1

OFFICE OF INTERNATIONAL AFFAIRS

GLOBAL EXCHANGE RATE ASSESSMENT FRAMEWORK 

METHODOLOGY

Prepared by Staff of the Office of Global Economics and Debt1

Current Version: December 20212

1 Drafted by Alexander Herman, Max Harris, and Daniel Hall.

2 This version provides an update to the August 2020 methodology paper. Relative to the previous version, this 

paper presents re-estimated GERAF results using revised historical data as of May 2021 and provides further detail 

on the construction of the safe asset index employed in GERAF.
2

Contents

I. Literature review...................................................................................................................... 3

II. GERAF contributions .......................................................................................................... 4

III. GERAF model specification and deriving current account gaps......................................... 5

Model specification and variable construction........................................................................... 5

Normative assessments of excess imbalances............................................................................. 8

IV. Exchange rate gaps ............................................................................................................ 12

Current account to REER conversion ....................................................................................... 12

REER to bilateral real exchange rate conversion..................................................................... 13

V. Conclusion............................................................................................................................. 15

References..................................................................................................................................... 16

Appendices

A: Construction of a Novel Safe Asset Index .......................................................................... 20

B: Data Sources and Descriptions............................................................................................ 22

C: List of Countries.................................................................................................................. 28

D: Robustness Checks and Regression Extensions.................................................................. 29
3

This paper explains the methodology underlying the Global Exchange Rate Assessment 

Framework (GERAF), a flexible tool created by the Department of the Treasury (Treasury) to 

study currency valuations. The model provides a rigorous, multilaterally consistent method for 

assessing external imbalances, exchange rate misalignment, and the role of policy in contributing 

to both. 

This paper proceeds as follows. Section I provides a brief review of the literature on assessing 

currency valuations. Section II discusses GERAF’s contribution to the applied practice of 

estimating currency valuations and notable differences from other currency valuation models. 

Section III describes the calculation of current account gaps, which forms the model’s core. 

Section IV explains the transformation of current account gaps into exchange rate gaps. Section 

V concludes. Appendix A describes the construction of the novel safe asset index employed in 

GERAF. Appendix B lists data sources and descriptions. Appendix C lists countries included in 

the GERAF sample. Appendix D presents robustness checks and regression extensions.

I. Literature review

GERAF builds on a substantial body of literature and applied practices for assessing currency 

valuations. Given the complexity of the task, researchers have employed a variety of methods. 

Some confront the problem looking directly at exchange rates; others study the structure of 

current accounts and then translate those findings into exchange rates.

Estimating fair-value real exchange rates via their theoretical determinants is the most direct 

approach. For instance, one can estimate real effective exchange rates (REERs) directly in a

Dynamic Equilibrium Exchange Rate (DEER) model, exploiting a panel cointegration approach 

to measure the long-run effect of factors such as productivity and terms of trade on exchange 

rates (Stolper and Fuentes (2007)). Such models are able to assess currencies on a relatively 

high-frequency basis but as a result do not have the ability to assess if these valuations are 

consistent with both internal balance (i.e., real output is close to potential) and external balance 

(i.e., external demand and the current account are at sustainable levels). 

Conversely, exchange rate valuations can be estimated vis-à-vis those consistent with current 

account balances that achieve medium-term equilibrium in the global economy. This approach 

uses lower-frequency data that allows slower moving macroeconomic variables to be included, 

thus improving model fit and providing a richer explanation of exchange rate misalignments. 

Moreover, estimating exchange rate valuation via external accounts tends to yield more stable 

and statistically robust results due to lower-frequency data. Some examples include 

Fundamental Equilibrium Exchange Rate (FEER) based models, which estimate the impact of 

factors such as domestic demand gaps, external demand gaps, and lagged REERs on current 

accounts and then derive the underlying current account consistent with closed domestic and 

external demand gaps (i.e., both domestic and external demand are at their respective potential 

levels).3 Other notable FEER-based models include the Peterson Institute of International 

Economics’ FEER model (see Cline and Williamson (2008)). While better equipped to assess 

currency misalignment in a more globally consistent manner, this class of models typically does 

not take into account the impact of particular macroeconomic policies on exchange rates.

3 See Stolper and Fuentes (2007).
4

The International Monetary Fund (IMF) applies both methods – looking directly at the REER 

and looking at the REER indirectly through current accounts – in its External Balance 

Assessment (EBA). As described in Cubeddu et al. (2019), the EBA provides a comprehensive

framework for assessing exchange rate misalignments and quantifying the role of 

macroeconomic policies in contributing to those misalignments. The EBA’s current account 

model variant first estimates the current account norm—that is, the cyclically adjusted current 

account that would occur when macroeconomic policies are set at desirable medium-term levels. 

Comparing the norm to the observed cyclically adjusted current account results in the current 

account gap, which can be further decomposed into policy gaps (owing to deviations of 

collective or individual policies from their desired levels) and residual gaps (other policy 

distortions, factors not explained by the model, and regression residuals). These current account

gaps can then be transformed into implied REER gaps. 

This approach provides a nuanced framework for assessing, among other factors, the impact of 

policies on currency valuations. By construction, the model is multilaterally consistent. Because

most variables are expressed as deviations from GDP-weighted world averages, larger 

economies have a larger influence in shaping the contributions to current account norms

(consistent with their greater economic weight). Consistency adjustments ensure that current 

account gaps add up to zero in nominal terms (i.e., fully addressing total gaps would 

mechanically eliminate excess imbalances). This approach also includes a large number of 

explanatory factors, including demographic variables that affect saving and investment behavior 

over the medium term. Finally, policy gaps can be broken down into domestic and foreign 

components. Doing so allows the estimated gap for each country to reflect domestic policy 

distortions as well as policy distortions in other countries.

II. GERAF contributions

GERAF builds on the EBA’s current account model and norm-gap analysis as documented in 

Cubeddu et al. (2019) to create a flexible model that allows for rigorous estimation of currency 

valuations relative to the dollar. Employing a framework in line with the IMF EBA exercise 

allows us to assess an economy’s current account and exchange rate based not only on structural 

factors and macroeconomic fundamentals, but macroeconomic policy distortions as well. 

Moreover, such a modeling framework allows us to disentangle the impact of domestic policy 

distortions versus those from abroad on excess imbalances. 

