation models (see [START_REF] Obstfeld | Foundations of international macroeconomics[END_REF] indicate that a deterioration in fiscal balance tends to have the same negative effect on the current account to the extent that it involves a redistribution of income from future to present generations. This relationship does not hold in the particular case of Ricardian equivalence in which private savings fully offset changes in public saving.

Degree of exchange rate flexibility and exchange rate policy. As the degree of exchange rate flexibility can affect the ability of an economy to respond to external shocks, one could argue that a flexible exchange rate regime is more likely to limit current account imbalances. According to [START_REF] Milesi-Ferretti | Current account sustainability[END_REF], the degree of exchange rate flexibility in response to external shocks can affect the ability of an economy to sustain current account deficits.

It is therefore more relevant to look at the relationship between current account imbalances and exchange rate regime, as we do in Section 4, rather than between current account and exchange rate regime. However, given that exchange rate policy may also affect the price competitiveness of an economy, the real effective exchange rate is usually employed to examine this competitiveness channel. As an appreciation in exchange rate corresponds to a loss of price competitiveness, a negative relationship is expected between the real effective exchange rate and current account position.

Trade integration. Trade openness is a macroeconomic policy choice that may impact the current account, a more open economy being more vulnerable to external shocks. This vulnerability is greater when trade flows are not diversified [START_REF] Milesi-Ferretti | Current account sustainability[END_REF]. In this respect, the degree of openness to international trade is often used as a proxy for barriers to trade. It could also be correlated with other factors that make a country attractive to foreign capital [START_REF] Bussière | Methodological advances in the assessment of equilibrium exchange rates[END_REF]. Thus, the sign associated with this variable can only be determined empirically.

Net foreign assets. The current account and net foreign assets are linked by definition as the current account is nothing else than the change in net foreign assets. However, the level of net foreign assets may affect the current account in opposite directions. On the one hand, we can expect a negative relationship to the extent that an economy with a high level of net foreign assets can afford to run trade deficits while remaining solvent. On the other hand, a positive relationship is likely to the extent that an improvement in the net foreign asset position leads to an increase in net foreign income flows, which improves the current account.

Economic growth. Economic growth is often identified as a potential determinant of current account. Its impact could be related to its effects as perceived by households regarding their future income: if they anticipate a rise in their future income, they increase their present consumption at the expense of savings. Consequently, economic growth exerts a negative effect on the current account.

Stage of development.

This variable is often included in current account models to capture the convergence process. Indeed, economies that are beginning their economic development process have a greater need for investment and are likely to finance it by external borrowing [START_REF] Obstfeld | Foundations of international macroeconomics[END_REF]. Thus, low-income countries are expected to experience larger current account deficits that decrease with the improvement in their level of development.

The literature typically uses the ratio of gross domestic product (GDP) per capita in terms of purchasing power parity (PPP) as a measure of stage of development and empirical studies generally introduce the squared relative income to allow for a non-linear relationship between relative income and current account (see [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF].

Demography. The life-cycle model provides a direct link between the stage in the life cycle and the behaviour of consumption and savings. A change in the age structure of the population will affect national saving behaviour and therefore the current account. Several variables, such as the old-age-dependency ratio, the young-age-dependency ratio and population growth, are used in the literature to evaluate the link between changes in the demographic structure and current account. The aging rate, defined as the expected change in the old-age-dependency ratio in the future, has also been introduced recently in the literature; countries in which the population is aging faster are expected to have higher savings (see [START_REF] Lane | External adjustment and the global crisis[END_REF]. Except for the aging rate, a negative relationship is expected between the other variables and the current account.

Structural features, external financing and international environment

Financial integration (financial deepening). The degree of sophistication and internationalization of the financial system can affect the level of savings and investment in an economy. However, the relationship between this variable and the current account is ambiguous. Indeed, while a well-developed financial system could induce more savings because higher yields are expected, it might also indicate fewer borrowing constraints and therefore lower savings (or more investment). Moreover, the effects on domestic investment are also not clear from a theoretical point of view [START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF]. As a measure of the sophistication and internationalization of the financial system, Ca' Zorzi et al. ( 2012) use the sum of foreign assets and liabilities as a share of GDP. Other authors use the ratio of money and quasi money (M2) to GDP as a proxy for financial deepening [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF][START_REF] Allegret | Current accounts and oil price fluctuations in oil-exporting countries: the role of financial development[END_REF], or the ratio of private credit to GDP [START_REF] Chinn | Current account balances, financial development and institutions: assaying the world "saving glut[END_REF][START_REF] Gruber | Explaining the global pattern of current account imbalances[END_REF][START_REF] Aghion | Exchange rate volatility and productivity growth: the role of financial development[END_REF].

Oil balance. This variable captures the sensitivity of the current account and the economy to changes in oil prices. An increase in oil prices should improve the current account for oilexporting countries and depreciate that of oil-importing countries. Thus, this variable allows the effect of oil prices to differ in sign and magnitude across countries (see [START_REF] Lee | Exchange rate assessments: CGER methodologies[END_REF]. [START_REF] Gruber | Explaining the global pattern of current account imbalances[END_REF] indicate that although the oil balance is a sub-part of the overall current account balance, its changes will not be correlated strictly to the current account if the non-oil balance also responds to oil price shocks. A positive relationship is nevertheless expected between the oil balance and current account position.

Terms of trade. In addition to the oil balance, terms of trade are generally introduced in current account models to capture the effects of changes in world market prices for a country's exports and imports (see [START_REF] Lane | External adjustment and the global crisis[END_REF]. Logically, we expect a positive effect from improved terms of trade on the current account to the extent that this improvement implies an augmentation in export prices relative to import prices.

Foreign aid. As shown by [START_REF] Rajan | Aid, Dutch disease, and manufacturing growth[END_REF], foreign aid could generate a Dutch disease effect. If this channel is proved for sub-Saharan African countries, a negative impact from aid on the current account through the appreciation of the exchange rate is expected. Beyond the potential Dutch disease effect, a negative relationship between aid and the current account can be expected if the aid for social projects contributes to lower precautionary savings on the part of households. Studying external balances in low-income countries, [START_REF] Christiansen | External balance in low-income countries[END_REF] found a negative effect from total foreign aid on the current account, generated especially by concessional loans.

Migrants' remittances. In relation to aid, remittances can be considered a potential determinant of the current account for developing countries. Remittances towards developing countries have increased in recent years and their impact on the real exchange rate in the recipient economies is the subject of several studies (Amuedo- [START_REF] Amuedo-Dorantes | Workers' remittances and the real exchange rate: a paradox of gifts[END_REF]Lartey et al., 2012, inter alia). Remittances may also have an impact on investment and consumption in the beneficiary countries. For example, [START_REF] Lartey | Remittances, investment and growth in sub-Saharan Africa[END_REF] finds that remittances have a significant impact on investment in sub-Saharan Africa. The effect of remittances on the current account depends in part on its relative impact on investment and savings.

Foreign direct investments (FDI). As FDI is likely to have an impact on national investment and savings, it is intimately linked to the current account. [START_REF] Fry | Foreign direct investment in a macroeconomic framework; finance, efficiency, incentives and distortions[END_REF] studies the impact of FDI on domestic investment and savings and deduces its influence on the current account for a set of developing countries. The link between the current account and FDI should be negative if the latter contributes more to strengthening domestic investment than domestic savings and vice versa.

Civil liberties. The quality of a country's institutions generally affects its attractiveness and credibility for foreign investment. Similarly, efficient and stable institutions are better able to inspire confidence among domestic private agents (both national and foreign) to stimulate investment and savings. Therefore, one may wonder about the relationship between the quality of institutions and the current account, in particular for developing countries. Civil liberties were used by [START_REF] Bussière | Methodological advances in the assessment of equilibrium exchange rates[END_REF][START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF] as one of the potential determinants of the current account. Other institutional variables are constructed by the World Bank. However, unlike civil liberties, data on these variables are not available over a long period or for all countries.

International environment. Although it is not often listed among the determinants of the current account, the international environment can have a significant impact, particularly for developing countries. As they predominantly export raw materials, developing countries are dependent on international conditions. Indeed, a favourable international environment could improve their export earnings and consequently their current account through greater foreign demand. However, a transmission delay may be observed. To capture its effect on the current account of developing countries, [START_REF] Calderon | Determinants of current account in developing countries[END_REF] use the economic growth of industrialized countries (OECD) and the world real interest rate as measures of the international environment.

Heavily Indebted Poor Countries (HIPC) initiative. Several countries in sub-Saharan Africa have benefited from the HIPC Initiative established by the World Bank and the IMF in 1996 with the aim of reducing the overall external debt of countries to bring it back to a sustainable level. If the HIPC Initiative itself cannot be considered a determinant of the current account, it is important in the case of sub-Saharan African countries to pay attention to the potential impact of such an initiative. Indeed, [START_REF] Coulibaly | Current account sustainability in sub-Saharan Africa: does the exchange rate regime matter? EconomiX Working Papers[END_REF] show that this initiative has had a significant impact on the current account sustainability of the recipient countries. A simple empirical way to monitor the impact of the HIPC Initiative is to introduce a dummy variable in the current account model.

