The External Balance Assessment Methodology: 2018 Update1
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The assessment of external positions and exchange rates is a key mandate of the IMF. This paper presents the updated External Balance Assessment (EBA) framework—a key input in the conduct of multilaterally-consistent external sector assessments of 49 advanced and emerging market economies—following the two rounds of refinements adopted since the framework was introduced in 2012 (as described in Phillips et al., 2013). It also presents new complementary tools for shedding light on the role of structural factors in explaining external imbalances and assessing potential biases in the measurement of external positions. Remaining challenges and areas of future work are also discussed.


The assessment of external positions and exchange rates is a key mandate of the IMF. This paper presents the updated External Balance Assessment (EBA) framework—a key input in the conduct of multilaterally-consistent external sector assessments of 49 advanced and emerging market economies—following the two rounds of refinements adopted since the framework was introduced in 2012 (as described in Phillips et al., 2013). It also presents new complementary tools for shedding light on the role of structural factors in explaining external imbalances and assessing potential biases in the measurement of external positions. Remaining challenges and areas of future work are also discussed.

I. Introduction

The assessment of external positions and exchange rates is a key mandate of the IMF. Often, current account imbalances can be appropriate, even necessary. For example, countries whose populations are aging rapidly may need to accumulate external savings (by running current account surpluses) that they can draw from when their workers retire. On the flip slide, young and rapidly growing economies with ample investment opportunities benefit from foreign funding and can afford to run current account deficits provided they can repay them out of future income. However, there are times when these external imbalances reflect macroeconomic and financial vulnerabilities. Countries that accumulate external liabilities on too large a scale may become vulnerable to sudden stops in capital flows and financial crises, with negative effects that extend beyond their borders. History offers important examples—the Great Depression and the Global Financial Crisis—when these imbalances led to deep and protracted disruptions at the global level. The IMF plays a role in alerting the global community of potential balance of payments stresses and in providing policy advice to reduce such risks.

So how does the IMF conduct external assessments? Although staff have conducted external assessments since the IMF’s inception, it was not until the early 1990s that assessments became informed by a multilaterally-consistent, model-based, framework. This framework has naturally evolved over time, building on insights gained from experience, feedback from stakeholders and experts, improvements in data availability, and methodological innovations. Initially, assessments were based on the framework of the Consultative Group on Exchange Rates (CGER), which focused on exchange rates of key advanced economies, evolving over time to include a wider country coverage and a broader range of measures and drivers of a country’s external position.

The External Balance Assessment (EBA) framework, which built on its CGER predecessor, was launched in 2012 with the development of new current account and real effective exchange rate (REER) models.2 The key innovations of the EBA framework included: (i) expanding the set of policy variables that affect external balances; and (ii) defining the concept of “norms” as the level of the current account or real exchange rate consistent not only with fundamentals but also with policies at their “desired” levels. These innovations improved the identification of the role of macroeconomic policies in driving excess external imbalances, better informing staff’s overall policy advice. In addition, the EBA framework included a richer model-based approach for removing cyclical and temporary factors from the current account balance in order to assess a country’s underlying external balance. The framework continued to rely on the external sustainability approach for assessments in cases where risks arising from a large net debtor position were relevant.

The first refinements to the 2012 EBA models were introduced in 2015. In addition to data updates, these mainly entailed: (i) revisions to the modeling of demographic factors to capture their nonlinear effects on the current account; and (ii) introducing another REER model to understand persistent differences in the level of the real exchange rate across countries.3

In 2018, additional refinements were implemented. These focused primarily on the current account model and were aimed at strengthening the modeling of some key fundamentals (demographics and institutional quality), macroeconomic policies (foreign exchange intervention and credit excesses), and country-specific features (role of financial centers). REER models remained generally unchanged, although some aspects were refined to ensure comparability and consistency with the changes to the current account model. In addition, complementary tools were developed to provide further insights into the potential role of structural factors in driving external imbalances, as well as to better understand and estimate possible measurement biases in current account statistics.4

It is worth stressing that while the EBA models provide key numerical inputs for the identification of external imbalances, in some cases they may not capture all relevant country characteristics and potential policy distortions. As such, external assessments naturally need to be complemented by country-specific knowledge and insights. To integrate country-specific judgement in an objective, rigorous and evenhanded manner, a process was created for arriving at multilaterally-consistency external assessments for a subset of the largest 30 economies, representing about 90 percent of global GDP (see discussion in the next section). These assessments are not only presented in the individual annual Article IV consultations but also in the annual External Sector Report (ESR), which discusses the risks from the configuration of global excess imbalances and policies to address them in a manner supportive of global growth.

This paper presents the latest generation of the EBA models, reflecting the refinements conducted in 2015 and 2018. It borrows heavily from Phillips et al. (2013) as well as from other IMF Board documents that describe earlier methodological changes. The paper is organized as follows: Section II provides an overview of the Fund’s external sector assessment framework and the combined role that models and judgment play in arriving at multilaterally-consistent assessments. Sections III and IV present the latest vintage of the EBA current account and REER models, respectively. Section V describes the process used to arrive at the norms and gaps for current account and the real exchange rates; and Section VI discusses different methods to estimate exchange rate semi-elasticities that help to map current account gaps into real exchange rate gaps. The External Sustainability approach is explained in Section VII; while Section VIII describes the complementary tools to shed light on the potential role of structural policies and on possible measurement biases in external sector statistics. Section IX concludes with a brief discussion on remaining challenges and areas for further work.

II. The External Sector Assessment Framework

There are good reasons for countries to run current account surpluses and deficits at certain points in time; for example, to smooth out the effect of temporary shocks or to allow capital to flow from countries where it is more abundant to countries where it is scarcer. Thus, the main challenge when conducting external assessments is to determine how much of an external surplus (or deficit) is appropriate and how much is an “excess surplus (or deficit)” relative to a given country’s fundamentals and desired policies over the medium term. Because there are many complex drivers of current account balances and exchange rates, no single model is likely to give the right answer in identifying excess imbalances for every country.

Recognizing the natural shortcomings of numerical inputs, the introduction of the EBA models was accompanied by a process for the conduct of external assessments (see Figure 1), under which numerical inputs from the various EBA models are combined with analytically-grounded, country-specific judgment. This judgement often involves considering multiple external sector and competitiveness indicators (e.g. evolution of real unit labor costs, export and import performance), as well as results of the External Sustainability approach in cases where the dominant source of concern is the size and composition of its international investment position (IIP). Year-round discussions take place between country teams and an interdepartmental External Sector Coordinating Group, who is responsible for vetting country team assessments and ensuring that the final assessments for the largest 30 economies covered in the External Sector Report are multilaterally consistent. These assessments provide an important input for arriving at policy recommendations at both the bilateral and multilateral level so that all countries—with either excess surpluses or deficits—address these imbalances in a manner that does not compromise stability and growth at both the country and global level.

Figure 1.
Figure 1.