Building on this approach, we make several contributions to the applied practice of assessing 

external imbalances based on fundamentals and policies. First, we construct and employ an

index for assessing the relative quality of safe assets across countries. Second, we incorporate

comprehensive estimates of foreign exchange intervention across all countries in the sample 

consistent with the methodology used in Treasury’s Report to Congress on Macroeconomic and 

Foreign Exchange Policies of Major Trading Partners of the United States (“Treasury’s Foreign 

Exchange Report”).

4

 Third, we account for differential impacts of foreign exchange intervention 

on current accounts in the presence of varying degrees of capital account mobility, allowing for a 

4 Treasury’s report is submitted pursuant to the Omnibus Trade and Competitiveness Act of 1988, 22

U.S.C. § 5305, and Section 701 of the Trade Facilitation and Trade Enforcement Act of 2015, 19 U.S.C. § 4421.
5

refined explanation of the efficacy of foreign exchange intervention. (Notably, whereas the suite 

of EBA models assume that foreign exchange intervention can affect current account imbalances 

only when capital controls are present, GERAF estimates the contribution of foreign exchange 

intervention to external balances even when the capital account is fully open.) Fourth, in our 

normative assessment of excess imbalances, we introduce the concept of an “inertia gap.” This 

latter component seeks to identify the portion of misalignments due to cumulative past policy 

distortions, notably those due to past foreign exchange intervention and their effect on net 

foreign asset positions.

A more detailed discussion of these contributions follows in Section III. 

III. GERAF model specification and deriving current account gaps

Model specification and variable construction

GERAF’s foundation is its empirical model of current account determinants. For a panel series 

of 51 countries (comprising 91% of world GDP in 2018) over the period 1986-2018, GERAF 

estimates the impact of the key drivers of current account balances using a panel-corrected 

standard error model.

5

 The model breaks down these factors into four groups:

5

In line with Cubeddu et al. (2019), the baseline GERAF specification employs a pooled Generalized Least Squares 

(GLS) method regression, controlling for cross-sectional dependence. The regression also includes a panel-wide 

AR(1) correction to control for potential autocorrelation in the dependent variable.
6

Cyclical factors:

Macroeconomic 

fundamentals:

Structural fundamentals: Policy variables:

• Output gap • Trade openness

(exports + imports) / 

GDP

• Old-age dependency 

ratio (OADR)

• Cyclically adjusted 

fiscal balance/GDP

• Commodity terms of 

trade gap

• Net foreign assets

(NFA)/GDP (lagged)

• Population growth • Public health 

spending/GDP (lagged)

• NFA/GDP * NFA 

debtor (lagged)

• Prime savers share • Foreign exchange 

intervention (FXI):

• Relative output per 

worker

• Life expectancy at 

prime age

o FXI/GDP

• Forecasted real GDP 

growth

• Life expectancy at 

prime age * Future 

OADR

o FXI/GDP * Capital 

account openness

• Safe asset index • Institutional and 

political environment

• Detrended private 

credit/GDP

• Oil and natural gas trade 

balance * Resource 

temporariness

• Capital controls:

o Relative output per 

worker * Capital

account openness 

(lagged)

o Demeaned VIX * 

Capital account

openness (lagged)

o Demeaned VIX * 

Capital account

openness * Safe asset

index (lagged)

Note: VIX index corresponds to the CBOE index measuring constant, 30-day expected volatility of the S&P 500 index.

As noted above, the GERAF model specification includes several novel variables:

Safe asset index: Cubeddu et al. (2019) and the methodology underlying earlier EBA model 

iterations (see Phillips et al. (2013)) include a reserve currency status variable that measures the 

share of a country’s own currency in the total stock of global foreign exchange reserves. While 

such a variable may be intended to capture the “exorbitant privilege” of reserve currency 

countries, it fails to fully capture the impact of “flight to safety” pressures. For example, 

conventional safe haven currencies such as the Japanese yen or the Swiss franc are highly 

responsive to changes in investor sentiment in risk-off episodes but comprise relatively small 

shares of global foreign exchange reserves. Moreover, a variable based only on the stock of 

reserves will by construction assume an equal effect across all euro area countries, whereas

country-specific risk premia vary. To refine the measurement of safe asset demand and its effect 

on financing current accounts, we introduce a country-specific safe asset index that intends to 

capture two facets of relative safeness of currencies and government securities: (i) price factors 

and (ii) quantity factors (see Figure 1). 
7

Figure 1: Safe asset index

To measure the price factors, we construct time-varying conditional correlations based on the 

nominal exchange rate (expressed as local currency per Special Drawing Right (SDR)),

6

 the 10-

year government bond yield, and the inverse of the VIX index.7 The underlying notion is that in 

a risk-off environment, as uncertainty or volatility rises, a safe haven country will generally see 

its exchange rate appreciate and its government bond yields fall. Thus, the greater the comovement between uncertainty and appreciation (interest rate reductions), the more the currency

(government security) is in demand in a risk-off environment. The conditional correlations are 

estimated for each country on a monthly basis using a dynamic conditional correlation estimator, 

a particular type of multivariate generalized autoregressive conditional heteroskedasticity model 

(see Engle (2002); Engle and Sheppard (2001)).8 The sum of these two conditional correlations 

can then be standardized relative to the entire panel of 51 countries and collapsed to the annual 

level. The trend component of this standardized index is then extracted in order for the variable 

to reflect longer-term, structural fundamentals in the relative price of safe assets. Lastly, the 

price factor index is weighted by the country's currency share of foreign exchange reserves. By 

doing this, the index combines price factors (which fluctuate in times of stress) with the longterm structural demand for safe assets (the quantity of global foreign exchange reserves, the 

shares of which exhibit relative stability over time). To our knowledge, this approach is novel

and provides an intuitive, empirically grounded framework for measuring the relative safeness of 

currencies and government securities. This also complements existing theory-based measures of 

identifying safe haven currencies, such as the Global Risk Response index introduced in 

Goldberg and Krogstrup (2018).9 For further description of the construction of the safe asset 

index variable, see Appendix A.

6 The SDR is used as a numeraire for exchange rates in order to include exchange rate variation for the United 

States.

7 Using the inverse of the VIX allows for an intuitive positive correlation between increasing uncertainty/volatility, 

increasing currency appreciation, and decreasing bond yields, where all three series move in the same direction.

8 Such an approach allows conditional correlations to follow a GARCH (p,q)-like process, implicitly controlling for 

time-varying volatility.