The benchmark current account model for Sub-Saharan African countries

The set of empirical relationships between the current account and its fundamentals cannot be fully analysed through a single theoretical model [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF]. Empirical studies are generally conducted to examine the predictions of different theoretical models, and most of them operate an arbitrary choice of model specification given the lack of clear theoretical guidance. This a priori choice is subject to potential bias given the very large number of possible specifications that are ignored. This bias is especially more likely in studies on developing countries because in addition to the traditional current account fundamentals, other potential determinants (such as ODA and migrants' remittances, civil liberties, for example) may be added to account for their specificity. Moreover, the lack or limited availability of data for these countries increases the inefficiency of an empirical model with several explanatory variables. It is therefore important to adopt an efficient strategy that combines the relevance of the explanatory variables and the robustness of the estimation. As a result, we adopt an econometric approach based on model uncertainty through the Bayesian model averaging.

Bayesian Model Averaging (BMA) methodology

We use the BMA technique8 to shed light on the main fundamentals of the current account while considering the uncertainty associated with model specification given the relatively large number of potential determinants. The interesting aspect of this approach is that it addresses two major issues that typically arise in empirical studies with a relatively large number of explanatory variables and limited data and for which classical regression models do not provide an effective response, namely: (i) which variables should be included in the model and (ii) their respective importance.

Let us consider the following empirical current account model:

y = α γ + X γ β γ + ∼ N (0, σ 2 I) (1)
where y is the current account, X is the matrix of potential explanatory variables, α γ is the constant, β γ denotes the coefficients, and is the error term.

BMA addresses the problem of uncertainty in relation to model specification by estimating models for all possible combinations of {X} and constructing a weighted average. Assuming that X contains K potential explanatory variables, this means estimating 2 K variable combinations and thus 2 K models, each with a certain probability of being the "true" model. If θ is the quantity of interest, such as coefficients β, the associated posterior distribution given data D is:

p (θ| D) = 2 K γ=1 p (θ| M γ , D)p (M γ | D) (2)
Thus, the posterior distribution of θ is an average of the posterior distribution under each of the models considered, weighted by their posterior model probability. For a model M γ , the latter are obtained using Bayes' theorem:

p (M γ | D) = p (D| M γ )p(M γ ) 2 K l=1 p (D| M l )p(M l ) (3)
where

p (D| M γ ) = p (D| θ γ , M γ )p (θ γ | M γ )dθ γ is the integrated likelihood of model M γ , θ γ is the vector of parameters of model M γ , p (θ γ | M γ ) is the prior density of θ γ under model M γ , p (D| θ γ , M γ )
is the likelihood and p(M γ ) is the prior probability that M γ is the true model. The latter has to be elicited by the researcher and should reflect prior beliefs. As [START_REF] Fernandez | Model uncertainty in cross-country growth regressions[END_REF], we choose a uniform prior probability, i.e. p(M γ ) = 2 -K . This is a popular choice to represent the lack of prior knowledge.

Following [START_REF] Hoeting | Bayesian model averaging: a tutorial[END_REF], the posterior mean and variance of θ are respectively given by:

E (θ| D) = 2 K γ=0 ∆ γ p (M γ | D), (4)
V (θ| D) = 2 K γ=0 (V (θ| D, M γ ) + ∆ 2 γ )p (M γ | D) -E (θ| D) 2 , (5)
where

∆ γ = E (θ| D, M γ ).

Data

We draw on a panel of 44 sub-Saharan African countries. 9 The data are annual, spanning the period from 1980 to 2012. We collected and constructed data on the basis of the list of potential determinants of the current account position presented in Section 2. Several of these determinants, namely the fiscal balance, GDP growth rate, the level of PPP-adjusted GDP per capita, as well as all demographic variables, were expressed in relative terms, given that only their idiosyncratic shifts should affect the current account (see [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF][START_REF] Lane | External adjustment and the global crisis[END_REF]. Thus, the country-specific weighted averages of foreign variables are computed using the average foreign trade flows during the period 1996-2012.

The first ten trading partners are considered for each country (see the list and the weight of trade partners in the Appendix, Table A1). Data sources and their descriptions are shown in Table A2 and descriptive statistics are provided in Table A3 in the Appendix.

For the purpose of ensuring robustness, we consider two sets of data: five-year averaged data and annual data covering all the countries studied. Both types of data are used for various purposes which may be complementary. On the one hand, the five-year averaged data allow the smoothing of business-cycle fluctuations and therefore an in-depth focus on the underlying determinants of the current account (see among others, [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF][START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF][START_REF] Lane | External adjustment and the global crisis[END_REF]. This is particularly relevant in the case of developing countries for which measurement errors are also at stake, in addition to the significant short-term fluctuations they face due to the volatility of commodity prices.

On the other hand, the use of longer time series (annual data) allows for more variation both across and within countries, which should help in obtaining the real robust determinants of the current account (see [START_REF] Dufrenot | The trade-growth nexus in the developing countries: a quantile regression approach[END_REF] concerning growth determinants).

Estimation Results

We consider the 21 potential determinants of the current account discussed in Section 2, leading to the estimation of more than two million different models (exactly 2 21=2097152 models). The results of the BMA approach for all the 44 sub-Saharan African countries are presented in Table 1. The importance of the variables in explaining the current account position (p (β i = 0| D)) is given in the column "BMA PIP", which represents the posterior inclusion probabilities (PIPs), i.e. the sum of posterior model probabilities for all models in which a variable was included. A variable is supposed to be relevant in explaining the current account and is considered a fundamental variable when its PIP is greater than or equal to 50% and there is no uncertainty about its sign (see [START_REF] Raftery | Variable selection and Bayesian model averaging in case? Control studies[END_REF][START_REF] Dufrenot | The trade-growth nexus in the developing countries: a quantile regression approach[END_REF].

The results for the annual data and for the five-year averaged data are very close. In both cases, eight fundamentals of the current account are identified, among which seven are common, namely: FDI, financial deepening, fiscal balance, net foreign assets, oil balance, migrants' remittances and stage of development. The similarity between these results reflects the fact that the medium-term fundamentals of the current account (five-year averaged data) are not especially different from the short-term fundamentals (annual data). Turning to the eighth variable, relative GDP growth is identified only in the five-year averaged data, while ODA is retained only for the annual data. ODA is likely to be regarded as a fundamental of the current account to the extent that its PIP is higher than 80% for the annual data and also is not negligible (27%) for the five-year averaged data. Consequently, ODA is linked to the current account to a much greater extent in the short term, which is of considerable relevance as the volume of aid to developing countries is often determined by the economic conditions in donor countries. Regarding relative GDP growth, its inclusion in the model is only significant for the five-year averaged data, suggesting that this variable is rather a medium-term determinant of the current account. The results are based on 500,000 draws and 100,000 burn-ins. For each simulation, we use a uniform prior10 and the birth-death MCMC sampler. Statistics in bold are those for which the posterior inclusion probability is greater than or equal to 50%. The correlation between iteration counts and analytical posterior model probabilities for the 2,000 best models is 0.9993.

If certain fundamentals, such as fiscal balance, net foreign assets, oil balance and stage of development, were expected given that they are highlighted by most previous empirical studies (see references in Section 2), this is not necessarily the case for other variables, such as financial deepening, ODA, migrants' remittances and FDI. Often, these are not or only poorly examined in empirical studies that generally focus on the traditional determinants of the current account without considering explicitly the specificity of the countries studied. This highlights the value of the BMA approach in identifying the fundamentals of the current account without bias and without ignoring the characteristics of the countries under study.

Whilst it is important to determine properly the fundamentals of the current account, it is equally important to check whether the signs of the coefficients associated with the different variables are consistent with the predictions of theoretical models. The column "Post Mean" displays the coefficients averaged over all models, including the specifications wherein the variable was not contained (in which case its coefficient is zero). The coefficient sign can be derived from the column "Cond.Pos.Sign" which shows the "posterior probability of a positive coefficient expected value conditional on inclusion", respectively "sign certainty" (see [START_REF] Zeugner | Bayesian model averaging with BMS[END_REF]. When the statistic is close to 1, the variable undoubtedly has a positive sign, whereas when it is close to zero, the variable has a negative sign. Figure 1 goes further in the analysis and presents the posterior coefficient densities to shed light on their sign and to compare them to theoretical predictions.