The IMF’s Current External Assessment Framework

Citation: IMF Working Papers 2019, 065; 10.5089/9781498300933.001.A001

III. The EBA Current Account Model

The EBA current account model builds on the extensive literature on the macroeconomic determinants of saving and investment decisions.5 The current version of the model is guided by the same principles as the original EBA methodology (Phillips et al., 2013), including by specifying most regressors as deviations from the GDP-weighted global average.6 This implies that, for instance, population aging will affect a country’s current account balance only to the extent that this country is aging faster or slower than the world average. Similarly, the fiscal balance affects the current account only to the extent that other countries maintain different fiscal balances. This approach ensures multilateral consistency and allows for a decomposition of the effect of a certain policy variable on a given country’s current account into its domestic and foreign component. Current account determinants are selected based on their conceptual underpinning and on whether the estimated coefficients are consistent with the theoretical priors, although for policy variables there is generally a higher bar since coefficients are also required to be statistically significant.

A. Sample and Estimation Method

The 2018 version of the current account model is estimated for a sample of 49 countries using annual data for the 1986–2016 period.7 The estimated 2018 model not only includes longer time series but also data revisions, including the migration of external statistics data to the IMF’s Balance of Payments Manual, 6th edition (BPM6) and new demographic estimates and projections (2017 Revision of UN World Population Prospects).

The current account model is estimated using a pooled Generalized Least Squares (GLS) method with a panel-wide AR(1) correction due to the autocorrelation of the current account data. Country fixed effects are not included since they do not provide an economic explanation of observed current account balances and may simply pick up policy distortions that have persistent effects. Similarly, the model does not include the lagged current account, despite its statistical significance (see Lee et al., 2008, and Calderón et al., 2002), since this would not explain the desirability of current account persistence. These two econometric choices inevitably penalize model fit when compared to other studies but are necessary for a normative interpretation of the results. Finally, to deal with endogeneity and reverse causality issues, some policy variables (fiscal policy and foreign exchange intervention) are instrumented.8 Other country fundamentals (net foreign asset position, productivity, global financial conditions) and policies (health spending) are lagged. Further details on the treatment of the different determinants are provided below.

B. Regression Model and Results

This subsection describes the regressors included in the current account model, provides the theoretical justification for their inclusion, and discusses their estimated impact on the current account. The dependent variable is the CA-to-GDP ratio. The determinants are grouped into cyclical factors; fundamentals (macroeconomic and structural); and policy variables. Regression estimates are presented in Table 1, while Table 2 compares these estimates to earlier versions of the model. Annex I includes a qualitative description of the refinements introduced since 2013, and the text, highlights situations when the size and statistical significance of the coefficient vary substantially across versions.9 Meanwhile, Annexes II and III describe the data sources and definitions of the variables included in the regression models.

Table 1.

EBA Current Account Regression, Dependent Variable CA/GDP

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Standard errors in parenthesis. * significant at 10%; ** significant at 5%; *** significant at 1% # sign means variable is included in differences from (GDP‐weighted) world counterpart. Capital account openness is calculated as one minus capital control index.
Table 2.

EBA Current Account Regression Results, 2013 and 2015 Models

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* significant at 10%; ** significant at 5%; *** significant at 1% # sign means variable is included in differences from (GDP‐weighted) world counterpart. Capital account openness is calculated as one minus capital control index.

Cyclical Factors

Temporary and cyclical factors can substantially impact current account fluctuations. Thus, their estimated effects need to be stripped from the actual current account balance to derive a 'cyclically-adjusted’ measure—i.e., that would prevail over the medium term—that can be compared to the medium-term current account norm (see also Section V). These transitory factors include:

Output gap. Current account levels tend to reflect the state of the business cycle, as weak domestic demand—reflected in negative output gaps—leads to higher saving and lower investment. The output gap, measured in percent of potential GDP, is used as a regressor to capture this. As most other variables in EBA, the output gap is measured in relative terms with respect to the world average to account for differential effects when business cycles are not synchronized. The estimated coefficient indicates that an increase in the relative output gap of 1 percent reduces the current account balance by about 0.35 percentage points of GDP.

Commodity terms-of-trade gap (interacted). Short-term fluctuations of terms of trade, especially of commodities, are expected to affect the current account as the associated temporary income gains (losses) are normally matched by higher saving (dissaving). The commodity terms-of-trade are measured as the ratio of a geometric weighted-average price of 43 commodity export categories to the equivalent geometric weighted-average price of commodity imports, each relative to manufactured goods prices in advanced economies (see further details in Annex III). The model includes the deviations of this index from its trend—to capture the temporary component—interacted with trade openness. The estimated coefficient suggests that a 10 percent temporary terms-of-trade improvement is associated with a 0.8 percentage points of GDP increase in the current account balance for a country with an openness degree of 50 percent of GDP.10

Macroeconomic Fundamentals

The 2018 EBA specification preserves many of the macroeconomic fundamentals included in earlier versions of the model (see Phillips et al., 2013) as well as in the CGER’s macro-balance approach (see Lee et al., 2008). These include:

Net foreign assets (lagged). In general, countries with larger Net Foreign Asset (NFA) positions tend to exhibit higher current account balances. As in the CGER and earlier EBA specifications, the lagged NFA-to-GDP ratio is included to account for its effect on the net income balance. Such effect partly captures measurement issues associated to the treatment of (nominal) interest income and retained earnings on portfolio equity positions (see Section VIII for more details). The estimated coefficient of 0.023, somewhat higher than in the earlier EBA versions, suggests that empirically, higher NFA and income balances are not fully offset by a lower trade balance.11 The linear relationship between the NFA and the current account does not hold at large negative NFA levels, as large debtor countries need to adjust their stock positions by running higher current account balances. To account for this non-linear effect, the model also includes a dummy variable for countries with an NFA position below -60 percent of GDP (interacted with the NFA-to-GDP ratio itself). The associated coefficient is negative, as expected.

Output per worker (lagged). Richer countries, with already higher capital-labor ratios, are expected to export capital to poorer countries by running higher-than-otherwise current account balances, while the opposite would be expected for poorer economies (see Chinn and Prasad, 2003; Lee et al., 2008). To measure this effect, and given constraints on reliable capital-labor ratio data, a country’s GDP per working age population (in PPP terms) is compared to the average of the top three economies (Germany, Japan and the United States), which are taken as the frontier. The variable is also interacted with the capital account openness policy variable (see below for more discussion) as the flow of capital from richer to poorer countries depends on the degree of capital mobility. Results suggest that a 10 percent increase in relative output per worker would increase the current account balance by about 0.64 percent of GDP in fully open economies.

Expected real GDP growth (5 years ahead). This variable is a determinant of both investment and savings. Higher expected output growth is likely to lead to higher investment, in anticipation of higher returns to capital, as well as higher consumption and lower saving to the extent that households engage in consumption smoothing. Both effects operate in the same direction (of higher growth leading to lower current account balances). Real GDP growth 5 years ahead from the World Economic Outlook (WEO) is used to proxy for expected growth. Results indicate that an increase of 1 percentage point in expected real growth lowers the current account by about 0.3 percent of GDP.