9 See Goldberg and Krogstrup (2018) for further discussion of the identification of safe haven currencies in the asset 

pricing literature.

Safe asset index

Captures the 

appreciation/yield 

reduction 

pressures due to 

increased demand 

during risk-off 

episodes

Captures the 

appreciation 

pressures due to 

demand for the 

currency as 

reserves
8

As expected, this final index shows an outsized role of the United States (reflecting the 

substantial global demand to hold U.S. safe assets). Relative to a variable that was based only on 

the global stock of foreign exchange reserves, our safe asset index displays larger values for 

Japan and Switzerland (reflecting their role as safe havens), as well as heterogeneity across euro 

area countries. When placed in the GERAF baseline specification, the coefficient displays the 

expected negative sign and is statistically significant. 

Refined estimates of foreign exchange intervention: The GERAF specification includes 

estimates of foreign exchange intervention consistent with the methodology set forth in 

Treasury’s Foreign Exchange Report. Estimates are normally based on publicly available data 

for intervention on foreign asset purchases by authorities or estimated based on valuationadjusted foreign exchange reserves. This adjustment requires assumptions about both the 

currency and asset composition of reserves in order to isolate returns on assets held in reserves 

and currency valuation moves from actual purchases and sales, including estimations of 

transactions in foreign exchange derivatives markets. Estimates can also be based on alternative 

data series when they provide a more accurate picture of foreign exchange balances than 

estimates derived from changes in valuation-adjusted reserves. These estimates are then 

combined with data reported to the IMF on official transactions in foreign exchange derivatives 

markets. Ultimately, this approach provides more refined intervention estimates than using 

changes in reserve asset positions or the flow of reserves from balance of payments statistics as a 

proxy for intervention. 

Bayoumi, Gagnon, and Saborowski (2015) demonstrate that capital account mobility tends to 

lessen the impact of foreign exchange intervention on current accounts. Hence, in addition to 

foreign exchange intervention, GERAF includes foreign exchange intervention interacted with 

capital account openness to control for the differential effects of foreign exchange intervention 

across varying degrees of capital mobility. The full interpretation of the effect of foreign 

exchange intervention takes into account the combination of these regressors. In this context, 

both coefficients display the expected signs and are statistically significant in the baseline 

GERAF specification.

For a complete list of variables, sources, and descriptions, see Appendix B. For a complete list 

of countries in the sample, see Appendix C. Table 1 lists summary statistics for the panel sample 

of observations in the baseline regression specification.

As previously mentioned, the GERAF model specification is estimated across 51 economies and 

33 years between 1986 and 2018. Each of the 23 independent variables takes the expected sign 

and is consistent with previous empirical findings in the literature. Additionally, the model fit is 

generally in line with different specifications found in the literature. Table 2 shows results for 

the baseline specification.

For robustness checks and regression extensions, see Appendix D.

Normative assessments of excess imbalances
9

GERAF can then provide a normative assessment of excess imbalances based on: (i) the 

historical relationship between the current account and each of the regressors; (ii) the deviations 

between observed and desired policy levels; (iii) the level of net foreign assets in the absence of 

official reserve positions (i.e., the inertia gap); and (iv) the regression residual. 

As mentioned above, GERAF introduces the concept of an inertia gap so that normative current 

account assessments take into account the level of official reserve holdings. While larger net 

foreign asset positions are descriptively associated with higher current account balances, it is not 

the case that higher levels of official reserve holdings (i.e., greater precautionary external 

buffers) make higher current account balances warranted or desirable.

10

 To this end, the inertia 

gap adjusts the contribution of net foreign assets to current account norms by stripping out 

official reserves from the total net foreign asset stock, essentially including only private net 

foreign assets in the final normative assessment of excess imbalances.11

The remainder of the GERAF norm and gap analysis is consistent with that in Cubeddu et al. 

(2019). The baseline GERAF specification is expressed as:

(

CA

GDP)

i,t

= α + β

cycXi,t

cyc′

+ βXi,t

′ + δZi,t′ + γPi,t′ + εi,t

(3.1)

where Xi,t

cyc′

denotes the vector of cyclical factors, Xi,t

′

denotes the vector of macroeconomic and 

structural fundamentals, Zi,t

′

denotes the (lagged) net foreign asset position, and Pi,t

′

denotes the 

vector of policy variables (set at their observed values). Here, α denotes the regression constant 

and εi,trepresents the regression residual (zero mean, normally distributed, and assumes an 

AR(1) process). Using the model coefficients, predicted current account values can be denoted 

as:

(

CA

GDP)

i,t

̂ = α̂ + β̂cycXi,t

cyc′

+ β̂Xi,t

′ + δ̂Zi,t′ + γ̂Pi,t′

(3.2)

This can also be expressed in terms of deviations between observed and desired policy levels 

(the latter denoted as Pi,t

∗

′

), as well as the deviation between the observed net foreign asset 

position and the adjusted net foreign asset position (the latter denoted as Zi,t

×

′

):

(

CA

GDP)

i,t

̂ = β̂cycXi,t

cyc′

⏟ 

Cyc. component

+ α̂ + β̂Xi,t

′ + δ̂Zi,t×

′

+ γ̂Pi,t

∗

′

⏟ 

Cyclically adjusted CA norm

+ δ̂(Zi,t

′ − Zi,t×

′

⏟ )

Inertia gap

+ γ̂(Pi,t

′ − Pi,t∗

′

⏟ )

Policy gap

(3.3)

Here, the cyclically adjusted current account norm corresponds to the current account that, 

according to the model, would exist if policies were set at their desired levels and with adjusted 

10 This normative view is consistent with the findings of Bayoumi, Gagnon, and Saborowski (2015), who find that 

lagged intervention positively impacts current accounts, potentially operating through the portfolio balance channel.

11 In line with the specification in Cubeddu et al. (2019), GERAF uses lagged values of the net foreign asset 

position.
10

net foreign asset positions, accounting for observed macroeconomic and structural fundamentals 

and stripping out cyclical factors. The cyclical component corresponds to the portion of the 

predicted current account attributable to cyclical factors (i.e., output gaps and commodity terms 

of trade gaps). The inertia gap measures the degree to which official reserves (a subset of the net 

foreign asset position) contribute to the deviation between the predicted current account and its 

norm. Lastly, the policy gap measures the degree to which deviations between observed and 

desired policies impact the deviation between the predicted current account and its norm. For 

further discussion on the effect of policy gaps, see Box 1.