As shown in Figure 1, the signs of the traditional fundamentals are consistent with theoretical predictions. Indeed, the fiscal balance is clearly positively related to the current account, 95% of the mass of its posterior coefficients being between 0.099 and 0.593 with an estimated average of 0.19. For comparison, [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF] found a coefficient ranging between 0.39 and 0.45 for their subsample of developing countries. [START_REF] Christiansen | External balance in low-income countries[END_REF] obtained a coefficient ranging between 0.24 and 0.37 for a sample of low-income countries. According to our findings, the "twin deficit" assumption seems to apply to sub-Saharan African countries, which means that an increase in fiscal deficits leads to a deterioration in the external balance. This is consistent with the predictions of overlapping generation models, as well as [START_REF] Blanchard | Debt, deficits and finite horizons[END_REF] finite horizon model. Similarly, initial net foreign assets and the oil balance have a positive influence on the dynamics of the current account, which is in line with most empirical studies (see e.g. [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF][START_REF] Lee | Exchange rate assessments: CGER methodologies[END_REF][START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF]IMF, 2013). Indeed, our results show that the coefficients associated with initial net foreign assets and the oil balance are concentrated at 95%, between 0.015 and 0.034 for initial net foreign assets and between 0.132 and 0.300 for the oil balance, with respective estimated averages of 0.027 and 0.215. Thus, our estimated coefficient for net foreign assets is very close to that of [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF], which ranges between 0.017 and 0.043 for their subsample of developing countries. Ca' Zorzi et al. (2012) find significant estimated coefficients of 0.03 for net foreign assets and 0.14 for the oil balance for their sub-sample of African countries, which are also very close to ours. In addition, the stage of development appears to be related positively to the current account, with an estimated coefficient between 0.019 and 0.176, confirming the theoretical effect expected in the context of the convergence process. Regarding financial deepening, although there is no clear theoretical guidance on the direction of its link with the current account [START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF], our results show that this link is negative for sub-Saharan African countries even if the coefficient is very low (between -0.010 and -0.004, with an estimated average of -0.007).11 This negative relationship-also evidenced by [START_REF] Cheung | Structural and cyclical factors behind currentaccount valances[END_REF] and [START_REF] Allegret | Current accounts and oil price fluctuations in oil-exporting countries: the role of financial development[END_REF]-can be justified by the fact that the movement of domestic financial liberalization that occurred in many sub-Saharan African countries during the 1980s and was reinforced in the 1990s has contributed to reducing borrowing constraints and boosting investment and a positive relationship between financial deepening and investment has been established in most studies [START_REF] Benhabib | The role of financial development in growth and investment[END_REF]Christopoulos and Tsionas, 2004, among others).

In addition to these fairly standard results, our estimates establish that other variables, such as FDI, migrants' remittances and, to some extent, ODA, influence the dynamics of the current account in sub-Saharan African countries. The coefficient associated with remittances from migrants is globally between 0.081 and 0.421, with an estimated average of 0.189. Regarding FDI and ODA, their influence on the current account is shown to be negative (with coefficients between -1.114 and -0.578, and between -0.282 and -0.002 respectively). To gain a greater understanding of the relationship between these non-traditional variables and the current account, we examine the relationship between each of these and savings and investment. Table 2 presents these links for different types of data (annual, five-year averages, cross-sectional) for all countries. We find that the negative relationship between FDI and the current account can be clearly explained by the savings-investment channel. Indeed, although FDI seems to have a positive and significant impact on savings and investment for sub-Saharan African countries, the influence on domestic investment is much higher than on domestic savings. The difference between the coefficients associated with savings and investment is equal to the coefficient linking FDI and the current account. We also observe that the negative relationship between ODA and the current account could be explained by the same channel. Indeed, ODA is associated with a decline in national savings in sub-Saharan African countries, while it has no significant influence on investment. Given that ODA is often oriented towards social spending (health, education) or social projects, it would contribute to reducing the precautionary savings of households and consequently national savings. The positive relationship between migrants' remittances and the current account cannot really be clarified by reference to the same channel as such remittances seem to have an almost equal impact on investment and savings (even with a slight advantage for investment).

Robustness checks

To assess the robustness of our results and ensure that we have identified the "true" fundamentals of the current account, we reprocess our database to eliminate possible outliers that could potentially bias our results. As a first step, we exclude all countries for which the absolute value of the current account is greater than or equal to 45% of GDP at any point in time. In a second step, we exclude countries with significant missing data (greater than or equal to 25% of the total number of observations for variables previously considered relevant). Finally, given that data issues tend to be more frontloaded and that some relevant variables, such as fiscal balance, are available for the vast majority of the countries from 1990 onwards, we consider the shorter period 1990-2012. This also allows us to check if the fundamentals remain robust over time. Our sample now comprises 35 countries12 over the period 1990-2012.

The BMA results for this new sample are reported in Table 3.

These results clearly confirm our previous findings. Indeed, six of the seven fundamentals previously identified jointly for the annual and five-year averaged data, namely FDI, financial integration, fiscal balance, initial net foreign assets, oil balance and stage of development, are also retained here. The seventh variable, migrants' remittances, also has a PIP of 100% for the annual data and a PIP of 35% for the five-year averaged data, highlighting the robustness of this variable as a current account fundamental. The only variable which is no longer identified as a fundamental is relative GDP growth. In addition, two new variables are identified using the annual data, namely population growth and the world real interest rate, which are negatively linked to the current account. These results are not very surprising because (i) financial openness in most sub-Saharan African countries actually began from 1990 onwards and (ii) the result regarding population growth is fairly standard in empirical studies (see [START_REF] Lee | Exchange rate assessments: CGER methodologies[END_REF][START_REF] Christiansen | External balance in low-income countries[END_REF][START_REF] Lane | External adjustment and the global crisis[END_REF]. The sign of the coefficients also remains stable and consistent with previous results, as illustrated in Figure 2, showing the marginal density of posterior coefficients. The results are based on 500,000 draws and 100,000 burn-ins. For each simulation, we use a uniform model prior and the birth-death MCMC sampler. Statistics in bold are those for which the posterior inclusion probability is greater than or equal to 50%. The correlation between iteration counts and analytical posterior model probabilities for the 2,000 best models is 0.9988.

As another robustness check, we look at what would have prevailed had we only used classic regression techniques to determine the fundamentals of the current account. We thus perform panel OLS estimations on both the entire sample and on the second restricted sample of 35 countries. This conventional method13 is the most commonly used in empirical studies on the medium-term determinants of the current account (see [START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF]Chinn and Ito, 2005;[START_REF] Gruber | Explaining the global pattern of current account imbalances[END_REF][START_REF] Ca' Zorzi | Thousands of models, one story: current account imbalances in the global economy[END_REF][START_REF] Lane | External adjustment and the global crisis[END_REF][START_REF] Gnimassoun | Current-account adjustments and exchange-rate misalignments[END_REF][START_REF] Steiner | Current account balance and dollar standard: exploring the linkages[END_REF]among others). The results are presented Note: The coefficients represented here are those obtained from 5-year averaged data according to the robustness analysis.

in Tables A.3 and A.4 in the Appendix and are consistent with our previous BMA findings regardless of the type of data used. For example, we find that all the fundamentals identified by BMA are significant at the 1% level for the different regressions based on models that include all potential determinants of the current account. We also note a strong similarity between the values of coefficients estimated by BMA and those estimated using classic regressions. Other variables, such as terms of trade, the young-age-dependency ratio and trade openness, have the expected signs but their significance depends (in most cases) on the model specification.

Regarding the young-age-dependency ratio, for instance, the negative sign derives from the face that the population is inactive and that an increase in this ratio compared to that of the active population will lead to a decline in national savings. The problem is that in developing countries, the share of the population of working age does not necessarily reflect the share of the working population. Consequently, the expected effect of the young-age-dependency ratio on national savings is not necessarily obtained as the indicator itself does not reflect reality.14

Current account imbalances and the exchange rate regime

Having shedding light on the fundamentals of the current account in the previous section, we now examine the relationship between external imbalances and the exchange rate regime. This is a key question, especially for developing countries for which the choice of the exchange rate regime is crucial to consolidate and accelerate the pace of their economic growth. Since the works of [START_REF] Friedman | The case for flexible exchange rates[END_REF] and [START_REF] Mundell | A theory of optimum currency areas[END_REF], it has been well known that the main disadvantage of belonging to a monetary union or the choice of a fixed exchange rate regime is the loss of the autonomy of exchange policy to deal with external shocks, such as terms of trade shocks for example. One might think that countries under fixed exchange rate regimes or those belonging to a monetary union may have more current account imbalances, which is detrimental to economic growth. However, other works, including those of [START_REF] Giavazzi | The advantage of tying one's hands: EMS discipline and central bank credibility[END_REF], [START_REF] Aghevli | Exchange rate policies in developing countries: Some analytical issues[END_REF] and [START_REF] Blanchard | Current account deficits in the euro area: the end of the Feldstein-Horioka puzzle?[END_REF], show that this does not necessarily generate more current account imbalances as countries that opt for such a system generally have more discipline in terms of economic policy-especially fiscal policywhich is often governed by rules. According to these authors, the choice of a fixed exchange rate regime or membership in a monetary union strengthens the credibility of the countries concerned. The debate on this issue is still open, as illustrated by the recent works of [START_REF] Ghosh | Is the exchange rate regime really irrelevant for external adjustment[END_REF] and [START_REF] Chinn | A faith-based initiative meets the evidence: does a flexible exchange rate regime really facilitate current account adjustment[END_REF] which show different results for the role of the exchange rate regime in the adjustment of current accounts.