Reserve currency status. Countries that issue reserve currencies, especially the United States, tend to benefit from what is called an “exorbitant privilege”. This broadly refers to the effect of the global demand for safe assets on the reserve currency issuer’s funding costs, which tends to tilt consumption towards the present, and leads to higher investment. Global demand for reserve assets also tends to appreciate the currency of reserve issuers. These effects unambiguously weaken reserve currency issuers’ current accounts. To capture this effect, as in earlier model versions, a measure of the share of a country’s currency in world reserve holdings is included. The estimated coefficient suggests that for each 10 percentage points of global reserves held in its currency, a country’s current account balance is weakened by about 0.3 percent of GDP.

Structural Fundamentals

Demographics. The current demographic specification, compared to earlier versions, focuses on disentangling static effects (associated with the age composition of a country’s population) from dynamic effects (associated with longevity or old age survival risk). Generally, countries with a relatively high share of young or a high share of elderly tend to dissave, while countries with a higher proportion of prime-aged savers will tend to save more. The age-composition effect is captured through the inclusion of three variables: population growth (which partly proxies the share of young), the old-age dependency ratio (OAD), and the share of prime-aged savers as a proportion of the total working age population. The sign of the estimated coefficients aligns with economic priors. The dynamic effect is captured by the life expectancy of a current prime-aged saver as well as its interaction with future (20 years ahead) OAD. The intuition, based on the findings of a life-cycle model, is that countries save more when prime-aged savers expect to live longer (or have longer retirement periods), and more so when they cannot rely on future generations for old-age support. The estimated life expectancy term and its positive interaction with future OAD capture the non-linearities observed in the reduced-form relationship between life expectancy and the current account balance. Annex V and Dao and Jones (2018) provide further details on the current demographic specification.12

Institutional quality. In line with the vast literature that points to the quality of institutions as a key determinant of a country’s ability to finance current account deficits, the model includes an institutional quality proxy based on information compiled by the International Country Risk Guide (ICRG).13 Compared to the earlier versions of the model, the current indicator uses a broader range of institutional, social and political risk attributes that are considered important in saving and investment decisions.14 Results indicate that a country at the 75th percentile of the institutional quality distribution would have, all else equal, a 0.5 percentage points of GDP lower current account balance compared to the median country.

Exhaustible oil and natural gas resources. Exporters of natural resources are expected to save a portion of their export income for inter-generational equity reasons thus leading to, other things equal, higher current account balances. The fraction of natural resource exports that is saved often depends on the temporariness of this source of income (that is, countries would save more, the more temporary this income is). Thus, the model includes a variable that combines the size of the oil and natural gas balance, in percent of GDP, and a measure of its degree of temporariness based on the ratio of current extraction to proven reserves (see Annex VI for additional details).15 The estimated coefficient implies that a 1 percent of GDP increase in the “temporariness-adjusted” energy balance increases the current account balance by about 0.31 percent of GDP. This coefficient applies to 10 out of the 49 economies in the sample, where the net oil and gas balance is positive.

Policy Variables

Fiscal policy (instrumented). The relationship between fiscal policy and the current account has been extensively documented in the literature.16 An increase in government spending leads to higher domestic demand and, for a given level of output, to a lower current account balance. The magnitude of such effect depends, among other things, on the extent of private sector offset. If Ricardian equivalence holds, private consumption would tend to offset the change in government spending in anticipation of future changes in taxes (necessary to satisfy the government’s intertemporal budget constraint), in which case the effect on the current account would tend to be only partial. On the other hand, fiscal policy may also have supply-side effects that can be expansionary (via public investment) or contractionary (if fiscal policy entails changing distortionary taxes). Like in earlier versions, the fiscal policy variable is measured by the cyclically-adjusted general government overall balance. In addition, to overcome endogeneity issues, this measure of fiscal policy is instrumented using lags for relevant global factors (world real GDP growth, world output gap, world cyclically-adjusted fiscal balance, and global risk aversion, which is proxied by the U.S. corporate credit spread), as well as country-specific features (lagged GDP per capita, lagged output gap, the exchange rate regime, and a democracy index ranking). The estimated coefficient of 0.33 is comparable to those found in the literature that considers both advanced and emerging economies (Coutinho et al. 2018, Phillips et al. 2013, Lee et al., 2008).

Health spending (lagged). The generosity of the social safety net can have important bearings on aggregate saving due to precautionary motives. While there is no unique way of measuring the degree of social safety net provision, the level of public health spending relative to GDP provides a good empirical proxy, with consistent data for the estimation period and cross-country sample.17 The health spending variable is included in the model with a lag to deal with potential endogeneity issues. The estimated coefficient indicates that an increase in public health expenditures of 1 percent of GDP reduces the current account by an average of about 0.4 percentage points of GDP.

Foreign exchange intervention (interacted and instrumented). Interventions in the foreign exchange rate market can have important effects on the exchange rate and, thus, the current account, although these would depend on the degree of capital mobility, as documented in the extensive literature on the subject. To capture this, the EBA model includes as a regressor the FXI-to-GDP ratio, interacted with the Quinn index of capital controls (see below). FXI is proxied by the transaction-based change in reserves, as recorded in balance of payments (BOP) statistics—or, in a few cases where BOP data are not available, the change in the stock of reserves—plus comparable operations in derivatives markets.18 This broad measure of FXI builds on the notion that on- and off-balance sheet foreign exchange operations have similar effects on exchange rates and current accounts (see IMF, 2014; and Nedeljkovic and Saborowski, 2017). FXI is also instrumented to address endogeneity issues.19 The estimated effect of FXI under the refined model is larger than in earlier versions, and more in line with theoretical predictions and recent empirical studies (e.g., Bayoumi et al, 2015; Gagnon, 2017). Specifically, the results indicate that a 1 percent of GDP in FX purchases leads to a 0.19 percent of GDP improvement in the current account for a country in the 75th percentile of the distribution of capital controls index (compared to 0.11 under the earlier EBA specification) and 0.38 for a country at the 90th percentile (compared to 0.22).

Financial excesses. A large body of research shows that the current account deteriorates and the REER appreciates in countries that experience credit booms, with the opposite occurring during credit busts.20 To capture the role of financial excesses, an updated credit gap measure that draws on recent advances in the literature and the Bank for International Settlements (BIS) methodology (Drehmann et al., 2011) is employed. Specifically, a one-sided Hodrick-Prescott (HP) filter is applied to the credit-to-GDP ratio, using a large penalty parameter that takes into account that financial cycles have a longer duration than the real business cycles.21 Results imply that a 10 percent of GDP increase in credit relative to its trend (or credit gap) would be associated with a 1 percent of GDP deterioration in the current account. The estimated coefficient is highly significant, suggesting that the “financial cycle” has an independent effect on the current account, above and beyond the business cycle proxied by the output gap and other fundamentals and policies. Earlier versions of the model proxied credit excesses as deviations of a country’s credit-to-GDP ratio from its historical mean. Among other shortcomings, the previous proxy did not always adequately isolate the financial cycle nor recognize the existence of low-frequency drivers. This often led to large and permanent deviations of credit from its historical average, which were not necessarily related to financial excesses.