GERAF defines the observed cyclically adjusted current account as:

(

CA

GDP)

i,t

cyclically adjusted

= (

CA

GDP)

i,t

− β̂cycXi,t

cyc′

⏟ 

Cyclical component

(3.4)

Combining equations 3.1, 3.3, and 3.4 the cyclically adjusted current account can also be 

expressed as:

Cyclically adjusted CA = Cyclically adjusted CA norm + total CA gap (3.5)

or:

Cyclically adjusted CA = Cyclically adjusted CA norm + inertia gap +

policy gap + regression residual (3.5)

While most variables are expressed relative to the annual GDP-weighted world average, further 

adjustments are necessary to ensure current account gaps over the GERAF sample add up to zero 

in nominal terms in each year (see Cubeddu et al. (2019)). In the case of GERAF, multilateral 

consistency adjustments are made to a portion of the cyclical component of the current account,12

each individual policy gap, the inertia gap, and the residual. Country amounts are adjusted by a 

GDP-weighted share of each respective cumulative component (expressed in nominal terms) in 

every year. Thus, the GERAF sample current account statistical discrepancy is implicitly 

attributed to current account norms (i.e., GERAF does not attempt to model the statistical 

discrepancy of current accounts at the global level).

Similar to the methodology laid out in Cubeddu et al. (2019), GERAF can simultaneously

estimate country-and-year-specific standard errors associated with each estimated current 

account norm. These standard errors, which can be applied to the norms or to the overall current 

account gaps, highlight the degree of model-implied uncertainty surrounding each estimated 

norm and gap. The corresponding upper and lower bounds can also be translated into exchange 

rate gaps, as explained further in Section IV. 

The standard errors are estimated using the variance-covariance matrix of the regression as 

follows:

12 For the cyclical component of the current account, this adjustment is only applied to the commodity of terms of 

trade gap, as output gaps by construction add up to zero.
11

√V̂ (

CA

GDP)

i,t

cyclically adjusted norm

= √V̂(α̂ + β̂Xi,t

′ + δ̂Zi,t×′

+ γ̂Pi,t

∗

′

) (3.6)

Box 1: Example of policy gaps

GERAF’s normative analysis is founded on the gap between observed levels of policy variables 

and their desired levels. Treasury calibrates these desired levels for each year in line with 

Treasury’s view of the policies that will achieve strong, sustainable, and balanced growth over 

the medium term (reflecting appropriate domestic and external balances for all countries).

To better understand the calculation of policy gaps, consider a simplified example where there 

are two countries in the world: A and B. Each accounts for half of the world economy. The only 

policy lever is fiscal policy, and the desirable fiscal policy for both countries is a balance of 0% 

of GDP. Suppose Country A has a balance of 0% of GDP and Country B has a balance of -4% 

of GDP (i.e., the fiscal balance is in deficit). 

Let pi denote the fiscal balance of country i expressed as a percent of GDP, 

w denote the GDP-weighted world fiscal balance expressed as a percent of world GDP,

Pi denote the fiscal balance for country i relative to the world average w,

∗ denote policies at their desirable levels. 

The following results:

pA = 0%

pB = −4%

w = 0.5(0%) + 0.5(−4%) = −2%

PA = 0% − (−2%) = 2%

PB = −4% − (−2%) = −2%

w

∗ = 0.5(0%) + 0.5(0%) = 0%

pA

∗ = pB∗ = 0%

PA

∗ = PB∗ = 0%

The policy gaps are thus:

PA

GAP = PA − PA

∗ = 2%

PB

GAP = PB − PB

∗ = −2%

Note that both countries have policy gaps even though only Country B has an undesirable deficit. 

This results from defining variables relative to the world average: there will be a policy gap 

whenever a country’s policy distortion (or lack thereof) differs from the world average.

We can isolate the role of domestic policy distortions in contributing to the total policy gap. The 

domestic policy gap is simply the difference between observed and desired policy:
12

pA

GAP,domestic = pA − pA

∗ = 0%

Country A’s fiscal policy is at its desired level, so the entirety of its gap is due the policy 

distortion in Country B. As for country B, pB

GAP,domestic = −4%.

When assessing whether or not a country’s policies are distorting its current account, it is helpful 

to look at the domestic policy gap. When assessing the total impact of policy distortions in a

multilaterally consistent manner, it is most appropriate to look at the total policy gap. 

IV. Exchange rate gaps

After calculating current account gaps – whether total gaps or those relating to specific policies –

GERAF estimates the corresponding exchange rate gaps. The first transformation is from 

current account gaps to REER gaps, and the second transformation is from REER gaps to 

multilaterally consistent bilateral real exchange rate gaps.

Current account to REER conversion

To transform current account gaps into REER gaps, GERAF uses country-specific semielasticities that relate the responsiveness of the current account to the REER. The semi-elasticity 

is defined as follows:

η

CA =

∆ (

CA

GDP)

∆REER

REER

(4.1)

Following the CGER-inspired approach outlined in Cubeddu et al. (2019), it is assumed that 

exchange rate adjustment occurs through the trade balance (TB). The trade balance semielasticity can be estimated as

η

TB = ηx

s

x − ηmsm (4.2)

where η

x

(η

m) is the elasticity of export (import) volume with respect to the REER,

s

x

(s

m) is the share of nominal exports (imports) to GDP.

η

x

and η

m are assumed to be common to every country and, as in Cubeddu et al. (2019), they are 

calibrated to -0.11 and 0.57 respectively. s

x

and s

m are calculated for every country by 

averaging the share of exports and imports to GDP, respectively, over 2010-19. Intuitively, the 

formula shows that the more open an economy, the larger the semi-elasticity in absolute terms 

and thus the more responsive the trade balance to a change in the REER.

The conversion from CA gap to REER gap is then:

REER

gap =

CA

gap

η

TB (4.3)
13

Note that this semi-elasticity is used to convert the total current account gap into the total REER 

gap and current account gaps due to specific policy distortions into the REER gaps due to those 

distortions. 

REER to bilateral real exchange rate conversion

Because REERs are weighted averages of bilateral real exchange rates, it is possible to convert 

REERs (and REER gaps) into a set of multilaterally consistent bilateral real exchange rates 

against the dollar (and bilateral real exchange rate gaps against the dollar). For this conversion, 

GERAF employs the method described in Alberola et al. (1999) and outlined below. 