Assessment of current account imbalances

We define current account imbalances as the gap between the observed current account (in percentage of GDP) and its estimated "equilibrium" value as follows:

CA gap i,t = CA i,t -CA i,t (6)
where CA gap i,t is our measure of current account imbalance, CA i,t is the observed current account and CA i,t is the equilibrium current account estimated from fundamentals. In other words, the external imbalances are defined as the residues from the estimation of equation (1). According to our previous results, the equilibrium current account is given by:

CA i,t = β 1 F DI i,t + β 2 F IN T i,t + β 3 F IS i,t + β 4 L.N F A i,t + β 5 ODA i,t + β 6 OILB i,t + β 7 REM i,t + β 8 RP ROD i,t (7)
For the assessment of external imbalances, we use annual data. 15 We keep our two samples for 0.02 and 0.04 for the annual and five-year averaged data respectively. Thus, we do not comment further on these coefficients, derived from a particular specification and not identified by BMA. The fact that BMA does not identify these variables as relevant determinants of the current account seems quite reasonable.

comparison purposes. However, to avoid the over-interpretation of external imbalances due to their possible overestimation, we pay more attention to the results from the sample purged of outliers. Table 4 summarizes the coefficients obtained from regressions (BMA, panel OLS, instrumental variables 16 (IV)) based only on fundamentals (see Table A.6 in the Appendix). As the current account imbalances derived from these coefficients give very similar results, 17 we keep those obtained with BMA. Note: The columns "BMA" refer to the BMA results presented respectively in Tables 1 and3. The columns "Panel OLS" show the coefficients estimated using panel OLS regressions based only on the fundamentals. The columns "IV regression" show the coefficients estimated using instrumental variables (IV) regression based only on the fundamentals (see Table A.6 in Appendix).

Empirical relationship between current account imbalances and the exchange rate regime

Having evaluated external imbalances, we can now explore whether there is an empirical link between these disequilibria and the exchange rate regime. We use two measures for comparison and robustness purposes: the de facto exchange rate classifications initially developed by [START_REF] Reinhart | The modern history of exchange rate arrangements: a reinterpretation[END_REF] and recently updated by [START_REF] Ilzetzki | The country chronologies and background material to exchange rate arrangements in the 21st century: which anchor will hold? mimeo[END_REF]-henceforth IRR-as well as the IMF official classifications (the de jure classifications). 18 Drawing on these classifications, we define three broad categories of exchange rate regimes-fixed, intermediate and flexible-allowing greater clarity in the analysis, as is fairly standard in the literature (see [START_REF] Chinn | A faith-based initiative meets the evidence: does a flexible exchange rate regime really facilitate current account adjustment[END_REF][START_REF] Ghosh | Is the exchange rate regime really irrelevant for external adjustment[END_REF][START_REF] Pancaro | Current account reversals in industrial countries. Does the exchange rate regime matter? European Central Bank[END_REF]. 19 Figure 3 displays the relationship between external imbalances and exchange rate regimes.

16 Instrumental variables (IV) regression is used here to correct biases associated with a possible endogeneity phenomenon. Lagged values of the explanatory variables are used as instruments. We find similar results by regressing the current account on the lagged values of the regressors by OLS. 17 The correlation coefficients calculated between external imbalances are close to unity; see Table A.7 in the Appendix.

18 The de facto classifications reflect country real practice until 1997; the IMF classifications were based on the country's official claim. Another well-known classification is that proposed by [START_REF] Levy-Yeyati | Classifying exchange rate regimes: deeds versus words[END_REF]. We do not use it here because it has not recently been updated.

19 See Table A.8 in the Appendix. The correlation coefficient between the IRR and IMF classifications after being grouped into three broad categories is 0.60.

As shown, external imbalances have higher amplitudes under fixed and intermediate regimes, irrespective of the classification scheme. However, given the difference in the number of observations between exchange rate regimes, it is important to consider the relative shares of imbalances depending on exchange rate regimes. According to the IMF classification, approximately 15% of the absolute values of external imbalances under fixed and intermediate regimes are greater than 10% against only 6% under the flexible regime. When considering the IRR classifications, this share is 16% under the fixed exchange rate regime, 13% under the intermediate regime and 8% under the flexible regime. The share of external imbalances greater than 20% in absolute value is higher under the fixed and intermediate regimes. External imbalances, being the residues derived from the medium-term estimation of the current account, are by definition the consequence of deviations of the fundamentals from their medium-term value. Thus, in addition to our variable of interest, namely the exchange rate regime, it is also important to include the deviations of the fundamentals from their medium-term value as control variables. As economic determinants in developing countries generally vary with their stage of development, we consider as the medium-term value of fundamentals their moving average of order 3. Moreover, as currency crises tend to exacerbate external imbalances, we follow the literature by including a dummy variable for the crisis years as in the database of [START_REF] Reinhart | From financial crash to debt crisis[END_REF]. This is even more important as several countries in our panel, in particular the CFA zone countries, experienced a devaluation of their currency in 1994. A positive sign is thus expected for this variable. As significant positive or negative external imbalances are not optimal, our dependent variable is the absolute value of external imbalances. Indeed, as argued by [START_REF] Blanchard | Why) Should current account balances be reduced? IMF Staff Discussion Note[END_REF], there are both national and international reasons for a country to reduce its external imbalances (surpluses or deficits). Similarly, to study the impact of exchange rate regimes on currency misalignments, [START_REF] Dubas | The importance of the exchange rate regime in limiting misalignment[END_REF] uses the absolute value of exchange rate misalignments as the dependent variable. This allows us to check whether the exchange rate regime influences the overall external imbalances. Finally, our specification to investigate the link between external imbalances and exchange rate regimes is given by:

CA gap i,t = ϑ i + ϕ t + π 1 REGM i,t + π 2 CRISIS + π 3 GAP F DI i,t + π 4 GAP F IN T i,t + π 5 GAP F IS i,t + π 6 GAP L.N F A i,t + π 7 GAP ODA i,t + π 8 GAP OILB i,t + π 9 GAP REM i,t + π 10 GAP RP ROD i,t + µ i,t (8)
Our explanatory variable of interest is the exchange rate regime (REGM ) that takes the values 1, 2 and 3 for fixed, intermediate and flexible exchange rate regimes respectively. If the coefficient associated with this variable is negative and significant, it means that the flexibility of the exchange rate regime allows the limiting of overall external imbalances. In the reverse case, the flexibility of the exchange rate regime would lead to more external imbalances and if this coefficient is not significant, it indicates that the exchange rate regime has no effect on the overall external imbalances. The variable reflecting currency crises (CRISIS) takes the value 1 in the years of currency crisis for a specific country and 0 otherwise. The other control variables are the deviations of the fundamentals from their medium-term value. These are considered both in nominal terms and in relation to absolute values. Although positive signs are expected for the absolute values of these deviations, this is not necessarily the case for nominal deviations (calculated deviations, positive or negative).

The estimate of the coefficient of interest (π 1 ) does not provide all information concerning the role of the exchange rate regime and in particular the relative impact of each regime. Thus, we explore this effect by rewriting equation ( 8) as follows:

CA gap i,t = ϑ i + ϕ t + π 1,1 REGM 1 i,t + π 1,2 REGM 2 i,t + π 2 CRISIS + π 3 GAP F DI i,t + π 4 GAP F IN T i,t + π 5 GAP F IS i,t + π 6 GAP L.N F A i,t + π 7 GAP ODA i,t + π 8 GAP OILB i,t + π 9 GAP REM i,t + π 10 GAP RP ROD i,t + µ i,t (9)
Equation ( 9) is the same as equation ( 8) with the difference that REGM is decomposed into its three components REGM 1, REGM 2 and REGM 3 (fixed, intermediate and flexible exchange rate regimes respectively). REGM 1 takes the value 1 for a particular year if the exchange rate regime is classified as fixed in this year and 0 otherwise. We proceed in the same way for REGM 2 and REGM 3.20

The results of the estimation of the parameters from equation ( 8) are displayed in Table 5.

As shown, there is a strong link between overall external imbalances and the exchange rate regime regardless of the classification and the sample; however, this link is stronger for the IRR classification and the reduced sample. The negative coefficient associated with the variable REGM suggests that a flexible exchange rate regime allows the limiting of absolute external imbalances. The variable CRISIS has the expected sign but appears generally insignificant. This result can be explained by the fact that the countries under study have actually experienced few or no real currency crises specific to themselves even though the countries in the CFA zone experienced currency devaluation in 1994. Regarding the other control variables, deviations in net foreign assets are significantly associated with an increase in absolute external imbalances regardless of the sample. The impact of the deviations in variables such as FDI, the oil balance, migrants' remittances and fiscal balance appears remarkably more significant when one considers the whole panel. We also note that the absolute deviations in ODA play a significant role in increasing absolute external imbalances.

Returning to our variable of interest, Tables 6 and7 show the results of estimating the parameters of equation ( 9) allowing the specific effect of each exchange rate regime to be examined. Under each classification, we have six columns. In the first two columns, fixed and intermediate regimes are included in the estimation and the flexible regime is used for identification. In the next two columns, intermediate and flexible regimes are considered and in the last two columns, we look at the relative effect of fixed and flexible regimes.