Capital controls (interacted). The measure of the degree of capital mobility is based on the Quinn index of capital controls (ranging from 0 in the case of full mobility to 1 in the case of no mobility).22 As in earlier versions, the capital control index is not included as an independent regressor but, instead, interacted with other fundamental variables (income per capita, and global risk aversion) and policy variables (FXI) consistent with the notion that differences in fundamentals translate into current account imbalances only to the extent that capital mobility allows it. The capital controls regressor is not significant when included independently.

  • Output per worker (lagged). The theoretical prediction that capital would flow from richer to poorer countries applies only to the extent that capital is mobile across countries. The interacted term between output per worker and capital account openness (defined as 1 minus the capital control index) captures this. Its estimated coefficient is statistically significant, with the expected positive sign and similar in magnitude to earlier model versions. The implied effect of a 10 percent increase of output per worker is about 0.6 percent in a country with fully open capital account, and 0.5 percent in a country at the 75th percentile of the capital controls index.

  • Global risk aversion (lagged). Heightened global risk aversion tends to lead to increased precautionary savings and lower investment in most economies, except in reserve currency countries, although the impact depends on each country’s degree of capital account openness. To capture this effect, the VXO index—the model’s global risk aversion proxy, which is expressed in terms of deviations from its historical average—is interacted with capital account openness.23 Results suggest that a 10 percent increase in global risk aversion would lead to a 0.17 percent of GDP increase in the current account balance of a non-reserve currency country with an average degree of capital account openness. To capture the offsetting safe-haven effect, the VXO is also interacted with a combination of capital account openness and the share of a country’s currency in world reserves. The corresponding estimated coefficient is small, however, both in quantitative and statistical significance terms.24

C. Model Fit and Robustness

The goodness of fit of the current model is generally in line with similar reduced form approaches, and somewhat stronger relative to earlier versions of the model.25 The model is also generally robust to different specifications or proxies of key variables, including institutions, credit excesses, demographics and foreign exchange intervention.

Institutional quality

The appropriateness of the ICRG as an indicator of institutional quality was assessed and compared against another widely-used institutional proxy, the Worldwide Governance Indicators (WGI). The latter are compiled by staff from the World Bank, the Natural Resource Governance Institute, and the Brookings Institution, and are based on multiple surveys of companies, citizens and experts. Since reliable WGI data are only available starting in 2002, alternative ways of merging both indicators were considered, including: (i) having both the WGI and ICRG proxies covering different periods (i.e. WGI from 2002 onwards and ICRG prior to 2002, with zero values elsewhere); and (ii) using the WGI proxy from 2002 and extending the series backwards using the average country-specific relationship between the ICRG and WGI for 2002–16. As shown in Table 3, the results of the baseline model, which are based on the broader set of ICRG indicators, are similar to the two alternative versions combining the ICRG and WGI—the model fit and statistical significance of coefficients generally coincide. The selection of the ICRG as the institutional proxy reflected both its wider cross-country and time-series coverage as well as its broader range of relevant institutional indicators.

Table 3.

EBA Current Account Regression Results, Robustness on Institutions

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Standard errors in parenthesis. * significant at 10%; ** significant at 5%; *** significant at 1% # sign means variable is included in differences from (GDP‐weighted) world counterpart. Capital account openness is calculated as one minus capital control index.

Financial excesses

Alternative ways to proxy for this variable were considered. These included: (i) computing the credit gap with a larger penalty parameter in the Hodrick-Prescott filter (25,000 instead of 1,600); (ii) using the earlier specification where the credit gap is defined as the credit-to-GDP relative to each country’s historical mean; and (iii) using the change in the credit-to-GDP ratio, with different lags, as a predictor of financial instability and external imbalances (see Jordá et al., 2011). The results of robustness analysis (see Table 4) show that while a larger HP filter penalty would not alter the fit of the model, it would come at the expense of increasing the volatility of the cyclical component of credit and lowering the estimated parameter. Meanwhile, reverting to the earlier demeaned credit specification would significantly reduce the model’s fit (the RMSE increases to 3.3 percent). When including both the demeaned and the detrended measures, the coefficient on demeaned credit turns small and statistically insignificant while the detrended coefficient is unchanged. Finally, when using the change in the credit-to-GDP ratio (both current year and one-year lagged) to proxy for financial excesses, the fit of the model is similar relative to the baseline specification and the coefficients are significant and in the right direction, confirming economic priors that sustained periods of high credit growth have a negative impact on the current account.26 The one-sided HP filter credit gap specification was ultimately selected given its superior fit, and its ability to measure the impact that sustained financial imbalances can have on the current account.27

Table 4.

EBA Current Account Regression Results, Robustness on Credit

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Standard errors in parenthesis. * significant at 10%; ** significant at 5%; *** significant at 1% # sign means variable is included in differences from (GDP‐weighted) world counterpart. Capital account openness is calculated as one minus capital control index.


Additional robustness exercises were also performed for the demographic block (see Table 5). First, the life expectancy variable was replaced by the aging speed variable, used in earlier EBA versions and other modeling work (Lane and Milesi-Ferretti, 2001). The results show that the estimated coefficients on aging speed, as well as that of all other demographic variables (old-age dependency ratio, population growth and the share of prime-aged savers), turn statistically insignificant (Column 2). This is not surprising since the aging speed variable confounds different forces in one indicator and is highly correlated with the prime-aged saver share variable. In addition, alternative specifications for the nonlinear effects of life expectancy (captured through the interaction of life expectancy with future OAD) were considered. While the coefficient on the squared life expectancy term is significant on its own (column 3), it becomes insignificant when the interaction term between life expectancy and future OAD is also included (column 4). Not only does the coefficient of interaction between life expectancy and future OAD remain significant, but also those of other regressors, suggesting nonlinear effects are best captured by this new theoretically-based interaction term.

Table 5.

EBA Current Account Regression Results, Robustness on Demographics

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Standard errors in parenthesis. * significant at 10%; ** significant at 5%; *** significant at 1% # sign means variable is included in differences from (GDP‐weighted) world counterpart. Capital account openness is calculated as one minus capital control index.

Foreign Exchange Intervention

Sensitivity analysis was conducted to explore the implications of each aspect of the refinements (i.e., broadening the definition of FXI to encompass derivatives and implementing a simpler instrumentation to prevent overfitting). When the effect of FXI is not instrumented, the estimated coefficient is still statistically significant but considerably smaller, indicating that endogeneity leads to a downward bias (see Table 6, column 3) as it would be expected when FXI responds predominantly to capital flow shocks (as opposed to current account variations). Results, however, varied depending on the definition of FXI. A narrow FXI definition—that only encompasses spot interventions—delivers a somewhat larger coefficient, possibly indicating that spot and derivative operations often offset each other (column 4). Irrespective of the latter and given the growing importance of off balance sheet FXI operations, a broader measure of FXI is necessary to properly capture the role of these policies in driving external imbalances.