Begin with the definition of the REER for currency i:

qi = ∑ wijrij

m

j

(4.4)

where qiis the log of the REER for currency i,

m is the number of currencies,

wij is the weight of currency j in the index for currency i, with ∑ wij = 1

m

j

and wii = 0,

rij is the log of the real bilateral exchange rate between currencies i and j.

The set of REERs can be expressed in matrix notation as:

Q = (W − I)R (4.5)

where Q is an (m × 1) column vector of REERs,

R is an (m × 1) column vector of the bilateral real exchange rates relative to the

numeraire (in the present case, the dollar),

W is an (m × m) matrix of trade weights with zeroes along the diagonal,

I is the (m × m) identity matrix.

Given REERs (Q), the aim is to obtain bilateral real exchange rates relative to the dollar (R). 

The system is over determined, however, as there are m exchange rates in R but only m − 1 are 

independent. Thus B = W − I is not invertible. The problem is solved by eliminating the row 

and column in B corresponding to the numeraire currency n, removing the entries in Q and R

corresponding to the numeraire currency, and expressing the remaining REERs relative to the 

numeraire currency. Equation 4.5 becomes

Q−n − 1 ∗ qn = B−nR−n − 1 ∗ qn

(4.6)

where the subscript – n denotes that the numeraire currency has been deleted, 1 is a vector of 1’s, 

and qn is the trade-weighted average of the n − 1 bilateral rates for the numeraire currency.

Letting C = B − 1 ∗ (wn1, wn2, ... , wnn−1) equation 4.6 can be rewritten as
14

Q−n − 1 ∗ qn = CR−n

(4.7)

In terms of REER gaps and bilateral real exchange rate gaps, equation 4.7 becomes

Q̂

−n − 1 ∗ q̂n = CR̂−n

(4.8)

where ^ indicates deviations from equilibrium. The vector of bilateral real exchange rate 

misalignments vis-à-vis the numeraire is thus

R̂

−n = C

−1

(Q̂

−n − 1 ∗ q̂n) (4.9)

GERAF follows this procedure with the dollar as numeraire to compute R̂

−n, which consists of 

the bilateral real exchange rate misalignments against the dollar for the 50 other countries in the 

sample (the rest of the world is assumed to be broadly in line and does not factor into the 

analysis). 

In addition to estimated REER gaps, Q̂

−n, this transformation requires W, the matrix of weights. 

GERAF assigns currency weights based on trade flows and applies a double-weighting approach 

for exports that takes into account third-market effects as detailed in Turner and Van’t dack 

(1993), which underpins the standard BIS method for computing REER trade weights. Currency

j’s weight in currency i’s basket is as follows:

Import weight wij

m =

mi

j

mi

Export weight wij

x = (

xi

j

xi

)

yj

yj+∑ xh

j

h

+ ∑ (

xi

k

xi

) (

xj

k

yk+∑ xh

k

h

k≠j )

Total weight wij = (

mi

xi+mi

) wij

m + (

xi

xi+mi

) wij

x

where:

xi

j

(mi

j

) is i’s exports to (imports from) j,

xi

(mi) is i’s total exports (imports),

yjis home supply of domestic gross manufacturing output of economy j, and

∑ xh

j

h is the sum of exports from h to j excluding those from i.

Trade flows are calculated based on manufactured goods (SITC 5-8). Home supply of domestic 

gross manufacturing is proxied by manufacturing value added plus imports of manufactures 

minus exports of manufactures. 

Thus, GERAF’s final output is the vector R̂

−n of bilateral exchange rate gaps against the dollar. 

Note that the input vector of REER gaps, Q̂

−n, will change according to the specific gap being

investigated. For instance, Q̂

−n could consist of total REER gaps, in which case R̂−n would 
15

represent total bilateral real exchange rate gaps with the dollar. Alternatively, Q̂

−n could consist 

of REER gaps due to a specific policy (e.g. fiscal policy), in which case R̂

−n would reflect 

bilateral real exchange rate gaps with the dollar resulting from fiscal policy distortions. Note 

also that these bilateral real exchange rate gaps are equivalent to bilateral nominal exchange rate 

gaps in this backward-looking exercise where inflation differentials are taken as given. 

V. Conclusion

GERAF provides Treasury with a robust framework for assessing currency valuations on a 

variety of dimensions. Beginning with a model of current account determinants, it calculates the 

gap between the observed cyclically adjusted current account and the current account norm (the 

current account that would exist if policies were set at their desired levels and with adjusted net 

foreign asset positions, accounting for observed macroeconomic and structural fundamentals and 

stripping out cyclical factors). This gap – or portions of it depending on the specific policy 

distortions of interest – is then converted into the corresponding REER gap and bilateral

exchange rate gap against the dollar, all while maintaining multilateral consistency. This tool 

will assist Treasury in its work on exchange rates.
16

References

Alberola, Enrique, Susana G. Cervero, Humberto Lopez, and Angel Ubide. 1999. “Global 

Equilibrium Exchange Rates: Euro, Dollar, ‘Ins,’ ‘Outs,’ and Other Major Currencies in a 

Panel Cointegration Framework.” IMF Working Paper 99/175, International Monetary 

Fund, Washington, DC.

Bayoumi, Tamim, Joseph Gagnon, and Christian Saborowski. 2015. “Official financial flows, 

capital mobility, and global imbalances.” Journal of International Money and Finance

52: 146-74.

Chinn, Menzie D., and Hiro Ito. 2006. “What matters for financial development? Capital 

controls, institutions, and interactions.” Journal of Development Economics 81(1): 163-

92.

Cline, William R., and John Williamson. 2008. “New estimates of fundamental equilibrium 

exchange rates.” Policy Brief No. PB08-7. Peterson Institute for International 

Economics.

Cubeddu, Luis, Signe Krogstrup, Gustavo Adler, Pau Rabanal, Mai Chi Dao, Swarnali Ahmed 

Hannan, Luciana Juvenal, Nan Li, Carolina Osorio Buitron, Cyril Rebillard, Daniel 

Garcia-Macia, Callum Jones, Jair Rodriguez, Kyun Suk Chang, Deepali Gautum, and 

Zijao Wang. 2019. “The External Balance Assessment Methodology: 2018 Update.” 