The results for the reduced sample (Table 6) clearly highlight that the flexible exchange rate regime plays a key role in reducing external imbalances and the intermediate exchange rate regime performs better than the fixed regime. Indeed, taking as our reference the fixed regime, for example, we find that the flexible exchange rate regime is associated with the lowest degree of absolute external imbalances with an estimated parameter of between -2.5 and -2.1 depending on the classification. The intermediate regime follows with an estimated coefficient of between -1.1 and -0.9 for the IRR classification, but its coefficient does not appear significant for the IMF classification. Moreover, compared to the intermediate case, the flexible regime reduces external imbalances by approximately 1.4 point more for both classifications, whereas the fixed regime increases these imbalances by approximately 1 point more for the IRR classification. Regarding the control variables, CRISIS has the expected sign but is not statistically significant. The fundamentals the deviations of which contribute most significantly to increasing absolute external imbalances are primarily net foreign assets, the oil balance and, to a lesser extent, migrants' remittances and fiscal balance. Note: Robust standard errors are in parentheses; *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level. Models 1 are those in which the deviations in the fundamentals are in nominal terms (not in absolute value), whereas in Model 2, we take the absolute value of these deviations. Note: Robust standard errors are in parentheses; *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level. Models 1 are those in which the deviations in the fundamentals are in nominal terms (not in absolute value), whereas in Model 2, we take the absolute value of these deviations. Referring to the whole panel (Table 7), most of the control variables-with the exception of the currency crisis and deviations in financial deepening-significantly explain overall external imbalances. As in the case of the restricted sample, the flexible exchange rate regime contributes significantly to reducing external imbalances in comparison to fixed and intermediate regimes, but the magnitude seems lower (between -1.8 and -1.3 points for the fixed regime and between -1.6 and -1.1 points for the intermediate regime using the IRR classification). However, for the IMF classification, the flexible exchange rate regime seems to do better only in comparison to the fixed regime. Moreover, the difference between the fixed and intermediate regimes seems low in relation to the IRR classification and there is an insignificant difference for the IMF classification. However, as previously stressed, more interest should be paid to the results obtained from the reduced sample as outliers in the overall sample are likely to influence the results.

Altogether, our empirical results show that there is a strong link between the exchange rate regime and external imbalances. The flexible exchange rate regime allows the limiting of absolute external imbalances. To a lesser extent, the intermediate regime is better than the fixed regime in terms of limiting external imbalances. Moreover, for most regimes, deviations in the current account fundamentals from their medium-term values tend to increase absolute external imbalances. Our conclusions are in line with Friedman's thesis, according to which flexible exchange rate regimes allow a faster adjustment of the current account. Our findings are also consistent with those of [START_REF] Coulibaly | Current account sustainability in sub-Saharan Africa: does the exchange rate regime matter? EconomiX Working Papers[END_REF], which show that current account sustainability was higher for countries under a flexible exchange rate regime over the period 1980-2011. Moreover, employing a panel of 95 developing countries, [START_REF] Mu | Current account adjustment in developing countries: the role of exchange rate regimes[END_REF] find that fixed exchange rate regimes increase the duration of high deficit spells and thus delay current account adjustment, thereby supporting our results even though our samples are different. Even if we do not explicitly deal with the same issue, our findings are in line with those of [START_REF] Levy-Yeyati | To float or to fix: evidence on the impact of exchange rate regimes on growth[END_REF] who find that for developing countries (largely composed of countries in our sample), less flexible exchange rate regimes are associated with slower growth, as well as with greater output volatility. Indeed, for developing countries, especially those in our sample, output volatility is probably related to external imbalances, all aspects detrimental to the stability of growth and therefore to development. Our findings are also consistent with those of [START_REF] Edwards | Flexible exchange rates as shock absorbers[END_REF], who find that flexible exchange rate regimes help reduce the real impact of terms of trade shocks.

Conclusion

Despite the various crises that marked the 2000s, developing countries have experienced quite remarkable economic performance. In this context, the choice of the exchange rate regime to consolidate and accelerate the momentum of economic growth becomes a central issue, together with the intentions of forming monetary unions. In this paper, we focus on sub-Saharan African countries and investigate whether the choice of a country's exchange rate regime can influence the magnitude of its external imbalances. To this end, we first identify the main current account determinants of these countries by accounting for their structural characteristics using the Bayesian model averaging (BMA) approach. We then determine medium-term ex-ternal imbalances based on the following key fundamentals: foreign direct investment (FDI), financial deepening, fiscal balance, initial net foreign asset position, oil balance, migrants' remittances, official development assistance (ODA) and stage of development. Finally, using de facto as well as official exchange rate classifications, we demonstrate that there is a strong link between external imbalances and exchange rate regimes. More specifically, the flexible exchange rate regime is the most effective in preventing absolute external imbalances.

While our findings do not promote the choice of fixed exchange rate regimes or monetary unions in sub-Saharan Africa, they are in no way opposed to such guidance. Monetary unions can clearly be beneficial for sub-Saharan African countries, but the low economic diversification in sub-Saharan Africa should not be neglected. Specifically, the sharing of exogenous risks must be at the centre of discussions to avoid the painful economic measures often related to the adjustment of external imbalances for a particular country. In relation to this, [START_REF] Tapsoba | West African monetary integration and interstates risk-sharing[END_REF] advocates saving as an effective means of coping with heterogeneous shocks in the context of the envisaged monetary union in West Africa. Our findings support the development of adjustment mechanisms against external imbalances at a regional level, the responsibility for which will be the jurisdiction of future central banks. Given that the sum of trade flows with the first ten trading partners is less than 100%, the different weights were then normalized to 100%.

Figure 1 :

 1 Figure 1: Posterior Coefficient Densities

Figure 2 :

 2 Figure 2: Robustness check for the Posterior Coefficient Densities

Figure 3 :

 3 Figure 3: Current account imbalances and exchange rate regimes

Figure A. 1 .

 1 Figure A.1. Effect of the prior choice

Table 1 :

 1 BMA estimates for full sample

			Annual data			5-year averaged data
	Variables	BMA	Post.	Post.	Cond.	BMA	Post.	Post.	Cond.
		PIP	Mean	S.D.	Pos. Sign	PIP	Mean	S.D.	Pos. Signe
	CLIB	0.043	0.000	0.001	0.982	0.062	0.000	0.001	0.974
	FDI	1.000 -0.950 0.051	0.000	1.000 -0.830 0.135	0.000
	FINT	1.000 -0.006 0.001	0.000	0.993 -0.007 0.002	0.000
	FIS	0.997	0.172	0.042	1.000	0.686	0.236	0.191	1.000
	HIPC	0.250	0.004	0.007	1.000	0.113	0.002	0.007	0.999
	L.NFA	1.000	0.020	0.003	1.000	0.992	0.027	0.007	1.000
	L.OECD_G	0.043	0.005	0.042	0.972	0.074	0.043	0.212	1.000
	ODA	0.841 -0.095 0.053	0.000	0.271	-0.039	0.074	0.000
	OILB	1.000	0.231	0.022	1.000	1.000	0.215	0.042	1.000
	OPEN	0.038	0.000	0.002	0.868	0.138	0.004	0.012	0.932
	RAGING	0.044	-0.003	0.034	0.141	0.052	-0.005	0.061	0.252
	REER	0.057	-0.001	0.003	0.000	0.077	-0.002	0.008	0.000
	REM	1.000	0.279	0.048	1.000	0.751	0.189	0.132	1.000
	RGROWTH	0.059	-0.003	0.018	0.000	0.970 -0.400 0.133	0.000
	RO	0.063	-0.002	0.009	0.001	0.047	0.000	0.009	0.489
	RPOPG	0.198	-0.099	0.229	0.000	0.045	-0.002	0.119	0.531
	RPROD	0.939	0.082	0.043	1.000	0.501	0.043	0.051	1.000
	RPROD2	0.154	-0.007	0.043	0.341	0.194	0.011	0.043	0.856
	RY	0.070	0.001	0.004	0.971	0.066	0.000	0.006	0.657
	TOT	0.182	0.003	0.007	1.000	0.070	0.001	0.006	1.000
	WRIR	0.295	-0.169	0.298	0.000	0.071	-0.045	0.233	0.000
	Note:

Table 2 :

 2 Link between (FDI, REM, ODA) and(Saving, Investment)

		Annual data	5-year averaged	Cross-sectional
	Variables	(1980-2012)	data (1980-2012)	data (mean 1980-2012)
		Saving	Investment	Saving	Investment	Saving	Investment
		Coef.	Coef.	Coef.	Coef.	Coef.	Coef.
	FDI	0.12**	1.17***	0.31***	1.34***	1.14***	2.81***
		[2.46]	[23.84]	[2.83]	[12.43]	[3.03]	[7.43]
	REM	0.30***	0.37***	0.30***	0.37***	0.31**	0.37**
		[9.87]	[13.09]	[5.53]	[5.62]	[2.58]	[2.22]
	ODA	-0.16***	0.06*	-0.21***	0.05	-0.22	0.20
		[-5.22]	[1.72]	[-3.53]	[0.74]	[1.63]	[1.03]

Note: T-statistics are given in brackets; *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.