Table 6.

EBA Current Account Regression Results, Robustness on Foreign Exchange Intervention

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Standard errors in parenthesis. * significant at 10%; ** significant at 5%; *** significant at 1% # sign means variable is included in differences from (GDP‐weighted) world counterpart. Capital account openness is calculated as one minus capital control index.

IV. EBA Real Exchange Rate Models

This section presents the two EBA real effective exchange rate (REER) models: the REER-Index and REER-Level regressions. The latest version of the models builds on past IMF work, including Phillips et al. (2013) and Mano et al. (2018), as well as the ample literature on the subject.28 As explained throughout the section, the two REER models build on the EBA current account model yet capture distinct aspects of the data. Figure 2 summarizes the regression specification across the EBA models.

Figure 2.
Figure 2.

EBA Models: Summary of Explanatory Variables in Current Account and REER

Citation: IMF Working Papers 2019, 065; 10.5089/9781498300933.001.A001

Note: Lagged variables have an L superscript, while those interacted have an X superscript. Some policy variables (fiscal, FXI) are instrumented as well. The REER-Index model includes country fixed effects (FE).

The REER-Index model focuses on the country-specific determinants of movements in REER indices.29 A main limitation of index data, which typically are normalized to a value of a 100 in the base year, is that they do not provide information on how a country’s exchange rate level compares relative to other countries at any point in time. Therefore, the estimation requires the use of country fixed effects, which implies that the model residuals of each country average zero over the sample period. Thus, this specification does not allow for persistent deviations of the exchange rate from the level “consistent with fundamentals and desired policies”.

In contrast, the REER-Level model aims at understanding differences in real exchange rate levels across countries, shedding light on possible persistent deviations from equilibrium levels across countries. The model was introduced in 2015 and builds on the work by Bergstrand (1991), who established a positive cross-country correlation between REER levels and GDP per capita, the so called “Penn effect”. This relationship reflects not only supply-side factors—productivity differentials (the Balassa-Samuelson effect) and relative factor endowments (the Kravis-Lipsey-Bhagwati effect)—but also demand-side factors—non-homothetic preferences that reflect differences in consumption smoothing patterns across countries.

The REER-level variable is constructed in two steps, combining cross-sectional information from PPP exchange rates as well as information across time contained in REER indices. In the first step, REER level cross-country data from the World Bank International Comparison Program (ICP) is used to compute price levels relative to the United States for the base year (2011). In the second step, the REER-level data is extended across the sample period (1990–2016), using REER indices re-scaled to their base year value. The rescaling of the index ensures that the basket of goods used to compute the REER level is comparable over time.30 The data, however, present certain challenges. In particular, base-year ICP data could be subject to measurement uncertainty (see e.g., Deaton and Heston, 2010), with the potential of affecting the constructed REER level for the sample period (and to a lesser extent, the REER of its trading partners).

A. Sample and Estimation Method

Reflecting current data constraints, the REER models are estimated for the period 1990–2016 and for a sample of 40 (39) countries in the index (level) regression, compared to the 49 included in the EBA current account regression. As with the current account model, most REER determinants are expressed as deviations from each country’s trading partners weighted average, some regressors are lagged to address endogeneity concerns, and FXI is instrumented to deal with potential reverse causality issues. An increase (decrease) in the REER implies appreciation (depreciation). Both models are estimated with panel data methods that are compatible with a REER that is either stationary or nonstationary, but cointegrated with the regressors. Model results are reported in Table 7. This subsection discusses the determinants that are common across the two REER models, while the following two subsections present the variables that are specific to either the REER-Index or the REER-Level models, separately.

Table 7.

EBA REER Models Regression Results

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Standard errors in parenthesis. * significant at 10%; ** significant at 5%; *** significant at 1% † sign means variable is included in differences from (trade weighted) world counterpart. Dependent variable: REER (+=appreciation) Capital account openness is calculated as one minus capital control index.

B. Common REER Determinants

Many of the common REER determinants are also common to the current account model, with most parameter estimates having the expected sign—the opposite to the coefficients reported in the EBA-CA model—and similar statistical significance.31 Both REER models include the same policy variables— health spending, foreign exchange intervention and financial excesses—all of which are common to the current account model. The key exception is the fiscal balance, which remains excluded from the REER models because its impact was either insignificant or counterintuitive. Instead, the REER models include a monetary policy variable, proxied by real interest rate differentials.

Cyclical factors

Commodity terms-of-trade. Commodity terms of trade is measured as the ratio of commodity export prices to commodity import prices. The coefficient has a positive sign, indicating that more favorable commodity terms of trade are associated with a more appreciated exchange rate, reflecting the income effect on domestic demand. An increase of 10 percent in the terms-of-trade appreciates the REER by 1.8 percent in the Index model, and by 0.6 percent in the Level model.

Macroeconomic Fundamentals

Net foreign assets (lagged). The relationship between the NFA-to-GDP ratio and the REER is ambiguous. Countries with negative NFA positions should be expected to run trade surpluses and would need an exchange rate depreciation to achieve this goal. This hypothesis implies that the coefficient on this variable should be positive, consistent with the results from the REER=level model, and Lane and Milesi-Ferretti (2004). This is contrary to the REER-index model, where the estimated coefficient is consistent with that of the current account regression.

Output per worker (lagged). Richer countries are expected to have higher non-tradable prices and more appreciated exchange rates reflecting the Balassa-Samuelson effect, whereby countries with higher labor productivity in the tradable goods sector have higher domestic wages and non-tradable goods prices, implying a more appreciated exchange rate. The estimated coefficients in both REER models support the Balassa-Samuelson hypothesis, and suggest that an increase in output per worker of 10 percent appreciates the exchange rate by about 2 percent in both models.

Expected real GDP growth (5 years ahead). The coefficient on this variable is positive, consistent with the negative sign in the current account model. Better growth prospects are associated with higher domestic demand and a more appreciated real exchange rate in both REER models. An increase in expected growth of 1 percent appreciates the REER by about 2 percent in both models.

Reserve currency status. As explained for the current account model, currencies with reserve status tend to be more appreciated than otherwise, reflecting their greater global demand. The full interpretation of this effect needs to take into account the interaction with capital account openness and global risk aversion (see below).

Structural country features

Demographics (population growth). Consistent with Aloy and Gente (2009), higher population growth is related to a more appreciated currency. This effect is captured in both models, and it is consistent with the current account regression.

Trade openness (lagged). This variable is measured by the ratio of exports and imports to GDP. Trade liberalization generally lowers the domestic price of tradable goods, thus depreciating the real exchange rate. The variable is lagged to avoid the effect of contemporaneous exchange rate fluctuations on the indicator. As expected, the coefficient has a negative sign in both models. An increase in the openness indicator of 10 percentage points depreciates the REER by about 2 percent in the Index model and 3 percent in the Level model.

Share of administered prices in the CPI. Administered prices could in principle help to lower consumer prices thus depreciating the REER. The estimated coefficient corroborates this prior in both REER models. Specifically, a 1 percentage point increase in the share of administered prices depreciates the real exchange rate by about 2 percent in the REER-Index model and 3 percent in the REER-level model.