IMF Working Paper 19/65, International Monetary Fund, Washington, DC.

Drehmann, Mathias, Claudio Borio, and Kostas Tsatsaronis. 2011. “Anchoring 

Countercyclical Capital Buffers: The Role of Credit Aggregates.” International Journal 

of Central Banking 7(4): 189-240.

Engle, Robert. 2002. “Dynamic Conditional Correlation: A Simple Class of Multivariate 

Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business 

& Economics Statistics 20(3): 339-50.

Engle, Robert, and Kevin Sheppard. 2001. “Theoretical and Empirical Properties of Dynamic 

Conditional Correlation Multivariate GARCH.” NBER Working Paper 8554, National 

Bureau of Economic Research.

Goldberg, Linda S., and Signe Krogstrup. 2018. “International Capital Flow Pressures.” NBER 

Working Paper 24286, National Bureau of Economic Research.

Grigoli, Francesco, Alexander Herman, Andrew Swiston, and Gabriel Di Bella. 2015. “Output 

Gap Uncertainty and Real-Time Monetary Policy.” IMF Working Paper 15/14, 

International Monetary Fund, Washington, DC.

Ilzetzki, Ethan, Carmen M. Reinhart, and Kenneth S. Rogoff. 2019. “Exchange Arrangements 
17

Entering the Twenty-First Century: Which Anchor will Hold?” The Quarterly Journal of 

Economics 134(2): 599-646.

Phillips, Steve, Luis Catão, Luca Ricci, Rudolfs Bems, Mitali Das, Julia Di Giovanni, Filiz 

Unsal, Marola Castillo, Jungjin Lee, Jair Rodriguez, and Mauricio Vargas. 2013. “The 

External Balance Assessment (EBA) Methodology.” IMF Working Paper 13/272, 

International Monetary Fund, Washington, DC.

Quinn, Dennis P. 1997. “The Correlates of Change in International Financial Regulation.” 

American Political Science Review 91: 531-51.

Quinn, Dennis P., and A. Maria Toyoda. 2008. “Does Capital Account Liberalization Lead to 

Growth?” Review of Financial Studies 21(3): 1403-49.

Stolper, Thomas, and Monica Fuentes. 2007. “GSDEER and Trade Elasticities.” Paper

presented to a workshop at the Peterson Institute sponsored by Bruegel, KIEP, and the 

Peterson Institute, February.

Turner, Philip and Jozef Van’t dack. 1993. “Measuring International Price and Cost 

Competitiveness.” BIS Economic Papers, no 39.
18

Variable Obs. Economies Avg. years Mean Std. dev. Min p10 p25 p50 p75 p90 Max Kurtosis

Dependent variable

Current account/GDP 1,279 5 1 2 5 1986 - 2018 -0.004 0.047 -0.146 -0.055 -0.034 -0.011 0.024 0.058 0.164 3.916

Cyclical factors

Output gap 1,279 5 1 2 5 1986 - 2018 -0.001 0.030 -0.169 -0.034 -0.017 0.000 0.015 0.033 0.140 6.997

Commodity TOT gap 1,279 5 1 2 5 1986 - 2018 0.000 0.012 -0.076 -0.011 -0.005 0.000 0.005 0.011 0.072 10.658

Macroeconomic Fundamentals

Trade openness/GDP 1,279 5 1 2 5 1986 - 2018 0.552 0.335 0.088 0.215 0.341 0.472 0.630 1.019 1.819 5.568

L. NFA/GDP 1,279 5 1 2 5 1986 - 2018 -0.217 0.416 -1.963 -0.661 -0.424 -0.225 -0.041 0.205 1.996 6.126

L. NFA/GDP * (Dummy if L.NFA/GDP < -60%) 1,279 5 1 2 5 1986 - 2018 -0.037 0.135 -1.363 -0.061 0.000 0.000 0.000 0.000 0.000 30.772

L.Output per worker, relative to top 3 economies 1,279 5 1 2 5 1986 - 2018 0.162 0.369 -0.390 -0.282 -0.159 0.072 0.468 0.612 1.125 2.258

Real GDP growth, forecast in 5 years 1,279 5 1 2 5 1986 - 2018 0.039 0.018 -0.021 0.018 0.024 0.035 0.051 0.065 0.100 2.594

Safe asset index 1,279 5 1 2 5 1986 - 2018 0.016 0.065 -0.054 0.000 0.000 0.000 0.000 0.040 0.566 43.572

Structural Fundamentals

Old-age dependency ratio 1,279 5 1 2 5 1986 - 2018 0.250 0.099 0.102 0.136 0.159 0.257 0.334 0.384 0.594 2.063

Population growth 1,279 5 1 2 5 1986 - 2018 0.010 0.007 -0.007 0.001 0.004 0.010 0.015 0.021 0.030 2.645

Prime savers share 1,279 5 1 2 5 1986 - 2018 0.485 0.062 0.361 0.405 0.429 0.487 0.538 0.569 0.621 1.873

Life expectancy at prime age 1,279 5 1 2 5 1986 - 2018 31.046 3.185 21.663 26.867 28.754 31.420 33.559 35.013 37.567 2.795

Life expectancy at prime age * Future OADR 1,279 5 1 2 5 1986 - 2018 11.302 5.648 2.230 4.371 6.410 10.501 15.375 19.154 30.175 2.556

Institutional/political environment (ICGR-12) 1,279 5 1 2 5 1986 - 2018 0.725 0.125 0.293 0.563 0.632 0.738 0.831 0.876 0.961 2.649

Oil and natural gas trade balance * Resource temporariness 1,279 5 1 2 5 1986 - 2018 0.007 0.019 0.000 0.000 0.000 0.000 0.001 0.020 0.163 28.810

Policy Variables

Cyclically-adjusted fiscal balance

Observed 1,279 5 1 2 5 1986 - 2018 -0.020 0.035 -0.247 -0.065 -0.038 -0.018 0.002 0.017 0.107 6.196

Instrumented 1,279 5 1 2 5 1986 - 2018 0.006 0.017 -0.051 -0.015 -0.007 0.005 0.017 0.029 0.064 3.040

L.Public health spending/GDP 1,279 5 1 2 5 1986 - 2018 0.043 0.023 0.005 0.012 0.021 0.044 0.062 0.075 0.096 1.923