Table 3 :

 3 BMA robustness results

			Annual data			5-year averaged data
	Variables	BMA	Post.	Post.	Cond.	BMA	Post.	Post.	Cond.
		PIP	Mean	S.D.	Pos. Sign	PIP	Mean	S.D.	Pos. Signe
	CLIB	0.039	0.000	0.000	0.359	0.048	0.000	0.001	0.292
	FDI	1.000 -0.653 0.069	0.000	1.000 -0.749 0.127	0.000
	FINT	1.000 -0.005 0.001	0.000	0.763 -0.003 0.002	0.000
	FIS	1.000	0.477	0.059	1.000	0.998	0.701	0.175	1.000
	HIPC	0.044	0.000	0.002	0.141	0.039	0.000	0.003	0.768
	L.NFA	1.000	0.013	0.003	1.000	0.636	0.011	0.009	0.991
	L.OECD_G	0.067	0.012	0.062	1.000	0.416	0.524	0.716	1.000
	ODA	0.079	-0.003	0.014	0.000	0.279	-0.041	0.075	0.000
	OILB	1.000	0.185	0.020	1.000	0.982	0.151	0.046	1.000
	OPEN	0.254	-0.005	0.011	0.000	0.065	0.001	0.006	0.786
	RAGING	0.040	0.000	0.026	0.474	0.066	-0.013	0.102	0.289
	REER	0.068	0.001	0.005	1.000	0.033	0.000	0.004	0.618
	REM	1.000	0.205	0.048	1.000	0.347	0.059	0.092	1.000
	RGROWTH	0.039	0.000	0.010	0.737	0.039	-0.003	0.030	0.120
	RO	0.162	-0.006	0.017	0.000	0.038	0.000	0.008	0.388
	RPOPG	0.749 -0.491 0.344	0.000	0.299	-0.285	0.500	0.000
	RPROD	0.983	0.098	0.042	1.000	0.557	0.048	0.058	1.000
	RPROD2	0.154	-0.012	0.043	0.108	0.166	-0.001	0.050	0.660
	RY	0.045	0.000	0.002	0.916	0.072	-0.001	0.006	0.260
	TOT	0.096	0.001	0.004	1.000	0.039	0.000	0.004	0.945
	WRIR	0.980 -0.843 0.254	0.000	0.075	-0.050	0.234	0.000
	Note:

Table 4 :

 4 Coefficients of the fundamentals

		Whole sample	Reduced sample (35 Countries
	Variables	(1980-2012)		from 1990 to 2012)
		BMA	Panel	IV	BMA	Panel	IV
		benchmark	OLS	regression	benchmark	OLS	regression
	β F DI	-0.950	-0.947	-1.059	-0.653	-0.710	-0.751
	β F IN T	-0.006	-0.006	-0.008	-0.005	-0.006	-0.005
	β F IS	0.172	0.180	0.638	0.477	0.467	0.752
	β L.N F A	0.020	0.019	0.007	0.013	0.018	0.020
	β ODA	-0.095	-0.117	-0.110	-0.041	-0.143	-0.008
	β OILB	0.231	0.229	0.175	0.185	0.177	0.289
	β REM	0.278	0.252	0.225	0.205	0.164	0.348
	β RP ROD	0.072	0.046	0.060	0.098	0.028	0.058

Table 5 :

 5 Overall effect of the exchange rate regime

	Whole sample	IRR regime IMF regime	Model 1 Model 2 Model 2 Model 1	-0.745** -0.525** -0.256 -0.702**	(0.311) (0.252) (0.260) (0.287)	3.041 1.889 2.618 4.276	(2.788) (4.229) (5.277)
	Reduced sample (35 Countries from 1990 to 2012)	IRR regime IMF regime	Model 1 Model 2 Model 2 Model 1	-1.200*** -0.927*** -0.963*** -0.642***	(0.277) (0.264) (0.246) (0.232)	2.982 2.990 3.788 3.624	(7.040) (6.719) (6.953) (6.716)
		Variables		REGM		DEVA

Table 6 :

 6 Specific effect of the exchange rate regime (Reduced sample)

	IRR regime

Table 7 :

 7 Specific effect of the exchange rate regime (Whole sample) Robust standard errors are in parentheses; *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level. Models 1 are those in which the deviations in the fundamentals are in nominal terms (not in absolute value), whereas in Model 2, we take the absolute value of these deviations.

	IRR regime

Table A

 A United States, TPC = Taiwan Province of China, CAF = Central African Republic, RDC = Democratic Republic of the Congo, UK = United Kingdom, UAE = United Arab Emirates, STP = Sao Tome and Principe, TT = Trinidad and Tobago.

		Sum		88.5%		60.8%		74.1%		74.6%		57.7%		70.7%		70.8%		56.9%		61.4%		60.7%		73.4%		72.7%
			USA	1.0%	Thailand	3.1%	USA	3.7%	India	3.3%	Netherlands	2.4%	Italy	1.9%	Zimbabwe	1.7%	Germany	3.2%	USA	3.1%	Netherlands	2.9%	Kuwait	2.2%	Netherlands	2.5%
			China	1.2%	Netherlands	3.4%	UAE	4.7%	Japan	3.8%	France	2.7%	Korea	1.9%	Thailand	2.4%	Ivory Coast	3.3%	Netherlands	3.2%	Switzerland	2.9%	Korea	2.5%	RDC	3.0%
			Gabon Brazil	2.2% 1.4%	Italy Spain	4.2% 3.8%	South Africa Germany	5.6% 5.0%	Germany Canada	5.0% 4.4%	India Italy	3.3% 3.1%	Germany UK	2.5% 2.4%	Mozambique TPC	2.7% 2.6%	Nigeria Benin	3.7% 3.6%	UK Germany	4.3% 4.0%	UK Germany	4.0% 3.7%	India UAE	2.8% 2.7%	USA Japan	3.7% 3.6%
	.1. (continued)	Main trade partners	Netherlands France	3.3% 2.3%	UK China	5.0% 4.4%	Singapore Spain	6.0% 5.7%	Netherlands UK	5.5% 5.3%	Japan Saudi Arabia	7.0% 3.3%	India Egypt	3.8% 2.8%	UK China	3.1% 3.1%	Netherlands Burkina Faso	4.4% 4.2%	South Africa Japan	4.5% 4.3%	Kenya Japan	5.8% 5.1%	UK Zimbabwe	4.0% 3.8%	Zambia Germany	4.2% 3.8%
			Belgium	4.8%	Mali	5.5%	Italy	6.9%	USA	6.6%	UK	7.2%	UAE	5.9%	India	3.2%	India	5.0%	China	5.5%	UAE	6.2%	RDC	8.3%	UK	4.7%
			Japan	5.3%	India	6.2%	Saudi Arabia	12.0%	China	7.9%	USA	9.4%	Saudi Arabia	6.5%	Korea	3.6%	Ghana	5.9%	India	6.9%	South Africa	7.6%	Switzerland	8.5%	Botswana	5.0%
			Angola	14.6%	Nigeria	6.5%	France	12.1%	Belgium	9.1%	China	9.7%	Japan	9.0%	USA	7.6%	France	8.0%	UAE	8.2%	India	11.1%	China	13.6%	China	6.3%
			Portugal	52.4%	France	18.8%	UK	12.5%	Ivory Coast	23.8%	Germany	9.8%	China	33.9%	South Africa	40.9%	China	15.5%	Kenya	17.5%	China	11.4%	South Africa	25.1%	South Africa	35.9%
		Country	STP	Senegal	Seychelles	Sierra Leone	South Africa	Sudan	Swaziland	Togo	Uganda	Tanzania	Zambia	Zimbabwe	Note: USA =

Table A .

 A 2. Data sources and Definition of the variables World Economic Outlook; WDI: World Development Indicators; FWS: annual Freedom in the World Survey; UNCTAD: United Nations Conference on Trade and Development.

	Primary data	Sources	Notation	Comments
	Current account balance	WEO Database (IMF)	CA	% of GDP
	Fiscal balance	WEO Database	FIS	Expressed as a ratio to GDP and measured
				relative to a weighted-average of the fiscal
				balance of country i's trading partners
	Net foreign asset to	Updated and extended version of	L.NFA	% of GDP
	GDP ratio (Lagged)	dataset constructed by Lane and
		Milesi-Ferretti (2011)
	Level of PPP-adjusted	WEO Database	RPROD	Relative to a weighted-average of
	GDP per capita			country i's trading partners
	Old-age dependency	WDI (The World Bank)	RO	Defined as the ratio of the population
	ratio			aged 65 and older to the working-age
				population, and measured relative to a
				weighted average of country i's trading
	Young dependency	WDI	RY	Defined as the ratio of the population
	ratio			aged 65 and older to the working-age
				population, and measured relative to a
				weighted average of country i's trading
	Population growth rate	WDI	RPOPG	Annual growth of total population
	Aging rate	Author's construction following		defined as the expected change in the
		Lane and Milesi-Ferretti (2012)		old-age dependency ratio in the future
				(constructed as the difference between the
				age dependency ratio in year t+20 and the
				ratio in year t, where the t+20 estimate is
				based on United Nations population
				projections)
	Trade openness	WDI	OPEN	(Exports + Imports)/GDP
	GDP growth rate	WEO Database	RGROWTH	Real GDP growth
	Oil balance	WEO	OILB	Oil trade balance in % of GDP
	Terms of trade	WDI	TOT	Index, price of exports/price of imports
	Migrants' Remittances	WDI	REM	Worker's remittances, receipts
	Real effective	UNCTAD Statistics and	REER	Real effective exchange rates are the
	exchange rate	author's calculation		weighted averages of bilateral exchange
				rates adjusted by relative consumer prices
	Note: WEO:

Table A .