Policy Variables

Monetary policy (interacted). A higher real interest rate differential should be related to a REER appreciation, and this relationship should be stronger with greater capital account openness. The regression includes the real short-term interest rate (i.e. adjusted for the contemporaneous inflation differentials) to capture this effect, and the associated estimated coefficients in both REER models display the expected signs. For economies engaging in Quantitative Easing (QE), “shadow” real interest rates could be considered to capture the QE effect. For countries with a fully open capital account, an interest rate differential of 1 percent appreciates the REER in both models by about 0.6–0.7 percent. For a country at the 75th percentile of the capital controls index, these effects are about 0.4–0.5 percent.

Health spending (lagged). When the safety net is insufficient, households need to increase their precautionary saving, reducing domestic demand and leading to a more depreciated real exchange rate. The estimated coefficient for this variable is positive in both models: an increase in health spending of 1 percent of GDP appreciates the REER by 2 percent in the Index model and about 4 percent in the Level model. This is consistent with theory and the estimated coefficient in the current account model.

Foreign exchange intervention (interacted). FXI can affect the nominal and real exchange rate, and more so in countries with less open capital accounts. A proxy of FXI, with the same instrumentation as in the current account model, is included in both REER models. The results indicate that official reserve purchases lead to a real depreciation, with smaller effects in countries where capital is more mobile. With the new FXI measure—which includes operations with FX derivatives—the size of the estimated coefficient increases in both REER models with respect to their earlier versions. The results indicate that a 1 percent of GDP in official foreign exchange purchases leads to a 0.6 real depreciation in the REER-Index model (and a 0.9 depreciation in the REER-Level model) for a country in the 75th percentile of the distribution of capital controls index. These effects double for a country at the 90th percentile.

Financial excesses. Consistent with the current account estimates and the relevant literature, the results show that credit booms—captured by private credit-to-GDP ratios above their long-term trends computed with filtering techniques—lead to a REER appreciation. However, the effect is statistically significant for the REER-index model only, where an increase in the credit gap of 10 percent of GDP appreciates the exchange rate by about 1 percent.

Capital controls (interacted). As in the current account model, the effect of capital controls is included indirectly through its interactions with global risk aversion and other policy variables (see above, FXI and monetary policy).

  • Global risk aversion (lagged). Variations in risk aversion tend to affect capital flow movements and exchange rates, although often with a differentiated impact across countries, depending on their degree of capital account openness and safe-haven status. Increased risk aversion tends to weaken the currency of most countries (especially those more financially integrated) while strengthening reserve currencies. This result is visible in both REER models.

C. REER-Index Determinants

Since the REER-Index model is estimated with country fixed effects, some slow-moving variables, such as institutional quality and certain demographic indicators (e.g. population age composition and longevity risks), are not statistically significant and hence are excluded from the model. Additional variables specific only to the REER-Index model include:

Output gap. As expected, a higher output gap, reflecting stronger domestic demand relative to potential output, is associated with a more appreciated real exchange rate. A positive output gap of 1 percent appreciates the REER by about 0.4 percent in the Index model.

Financial home bias (lagged). This variable, proxied by the share of domestic debt owned by residents, captures the role that investor preference for domestic assets has on a country’s REER. Since changes in the exchange rate can affect the indicator due to compositional effects (i.e. the share of foreign-held debt is more likely to be denominated in foreign currency), the variable enters the equation with a lag. As expected, an increase in the degree of home bias appreciates the exchange rate. The estimated coefficient is 0.2.

D. REER-Level Determinants

Since the REER-Level model measures differences in relative price levels across countries, proxies for supply-side differences in productivity (either in labor productivity or capital-labor ratios) and slow moving structural features (such as demographics, institutional quality, and indirect taxation) need to be considered. Additional variables specific to the REER-Level model include:

Capital-labor ratio (lagged). This variable captures the Bhagwati-Kravis-Lipsey effect, whereby countries with higher capital-to-labor ratios have higher non-tradable prices and a more appreciated REER, since the non-tradable sector tends to be more labor-intensive. Results suggest that a 10 percent higher capital-labor ratio is associated with a 1 percent real exchange rate appreciation.

Ratio of traded/non-traded sector productivity (lagged). This supply-side determinant captures the Balassa-Samuelson effect. The estimated coefficient is found to be positive, as expected: an increase in relative productivity of 10 percentage points appreciates the exchange rate by about 2 percent.

Demographics (old-age dependency ratio). Higher OAD ratios have been found to raise the demand for non-tradable old-age related services relative to tradable commodities, increasing the relative price of non-traded goods and thus leading to real exchange rate appreciation (Groneck and Kaufman, 2017). The estimated effect supports this hypothesis.

Institutional quality. Greater institutional risk—or the perception of such risk—is likely to be a disincentive to investment, leading to a higher current account balance and a more depreciated REER. A higher value for this indicator represents lower institutional risk, so the positive estimated coefficient is as expected, consistent with the current account model.

VAT revenue. Since indirect taxes create a wedge between domestic and foreign prices—which increases domestic consumer prices, thereby appreciating the REER— the share of VAT revenue in GDP is included in the REER-Level model. The estimated coefficient in the REER-Level model is found to have a positive sign (equivalent to about two-thirds), although it is not significant.

E. REER Model Fit and Robustness

In general, the fit of the REER models remains largely unchanged relative to earlier versions (See Tables 7 and 8). While the goodness of fit of the REER-level model (R-squared of 0.9) is stronger than that of the REER-Index model (R-squared of 0.58), these results are not directly comparable, as they refer to different models that aim at measuring different aspects of the data. In fact, to ensure that both models are not capturing a spurious relationship, several unit root and cointegration tests were conducted (Tables 9 and 10). All the tests reject the hypotheses of non-stationarity of the residuals and of no-cointegration among unit-root variables. In other words, the results indicate that our regressions—which use levels of non-stationary variables—capture a long-run equilibrating relationship, such that REER deviations from the values predicted by the independent variables are persistent but not permanent.

Table 8.

EBA REER Models Regression Results, 2015 Models

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Standard errors in parenthesis. * significant at 10%; ** significant at 5%; *** significant at 1% † sign means variable is included in differences from (trade weighted) world counterpart. Dependent variable: REER (+=appreciation) Capital account openness is calculated as one minus capital control index.
Table 9.

Unit Root Tests

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* 10% significance; ** 5% significance; *** 1% significance rel. TRD PTR denotes “relative to trading partners”.

Phillips-Perron test with no lags. Results are the same for the Dickey-Fuller tests.

Requires balanced panel. Test run for 1996–2016 period, and exluding Pakistan in the case of FXI and monetary policy variables.

Table 10.

Cointegration tests

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Based on fitted residuals

Assumes the same cointegrating vector across countries. The alternative hypothesis is that all panels are cointegrated.