FXI/GDP

Observed 1,279 5 1 2 5 1986 - 2018 0.008 0.029 -0.198 -0.013 -0.002 0.000 0.014 0.035 0.280 24.455

Instrumented 1,279 5 1 2 5 1986 - 2018 0.002 0.011 -0.057 -0.009 -0.004 0.001 0.007 0.013 0.086 12.775

FXI/GDP * K openness

Observed 1,279 5 1 2 5 1986 - 2018 0.005 0.022 -0.081 -0.009 -0.001 0.000 0.009 0.023 0.280 56.712

Instrumented 1,279 5 1 2 5 1986 - 2018 0.001 0.008 -0.056 -0.008 -0.003 0.001 0.005 0.009 0.042 8.640

Detrended private credit/GDP 1,279 5 1 2 5 1986 - 2018 0.003 0.113 -0.653 -0.121 -0.043 0.010 0.063 0.123 0.388 6.721

L.Relative output per worker * K openness 1,279 5 1 2 5 1986 - 2018 0.183 0.323 -0.268 -0.152 -0.095 0.051 0.442 0.607 1.125 2.599

L.demeaned VIX * K openness 1,279 5 1 2 5 1986 - 2018 0.001 0.055 -0.103 -0.067 -0.039 -0.011 0.037 0.079 0.146 2.762

L.demeaned VIX * K openness * Safe asset index 1,279 5 1 2 5 1986 - 2018 0.000 0.004 -0.037 -0.001 0.000 0.000 0.000 0.001 0.066 88.951

Notes: Summary statistics are calculated based on the baseline regression sample. For easier interpretation of the data, variables shown here are not constructed relative to the annual world GDP-weighted average.

Source: U.S. Treasury staff calculations.

Table 1. Summary Statistics

Years
19

(1)

GERAF 

Baseline

Cyclical factors

Output gap # -0.344***

(0.000)

Commodity TOT gap 0.258***

(0.000)

Macroeconomic Fundamentals

Trade openness/GDP # 0.019***

(0.002)

L. NFA/GDP 0.027***

(0.000)

L. NFA/GDP * (Dummy if L.NFA/GDP < -60%) 0.004

(0.712)

L.Output per worker, relative to top 3 economies 0.034

(0.105)

Real GDP growth, forecast in 5 years # -0.254***

(0.004)

Safe asset index -0.039**

(0.029)

Structural Fundamentals

Demographic block

Old-age dependency ratio # -0.114***

(0.005)

Population growth # -0.622*

(0.059)

Prime savers share # 0.227***

(0.000)

Life expectancy at prime age # -0.007***

(0.000)

Life expectancy at prime age # * Future OADR 0.017***

(0.000)

Institutional/political environment (ICGR-12) # -0.058***

(0.001)

Oil and natural gas trade balance * Resource temporariness # 0.515***

(0.000)

Policy Variables

Cyclically-adjusted fiscal balance (instrumented) # 0.527***

(0.000)

L.Public health spending/GDP # -0.254*

(0.064)

FX Intervention

FXI/GDP (instrumented) # 0.700***

(0.003)

FXI/GDP (instrumented) # * K openness -0.510*

(0.071)

Detrended private credit/GDP # -0.095***

(0.000)

Capital Controls

L.Relative output per worker * K openness 0.021

(0.359)

L.demeaned VIX * K openness 0.028**

(0.033)

L.demeaned VIX * K openness * Safe asset index -0.064

(0.554)

Constant -0.019***

(0.000)

Observations 1,279

Number of countries 51

R-squared 0.392

RMSE 0.019

Table 2. GERAF Current Account Model: Baseline Specification

"L." denotes variables expressed using a one year lag. "#" denotes variables expressed relative to 

the annual world GDP-weighted average. P-values in parentheses. Standard errors are robust to 

heteroskedasticity, autocorrelation and cross-sectional dependence. Regression includes a 

panel-wide AR(1) correction to control for potential autocorrelation in the dependent variable. 

***, **, * next to a number indicate statistical significance at 1, 5 and 10 percent, respectively.

Source: U.S. Treasury staff calculations.
20

Appendix A: Construction of a Novel Safe Asset Index

The safe asset index for country i at time t is constructed as follows:

SAIi,t =  i,t

permanent

⏟ 

Price factor

× θ⏟i,t

Quantity factor

(A1.1)

where  i,t

permanent corresponds to the price factor (as described below) and θi,t corresponds to 

the quantity factor, as measured by the country currency’s weight in the IMF Currency 

Composition of Official Foreign Exchange Reserves (COFER) portfolio.

The raw price factor, ωi,t, is estimated individually for each economy using a dynamic 

conditional correlation model — a particular type of multivariate generalized autoregressive 

conditional heteroskedasticity model (DCC-MGARCH) — using monthly level data. 

Let ei,t

LC⁄SDR denote the nominal exchange rate, expressed in local currency per SDR,13

i

i,t

10Y

denote the 10-year sovereign bond nominal interest rate, and

IVIXt denote the inverse VIX index. 

Here, the inverse VIX is expressed as the following, as to preserve the scale of discrete changes 

in the original VIX index:

IVIXt = (−1 ∗ VIXt

) + 100 (A1.2)

Using the inverse of the VIX and the exchange rate expressed in local currency per SDR allows 

for an intuitive positive correlation between increasing uncertainty/volatility, increasing currency 

appreciation, and decreasing bond yields, where all three data series move in the same direction.

For most economies in the sample, the DCC-MGARCH model follows a GARCH(1,1) process. 