 A 2. (continued)

	Primary data	Sources	Notation	Comments
	Financial integration	Updated and extended version	FINT	Sum of external assets and liabilities in
		of dataset constructed by Lane		% of GDP
		and Milesi-Ferretti (2011)
	Official Development	UNCTAD Statistics	ODA	Net official development assistance
	Assistance			(ODA), received, in % of GDP
	Foreign direct	UNCTAD Statistics	FDI	Foreign direct investment, net
	investment			inflows, in % of GDP
	OECD economic	OECD database	L.OECD_G	Average real economic growth rate
	growth (Lagged)			of OECD countries
	World real interest rate	IFS (IMF)	WRIR	World nominal interest rate adjusted
				by CPI percentage change for
				OECD countries
	Civil liberties	FWS	CLIB	Indexed between 1 (free)
				and 7 (no free)
	HIPC initiative	Author	HIPC	0 before HIPC initiative and
				1 after for recipient countries and
				0 for other countries

Note: WEO: World Economic Outlook; WDI: World Development Indicators; FWS: annual Freedom in the World Survey; UNCTAD: United Nations Conference on Trade and Development; OECD: Organisation for Economic Co-operation and Development.

This table contains 20 potential determinants of the current account on 21. The last determinant discussed in Section 2 is the relative income squared.

Table A

 A

			.3. Descriptive statistics
	VARIABLE	Obs	Mean	Std. Dev.	Min	Max
	CA	1451	-6.110	10.414	-125.670	40.825
	CLIB	1443	4.640	1.481	1.000	7.000
	FDI	1438	0.038	7.470	-46.422	145.330
	FINT	1387	22.306	20.333	2.455	133.816
	FIS*	943	9.107	17.730	0.060	179.068
	L.NFA	1419 211.438	46.028	95.307	341.091
	L.OECD_G	1452	52.581	15.997	23.278	104.673
	ODA	1439	1.265	1.157	-10.216	8.062
	OILB	1382	-5.347	3.100	-14.821	3.583
	OPEN	1408	2.279	18.267	-60.902	116.311
	RAGING	1452	69.947	39.625	3.983	263.877
	REER*	1386	11.667	11.430	-0.252	95.536
	REM	1161	3.941	11.071	0.000	106.412
	RGROWTH*	1420 112.410	48.171	0.000	357.576
	RO*	1452	2.965	6.113	-14.683	90.456
	RPOPG*	1452	126.977	71.208	25.909	768.291
	RPROD*	1402 157.513	355.983	0.000	7575.742
	RPROD2*	1402	2.073	1.095	1.136	5.696
	RY*	1452 464.033	148.088	100.000	700.000
	TOT	1372	-70.664	124.251	-541.032	1720.696
	WRIR	1452	2.550	1.551	-3.516	4.886
	Note: * for variable expressed in relative terms.

Table A .

 A 4. Classical regression results for full sampleStandard errors are in parentheses; *** (resp. **, *): significant at the 1% (resp. 5%, 10%) level.

			Annual data		5-year averaged data
	VARIABLES	Panel OLS 1 Panel OLS 2 Panel OLS 3	Panel OLS 1 Panel OLS 2 Panel OLS 3
	CLIB	0.00245			0.00607
		(0.00253)			(0.00443)
	FDI	-0.926***	-0.963***	-0.960***	-0.769***	-0.840***	-0.817***
		(0.0607)	(0.0497)	(0.0497)	(0.144)	(0.112)	(0.113)
	FINT	-0.00579***	-0.00619***	-0.00617***	-0.00669***	-0.00621***	-0.00667***
		(0.000867)	(0.000832)	(0.000831)	(0.00152)	(0.00141)	(0.00138)
	FIS	0.160***	0.173***	0.178***	0.251*	0.350***	0.353***
		(0.0407)	(0.0398)	(0.0399)	(0.129)	(0.119)	(0.118)
	HIPC	0.0138	0.0131**	0.0140**	0.0181
		(0.00897)	(0.00623)	(0.00625)	(0.0176)
	L.NFA	0.0179***	0.0192***	0.0193***	0.0265***	0.0248***	0.0256***
		(0.00302)	(0.00280)	(0.00280)	(0.00646)	(0.00584)	(0.00570)
	L.OECD_G	0.0946			0.694
		(0.183)			(0.616)
	ODA	-0.0834**	-0.112***	-0.114***	-0.0470	-0.157**	-0.136***
		(0.0371)	(0.0344)	(0.0344)	(0.0765)	(0.0612)	(0.0512)
	OILB	0.206***	0.242***	0.241***	0.168***	0.195***	0.189***
		(0.0248)	(0.0202)	(0.0202)	(0.0446)	(0.0356)	(0.0350)
	OPEN	-0.00379			-0.00528
		(0.0108)			(0.0197)
	RAGING	0.241			0.0993
		(0.199)			(0.351)
	REER	-0.0129			-0.0171
		(0.0104)			(0.0212)
	REM	0.333***	0.261***	0.260***	0.313***	0.206**	0.215***
		(0.0572)	(0.0456)	(0.0456)	(0.106)	(0.0810)	(0.0775)
	RGROWTH	-0.0424			-0.409***	-0.386***	-0.383***
		(0.0517)			(0.128)	(0.111)	(0.110)
	RO	-0.0466*			-0.0366
		(0.0262)			(0.0450)
	RPOPG	-0.527**	-0.463*	-0.465*	0.0142
		(0.264)	(0.245)	(0.245)	(0.553)
	RPROD	0.210***	0.0684***	0.0673***	0.205*		0.0451**
		(0.0673)	(0.0171)	(0.0171)	(0.121)		(0.0193)
	RPROD2	-0.122*			-0.104
		(0.0705)			(0.125)
	RY	0.0216			0.0183
		(0.0139)			(0.0238)
	TOT	0.0175**			0.0157
		(0.00858)			(0.0156)
	WRIR	-0.340			-0.205
		(0.304)			(0.766)
	TIME_DUM			-0.0193			-0.0171
				(0.0122)			(0.0118)
	CONSTANT	-0.0607	-0.0175*	-0.0167*	-0.101	0.00716
		(0.0417)	(0.00984)	(0.00985)	(0.0779)	(0.00879)
	OBS	711	727	727	175	178	178
	R-SQUARED	0.533	0.521	0.523	0.640	0.608	0.735

Table A .

 A 5. Classical regression results for 35 SSA countries from 1990Standard errors are in parentheses; *** (resp. **, *): significant at the 1% (resp. 5%, 10%) level.

			Annual data		5-year averaged data
	VARIABLES	Panel OLS 1 Panel OLS 2 Panel OLS 3	Panel OLS 1 Panel OLS 2 Panel OLS 3
	CLIB	-0.000867			0.00259
		(0.00246)			(0.00446)
	FDI	-0.605***	-0.619***	-0.609***	-0.597***	-0.667***	-0.662***
		(0.0719)	(0.0687)	(0.0676)	(0.136)	(0.110)	(0.106)
	FINT	-0.00401***	-0.00450***	-0.00463***	-0.00429***	-0.00534***	-0.00560***
		(0.000803)	(0.000756)	(0.000741)	(0.00144)	(0.00127)	(0.00123)
	FIS	0.476***	0.466***	0.475***	0.609***	0.621***	0.634***
		(0.0610)	(0.0592)	(0.0581)	(0.156)	(0.149)	(0.144)
	HIPC	0.00224			-0.0123
		(0.00895)			(0.0204)
	L.NFA	0.00972***	0.0122***	0.0131***	0.0150**	0.0189***	0.0202***
		(0.00290)	(0.00263)	(0.00260)	(0.00633)	(0.00545)	(0.00528)
	L.OECD_G	0.200			1.544**	1.181**	0.839
		(0.172)			(0.627)	(0.514)	(0.507)
	ODA	-0.0405			-0.0733
		(0.0350)			(0.0749)
	OILB	0.167***	0.185***	0.178***	0.133***	0.167***	0.162***
		(0.0240)	(0.0196)	(0.0192)	(0.0467)	(0.0349)	(0.0337)
	OPEN	-0.0212**	-0.0233**	-0.0233**	-0.0202
		(0.0105)	(0.0103)	(0.0101)	(0.0186)
	RAGING	0.164			-0.0695
		(0.188)			(0.334)
	REER	0.0169			0.0236
		(0.0130)			(0.0240)
	REM	0.273***	0.253***	0.233***	0.233**	0.185***	0.167**
		(0.0513)	(0.0472)	(0.0461)	(0.0931)	(0.0707)	(0.0685)
	RGROWTH	0.0147			-0.0213
		(0.0494)			(0.148)
	RO	-0.0450*	-0.0383*	-0.0296	-0.0382
		(0.0242)	(0.0218)	(0.0214)	(0.0427)
	RPOPG	-0.828***	-0.736***	-0.608***	-0.810
		(0.234)	(0.225)	(0.221)	(0.503)
	RPROD	0.232***	0.204***	0.194***	0.258**	0.0737***	0.0739***
		(0.0622)	(0.0528)	(0.0516)	(0.110)	(0.0227)	(0.0219)
	RPROD2	-0.150**	-0.118*	-0.104*	-0.198*
		(0.0647)	(0.0602)	(0.0587)	(0.113)
	RY	0.00937			0.00537
		(0.0131)			(0.0220)
	TOT	0.0144			0.00796
		(0.00931)			(0.0170)
	WRIR	-0.746**	-0.837***	-0.0554	-1.151
		(0.296)	(0.221)	(0.250)	(0.890)
	TIME_DUM			0.0495***			0.0329***
				(0.0118)			(0.0101)
	CONSTANT	-0.0441	0.00322	-0.0332**	-0.0753	-0.0600***	-0.0591***
		(0.0387)	(0.0130)	(0.0141)	(0.0724)	(0.0143)	(0.0138)
	OBS	598	600	600	144	144	144
	R-SQUARED	0.485	0.476	0.509	0.625	0.587	0.618