V. Constructing Current Account and Real Exchange Rate Norms and Gaps

Estimated EBA current account and REER models are used to establish norms and gaps, which are the main numerical inputs for IMF staff external sector assessments. Such norms (and the corresponding gaps) are not necessarily the fitted values of the estimated models: a normative view on the current account or REER requires taking a view on the appropriate (or desirable) level for policy variables. This process is summarized in Figure 3 and is explained in detail below for the current account model. 32

Figure 3.
Figure 3.

EBA Current Account Balance Assessment

Citation: IMF Working Papers 2019, 065; 10.5089/9781498300933.001.A001

A. Gaps and Norms

The first step in the analysis is the EBA current account regression:


where, to lighten notation, country and time subscripts have been omitted and e is the zero-mean, normally distributed regression residual, which is assumed to follow an AR(1) process. The set of policy variables are summarized in the vector P, which includes: the fiscal balance, measures of capital controls, public health spending, reserve accumulation and financial excesses. All fundamentals (macroeconomic and structural) are summarized in the vector X, whereas the vector Xcyc includes the cyclical factors (output gap and terms of trade gap).

Using coefficient estimates (denoted with a hat) of policy, cyclical and fundamental variables, the predicted values for the current account balance in percent of GDP are given by:


Let P* denote values of policy variables that are deemed desirable, which may or may not coincide with actual values P. Then, the predicted current account can be decomposed into three components: the cyclically-adjusted “CA norm”, the cyclical component, and the policy gaps:


The cyclically-adjusted “CA norm” is the current account balance implied by all underlying macroeconomic fundamentals at their actual values, assuming all policy variables are set at their medium-term desirable levels P* and excluding cyclical effects. Meanwhile, the cyclical component measures the contribution of the output gap and terms of trade to the predicted current account. Finally, the policy gap measures by how much deviations in policy variables from their desirable levels contribute to the overall deviation of the predicted current account balance from its norm. This implies that even when actual and predicted (cyclically-adjusted) current account balances coincide, the current account may not be consistent with the norm, as actual policies may not be at their desirable medium-term levels.

Defining the cyclically-adjusted current account as:


and making use of equations (1) and (3), the following expression can be derived:


Thus, the total EBA CA gap, or difference between the cyclically-adjusted current account balance and its cyclically-adjusted norm, is equal to the sum of model-identified policy gaps and the residual. This last term captures other policy distortions or fundamentals that are not explicitly modeled and regression errors. It is worth noting that even when the overall current account gap is zero, such that the actual current account balance coincides with the norm, underlying policy distortions may still exist, although in such a way that the identified policy gaps are mutually offsetting or are offset by the residual.33

Since policy variables are specified as deviations from (GDP-weighted) world averages, overall policy gaps appear if a country’s policy distortion is different from their corresponding world average. For example, if all countries are deemed to have a lower-than-desirable fiscal balance of the same magnitude, the contribution of the fiscal policy gap to the current account of each country would be zero. In other words, the estimated current account gap of each country not only reflects domestic policy distortions, but also responds to policy distortions in other countries, particularly of countries which have a large weight in the world average.

B. Benchmarks for Policy Variables (P*)

The estimation of current account and real exchange rate norms in the EBA exercise requires specifying normative policy benchmarks (P*) for appropriate levels of each of the policy variables in EBA: the fiscal balance, public health spending, capital controls, foreign exchange intervention, and monetary policy. Guidance on setting desired policies for financial excesses is also provided, recognizing that there are circumstances when the measured gap does not necessarily imply policy distortion needs to be addressed. The different policy benchmarks are guided by the following considerations:

  • For fiscal policy, the P* corresponds to levels of the cyclically-adjusted fiscal balance (as a share of potential GDP) that staff deem desirable from a medium to long-term perspective, when output gaps are closed. As such, the fiscal P* should be anchored around metrics such as the debt-stabilizing primary balance, or long-term adjustment needs given the fiscal costs of aging. Desired fiscal policy settings can differ from what may be recommended for the current year, when cyclical considerations may be important.

  • For public health spending (as a share of GDP), the P* is guided by benchmark estimates from a regression that includes (PPP-based) GDP per capita, a country’s population structure (the current old-age dependency ratio) and income inequality (see Annex VII for details). However, staff can choose a different desired public health spending level to the extent that a clear justification is provided for large departures from the benchmark or actual spending levels.

  • For capital controls, the benchmark level that is suggested as desirable for the medium term is either the cross-country average level of the controls index (0.16 in 2017, out of a potential 0 to 1 range), or a country’s actual level, whichever is smaller. This asymmetric treatment reflects that, in general, an open capital account is desirable, but that full liberalization should be achieved at an appropriate pace.

  • The desirable level of foreign exchange intervention (as a share of GDP) over the medium-term would normally be set to zero, as countries would be expected to reach a level of reserves (including comparable off balance-sheet FX positions) that is adequate from a precautionary viewpoint. Thus, no additional accumulation—beyond small amounts necessary to keep adequate FX coverage ratios unchanged—would be required. In exceptional circumstances, a nonzero desirable level could be set when reserves are significantly below the IMF’s Assessing Reserve Adequacy (ARA) metric, implying that reserve accumulation may be necessary over an extended period of time. Deviations from the medium-term desirable level (that is, the policy gap P – P*) would not necessarily be interpreted as a policy distortion. In fact, FXI policy gaps may be appropriate if they are an adequate response to current conditions or they reflect the necessary, temporary, build-up of reserves to reach an adequate level of reserves over the medium-term.

  • Regarding monetary policy, the desired short-term interest rate is the appropriate monetary policy stance that helps achieve output and inflation objectives (i.e. the country desk’s estimate of the “neutral” real rate). In most cases, when inflation is close to the target, this will be equal to the actual value. If the current policy stance were judged by the country desk to be inconsistent with that country’s own inflation and output stabilization needs, a monetary policy gap would be identified (in terms of the interest rate differential regressor) and thus contribute to a country’s overall REER gap.

  • Finally, policies relevant for financial excesses deserve special consideration. These are now measured as credit gaps directly and hence imply that the P* of this policy variable, i.e. the desirable credit gap over the medium term, should be zero in most cases. However, adjustments can be considered if the credit gap estimate does not provide an accurate picture of financial imbalances. This might be warranted in countries that are experiencing financial deepening (where the gap measure may be overstating financial imbalances by understating the long-term trend). Adjustments can also be considered in countries experiencing a credit bust (where the credit-to-GDP ratio is either not expected to return to pre-crisis levels or will recover only over a protracted period).34 It is also worth emphasizing that the presence of a credit policy gap does not necessarily mean that there is a policy distortion that needs to be addressed. Credit is an endogenous variable that may fluctuate for reasons other than inappropriate policy settings.

Finally, it is important to note that desirable policy settings are not aimed at targeting a specific level of the current account, but instead are aimed at meeting medium-term domestic objectives. For example, fiscal policy should aim at medium-term sustainability and intergenerational equity, public health expenditure should be guided by domestic welfare considerations (which aside from health outcomes should correct distortions from the lack of risk-sharing mechanisms), and credit policies (which can take the form of macroprudential policies) should avoid unsustainable credit booms and costly credit contractions.