  Table A.6. Coefficients of the fundamentals based on Panel OLS estimations

			All the sample		Sample without outliers
	VARIABLES				(35 Countries from 1990 to 2012)
		Panel IV	Panel OLS 1 Panel OLS 2	Panel IV	Panel OLS 1 Panel OLS 2
		CA	CA	CA	CA	CA	CA
	FDI	-1.059***	-0.947***	-0.961***	-0.751***	-0.647***	-0.710***
		(0.160)	(0.0493)	(0.0490)	(0.130)	(0.0627)	(0.0624)
	FINT	-0.00830***	-0.00608***	-0.00641***	-0.00548***	-0.00493***	-0.00585***
		(0.00174)	(0.000830)	(0.000819)	(0.00180)	(0.000767)	(0.000755)
	FIS	0.638*	0.180***	0.188***	0.752***	0.468***	0.467***
		(0.356)	(0.0396)	(0.0396)	(0.139)	(0.0602)	(0.0613)
	L_NFA	0.0263***	0.0193***	0.0208***	0.0199***	0.0138***	0.0178***
		(0.00652)	(0.00280)	(0.00272)	(0.00733)	(0.00267)	(0.00257)
	ODA	-0.110**	-0.117***	-0.162***	-0.00750	-0.0520	-0.143***
		(0.0529)	(0.0335)	(0.0268)	(0.0862)	(0.0323)	(0.0264)
	OILB	0.175***	0.229***	0.227***	0.289***	0.177***	0.177***
		(0.0440)	(0.0197)	(0.0198)	(0.0438)	(0.0189)	(0.0193)
	REM	0.225***	0.278***	0.252***	0.348***	0.207***	0.164***
		(0.0719)	(0.0450)	(0.0436)	(0.0821)	(0.0408)	(0.0405)
	RPROD	0.0602***	0.0722***	0.0462***	0.0575*	0.0777***	0.0284***
		(0.0199)	(0.0161)	(0.0112)	(0.0349)	(0.0147)	(0.0105)
	CONSTANT	-0.0168	-0.0156**		-0.0603***	-0.0322***
		(0.0163)	(0.00700)		(0.0184)	(0.00684)
	Observations	681	727	727	561	600	600
	R-squared	0.503	0.516	0.620	0.473	0.448	0.558
	Number of id	44	44	44	35	35	35

Note: Robust standard errors are in parentheses; *** significant at the 1% level, ** significant at the 5% level, * significant at the 10% level.

  Table A.7. Correlation between the estimated current-account gaps Table A.8. Classification of exchange rate regime

	IRR Fine classification		code		IMF official classification	code
	No separate legal tender		1		No separate legal tender	1
	Pre announced peg or currency		2		Pre announced peg or currency	1
	board arrangement				board arrangement
	Pre announced horizontal band that	3		Pre announced horizontal band that	1
	is narrower than or equal to +/-2%				is narrower than or equal to +/-2%
	De facto peg		4		De facto peg	1
	Pre announced crawling peg		5		Pre announced crawling peg	2
	Pre announced crawling band that		6		Pre announced crawling band that	2
	is narrower than or equal to +/-2%				is narrower than or equal to +/-2%
	De factor crawling peg		7		De factor crawling peg	2
	De facto crawling band that is		8		De facto crawling band that is	2
	narrower than or equal to +/-2%				narrower than or equal to +/-2%
	Pre announced crawling band that		9		Pre announced crawling band that	3
	is wider than or equal to +/-2%				is wider than or equal to +/-2%
	De facto crawling band that is		10		De facto crawling band that is	3
	narrower than or equal to +/-5%				narrower than or equal to +/-5%
	Moving band that is narrower than or	11		Moving band that is narrower than or	3
	equal to +/-2 % (i.e., allows for both			equal to +/-2 % (i.e., allows for both
	appreciation and depreciation over time)			appreciation and depreciation over time)
	Managed floating		12		Managed floating	3
	Freely floating	CA imbalances 13	BMA	OLS Freely floating IV	4
	Freely falling	BMA OLS	14	1.000 0.998	0.999 1.000 Freely falling 0.942 0.944	5
		IV	0.917 Our own classification 0.913	1.000
	Regime		IRR code	Regime		IMF code
	Fixed exchange rate regime		1, 2, 3, 4	Fixed exchange rate regime	1, 2
	Intermediate exchange rate regime		5, 6, 7, 8,	Intermediate exchange rate regime	3
			9, 10, 11
	Flexible exchange rate regime		12, 13, 14	Flexible exchange rate regime	4, 5

See, for example,[START_REF] Guillaume | Improving policy credibility: is there a case for African monetary unions?[END_REF],[START_REF] Chang | Exchange-rate policy for developing countries[END_REF],[START_REF] Frankel | Choosing an Exchange Rate Regime[END_REF],[START_REF] Qureshi | Hard or soft pegs? Choice of exchange rate regime and trade in Africa[END_REF].

Throughout this paper, the terms "current account imbalances" and "external imbalances" are used interchangeably.

Most of these studies focus on current account reversal[START_REF] Clarida | Are there thresholds of current account adjustment in the G7?[END_REF][START_REF] Freund | Current account adjustment in industrial countries[END_REF][START_REF] Freund | Current account deficits in industrial countries: the bigger they are, the harder they fall? NBER Chapters[END_REF] or the link between the latter and the exchange rate regime[START_REF] Chinn | A faith-based initiative meets the evidence: does a flexible exchange rate regime really facilitate current account adjustment[END_REF][START_REF] Ghosh | Is the exchange rate regime really irrelevant for external adjustment[END_REF][START_REF] Pancaro | Current account reversals in industrial countries. Does the exchange rate regime matter? European Central Bank[END_REF].

This technique is briefly presented in this paper. For more technical details, we refer the reader to some key references, such as[START_REF] Hoeting | Bayesian model averaging for linear regression models[END_REF][START_REF] Hoeting | Bayesian model averaging: a tutorial[END_REF] and[START_REF] Fernandez | Model uncertainty in cross-country growth regressions[END_REF].

We initially considered the 48 sub-Saharan African countries (according to the World Bank) and selected the 44 countries for which data on the current account were available over our whole period. The four countries that are not included are Eritrea, Liberia, Somalia and South Sudan.

Given that the choice of prior may influence the results, we have specified other types of priors (fixed and random with different sizes 5, 7, 9,

11). The results do not significantly change (see Figure A.1. in the Appendix).

This negative relationship is also maintained even if we take the ratio of M2 to GDP as a proxy for financial deepening, but the latter does not appear statistically significant.

The nine countries excluded from the initial sample are Chad, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Malawi, Mauritania, Sao Tome and Principe, Zambia and Zimbabwe.

Other methods are common in the literature, such as instrumental variables and the generalized method of moments. However, after presenting their advantages and disadvantages, Ca' Zorzi et al. (2012) conclude, as do[START_REF] Chinn | Medium-term determinants of current accounts in industrial and developing countries: an empirical exploration[END_REF], that it is more appropriate to use simple pooled OLS on non-overlapping average data in the analysis of medium-term determinants of the current account.

Moreover, the correlation coefficient between national savings and the young-age-dependency ratios are

We do not use five-year averaged data, which have the characteristic of smoothing fluctuations in the economic cycle, as this is worthwhile in identifying the underlying determinants of the current account. However, in the analysis of external imbalances, we cannot ignore economic fluctuations. Moreover, the fundamentals identified from the annual data are broadly similar to those identified with the five-year averaged data. Thus, using these same fundamentals here, we maintain consistency with the previous results.

Note that we clearly cannot introduce all three regimes simultaneously because of collinearity. Regimes are introduced in pairs, the third regime being used for identification in the equation (see[START_REF] Dubas | The importance of the exchange rate regime in limiting misalignment[END_REF]. In equation (9), for example, REGM 3 is used for identification. We then alternate between exchange rate regimes to check the robustness of our results.

APPENDIX

Given that the sum of trade flows with the first ten trading partners is less than 100%, the different weights were then normalized to 100%.