C. Standard Errors of Estimated Norms

An important innovation introduced with the 2018 refinements entails presenting the standard errors associated with the estimated country-specific CA norms. Since the EBA CA norm for each country is a linear function of model regressors and coefficient estimates, the norm is subject to statistical uncertainty inherent in the estimates. The standard error of the CA norm is obtained as a linear combinations of the variance-covariance matrix of the estimated coefficients, assuming the regressors are fixed:


The resulting standard errors are reported alongside the estimated EBA CA norms and are meant to provide guidance to staff in setting the uncertainty ranges. In circumstances when uncertainty is judged to be higher than the estimated standard errors, staff may use larger uncertainty ranges with proper justification.

D. Multilateral consistency

Since the EBA country sample covers more than 90 percent of global GDP, multilateral consistency is an important aspect of EBA analysis. At the global level, current account balances should (at least in theory) add up to zero. Similarly, REER gaps should average to zero. To a large degree such consistency is built into the design of the methodology, because most variables are expressed in terms of deviations from world averages, and hence their (GDP weighted) contributions effectively add up to zero. In practice, however, an additional small adjustment is necessary.

In the case of the current account, the need for an adjustment results from two factors. First, current account balances do not exactly sum up to zero over the EBA country sample, because of the existence of a global statistical discrepancy at the world level, and also because the EBA country sample does not cover the global economy (leaving aside a number of relatively large net commodity exporters). Second, a few variables do not enter the regression in deviations from world averages, and in some cases the effect is non-linear, so that their aggregate contribution does not necessarily add to zero (e.g. lagged NFA-to-GDP and global risk factor). In this context, multilateral consistency is ensured by adjusting (by a uniform amount, in terms of each country’s own GDP) the components of the current account balance (following the decomposition presented in Section V.A), so that, over the whole EBA country sample: (ii) policy gaps add up to zero; and (ii) residuals add up to zero. Regarding the cyclical component, relative output gaps sum to zero (by construction), but commodity terms-of-trade gaps do not, reflecting the fact that the EBA sample includes more commodity importers than exporters. As a result, the EBA sample current account statistical discrepancy (about 0.4 percent of global GDP in 2017) is mostly attributed to the current account norms, except for the part resulting from cyclical commodity price changes (which is attributed to the cyclical component).

In the case of the REER models, the weighted average of residuals must annually add to zero for multilateral consistency. In addition to ensuring that each variable is defined relative to the trading-partner weighted average of the same variable, real exchange rates also need to be adjusted by the global weighted average of residuals (for each year, the weights are given by the eigenvector associated with the unit eigenvalue of the trade weights matrix for that year). This consistency adjustment is generally small (about 2¼ percent of the global residual in 2017).

VI. Current Account-REER Elasticities

A key input of external sector assessments is the country-specific CA-REER elasticity, which allows one to translate an estimated CA gap into a consistent REER gap, and to compare results with those from the previously discussed REER models. Given that semi-elasticity estimates can vary across countries and over time depending on a country’s structural features (such as the degree of trade openness, participation in global value chains and commodity dependence), its estimation is especially challenging. As such, several benchmark estimates are provided, which are based on a consistent methodology that borrows heavily from Lee et al. (2008). IMF country teams can use alternative estimates (including from more disaggregated data if available), and adjust for country-specific factors, where justified.

The semi-elasticity of the CA/GDP ratio with respect to REER is defined as:


where ηTB=Δ(TB/GDP)ΔREER/REERandηIB=Δ(IB/GDP)ΔREER/REER are the semi-elasticities of the nominal trade balance-to-GDP ratio and the income balance-to-GDP ratio, respectively. Assuming that the current account gap will be closed by an adjustment in the trade balance, the corresponding REER gap (in percentage terms) can be derived as:


Two methodologies are used to estimate the semi-elasticity ηTB: (i) the original CGER approach, based on calibration methods, and (ii) the “CGER-inspired” approach, based on panel regressions for export and import equations.35

A. Original CGER Approach

The original CGER approach is based on Isard et al. (2001) and IMF (1998), and decomposes the parameter of interest into:


where ηX=Δ(X/GDP)/(X/GDP)ΔREER/REERandηM=Δ(M/GDP)/(M/GDP)ΔREER/REER are the elasticities of nominal exports/GDP and imports/GDP ratios with respect to the REER, and sX and sM are the nominal shares of exports and imports with respect to GDP. The original CGER relied on a macroeconomic model to calibrate ηX and ηM, which took values of 0.71 and 0.92, respectively, and which were assumed to be common across countries. The semi-elasticities of the nominal trade balance-to-GDP were obtained, for each country, by using the common calibrated values of ηX and ηM and the country-specific export and import shares (excluding oil), over the 2013–2023 period.36 Hence, while the import and export elasticities are common to all countries, the semi-elasticity of the current account may change depending on each country’s degree of openness.

B. CGER-Inspired Approach

The CGER-inspired approach builds on the original CGER method estimating the values of ηX and ηM used in equation (6) with updated data. Dynamic export (X) and import (M) equations are estimated using an unbalanced panel covering most EBA countries and quarterly data between 1980Q1 and 2017Q4:37




where both specifications include time and country fixed effects. Equation (7) relates exports to real exchange rates and world demand (proxied by trading partners’ real GDP). Similarly, imports are assumed to be a function of real exchange rates and domestic demand (proxied by domestic real GDP) in equation (8). Both equations allow for a rich dynamic lag structure (involving up to eight lags).38 Using estimates from the panel regression, long-run export and import elasticities are obtained as follows:


The panel estimation yields values of 0.11 and 0.57 for ηX and ηM, respectively. These volume elasticities are then used in equation (6), together with the country-specific openness ratios sx and sM to calculate each semi-elasticity ηTB. It is worth noting that in the CGER-inspired approach, the trade shares are computed based on aggregate imports and exports rather than their non-oil counterparts used in the original CGER approach. This more comprehensive measure of the trade balance was deemed appropriate for the updated elasticity estimates, including because of the increased substitutability between alternative sources of energy.39

C. Elasticity Estimates

Table 11 compares the updated estimated semi-elasticities with those in the original CGER approach. In general, the semi-elasticity estimates coming from the original CGER approach and the revised CGER-inspired approach are very similar—with a correlation of 0.9. It is important to note that the suggested elasticities coming from both approaches are computed using the same methodology for all countries, and do not necessarily correct for country-specific features (such as for commodity share of exports, value-added trade, capacity or other structural factors). As discussed earlier, however, IMF country teams are encouraged to explore more granular data, and adjust for country-specific factors, when necessary and with proper justification.

Table 11.

CA to REER Elasticities

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Figures shown are those previously used in the CGER exercise, in most cases using a common elasticity assumption of 0.71 for exports and 0.92 for imports.

Based on newly estimated common elasticities using quarterly data (1980Q1–2017Q4), adjusted by the size of exports and imports in GDP; 0.11 for exports and 0.57 for imports.