All variables are transformed such that they are stationary and are expressed in terms of change 

in percentage points. Hence, the variables in the DCC-MGARCH(1,1) specification are:

 ln(ei,t

LC⁄SDR)

 (i

i,t

10Y

)

 ln(IVIXt

)

There are a small number of economies in the sample where the above specification fails to 

iterate properly. For these economies, the DCC-MGARCH model follows a GARCH(2,2) 

process with the variables expressed as the following:

13 Using the SDR as a numeraire allows for a time-varying spot exchange rate for the U.S. dollar.
21

 ln(ei,t

LC⁄SDR)

 (i

i,t

10Y

)

 ln(IVIXt

)

Lastly, for where either of the above specifications fail to iterate properly, the DCC-MGARCH 

model follows a GARCH(2,2) process with the variables expressed as the following:

 ln(ei,t

LC⁄SDR)

 ln(i

i,t

10Y

)

 ln(IVIXt

)

ωi,tis then calculated using the estimated dynamic conditional correlations:

ωi,t = ρi,t(i

i,t

10Y

,IVIXt) + ρi,t(ei,t

LC⁄SDR

,IVIXt) (A1.3)

where:

ρi,t(i

i,t

10Y

,IVIXt) = estimated conditional correlation between i

i,t

10Y and IVIXt

ρi,t(ei,t

LC⁄SDR

,IVIXt) = estimated conditional correlation between ei,t

LC⁄SDR and IVIXt

This raw price factor is then standardized across the entire panel sample:

i,t = (

ωi,t − ω̅

σω

)

i,t

(A1.4)

After collapsing to the annual level, the permanent component of the raw price factor for each 

country is estimated using a Hodrick-Prescott filter:

 i,t

permanent = min

τ

(∑(yt − τt

)

2

T

t=1

+ λ∑[(yt+1 − τt

) − (yt − τt−1)]

2

T−1

t=2

) (A1.5)

where yt corresponds to i,t, τtis the trend component, and  is set to 100.

Lastly, the final price factor,  i,t

permanent, is multiplied by the quantity factor, θi,t

, as measured 

by the country currency’s weight in the IMF COFER portfolio:

SAIi,t =  i,t

permanent

⏟ 

Price factor

× θ⏟i,t

Quantity factor

(A1.6)
22

Appendix B: Data Sources and Descriptions

Table B1. GERAF Data Sources

Variable* Sources** Notes

Dependent variable

Current account/GDP IMF World Economic Outlook (WEO); national authorities; and Haver 

Analytics.

Cyclical factors

Output gap IMF WEO; Haver Analytics; and Treasury staff estimates. 1/

Commodity TOT gap IMF International Financial Statistics (IFS); Haver Analytics; and Treasury staff 

estimates. 2/

Macroeconomic Fundamentals

Trade openness/GDP IMF Direction of Trade Statistics (DOTS); IMF WEO; and Haver Analytics.

L. NFA/GDP IMF IFS; IMF WEO; and Haver Analytics.

L. NFA/GDP * (Dummy if L.NFA/GDP < -60%) IMF IFS; IMF WEO; Haver Analytics; and Treasury staff calculations.

L.Output per worker, relative to top 3 economies IMF WEO; national authorities; UN World Population Prospects, 2019 Revision; 

Haver Analytics; and Treasury staff calculations.

Real GDP growth, forecast in 5 years IMF WEO. 3/

Safe asset index

Chicago Board Options Exchange (CBOE); national authorities; IMF IFS; Bank 

of International Settlements (BIS); IMF Currency Composition of Official 

Foreign Exchange Reserves (COFER); Haver Analytics; and Treasury staff 

estimates.

4/
23

Table B1. GERAF Data Sources

Variable* Sources** Notes

Structural Fundamentals

Old-age dependency ratio (OADR) UN World Population Prospects, 2019 Revision; and Haver Analytics.

Population growth UN World Population Prospects, 2019 Revision; and Haver Analytics.

Prime savers share UN World Population Prospects, 2019 Revision; and Haver Analytics.

Life expectancy at prime age UN World Population Prospects, 2019 Revision; and Haver Analytics.

Life expectancy at prime age * Future OADR UN World Population Prospects, 2019 Revision; and Haver Analytics.

Institutional/political environment (ICGR-12) PRS Group, International Country Risk Guide (ICRG).

Oil and natural gas trade balance * Resource temporariness

IMF WEO; World Bank World Development Indicators (WDI); IMF Balance of 

Payments Statistics (BOPS); Haver Analytics; and British Petroleum Statistical 

Review of World Energy.

5/

Policy Variables

Cyclically-adjusted fiscal balance

Observed IMF Fiscal Monitor (FM); IMF WEO; Haver Analytics; and Treasury staff 

estimates. 6/

Instrumented

IMF FM; IMF WEO; Treasury staff estimates; national authorities; PRS Group, 

ICRG; CBOE; Ilzetzki, Reinhart, and Rogoff (2019); Haver Analytics; and 

Treasury staff calculations.

7/

L.Public health spending/GDP OECD Government Statistics; World Bank WDI; and Haver Analytics.
24

Table B1. GERAF Data Sources

Variable* Sources** Notes

FXI/GDP

Observed IMF IFS; national authorities; IMF WEO; Bloomberg L.P.; Haver Analytics; 

Treasury staff calculations; and Treasury staff estimates. 8/

Instrumented IMF IFS; national authorities; IMF WEO; Bloomberg L.P.; Haver Analytics; 

Treasury staff calculations; Treasury staff estimates; and World Bank WDI. 9/

FXI/GDP * K openness

Observed

IMF IFS; national authorities; IMF WEO; Bloomberg L.P.; Haver Analytics; 

Treasury staff calculations; Treasury staff estimates; Quinn database; and 

Chinn-Ito database.

8/

Instrumented

IMF IFS; national authorities; IMF WEO; Bloomberg L.P.; Haver Analytics; 

Treasury staff calculations; Treasury staff estimates; World Bank WDI; Quinn 

database; and Chinn-Ito database.

10/

Detrended private credit/GDP BIS; World Bank WDI; IMF WEO; Haver Analytics; Drehmann et al. (2011); 

and Treasury staff estimates.

L.Relative output per worker * K openness

IMF WEO; national authorities; UN World Population Prospects, 2019 Revision; 

Haver Analytics; Treasury staff calculations; Quinn database; and Chinn-Ito 

database.

L.demeaned VIX * K openness CBOE; Haver Analytics; Quinn database; and Chinn-Ito database.

L.demeaned VIX * K openness * Safe asset index

CBOE; national authorities; IMF IFS; Bank of International Settlements (BIS); 

IMF Currency Composition of Official Foreign Exchange Reserves (COFER); 

Haver Analytics; Treasury staff estimates; Quinn database; and Chinn-Ito 

database.

Other Data

CA-REER semi elasticity Cubeddu et al. 2019; IMF WEO; IMF IFS; national authorities; Haver Analytics; 

and Treasury staff calculations.
25

Table B1. GERAF Data Sources

Variable* Sources** Notes

REER trade weights UN COMTRADE; UN National Accounts; IMF DOTS; national authorities; 

Haver Analytics; and Treasury staff calculations. 11/

Additional Explanatory Variables (See Appendix D)

Reserve currency status IMF Currency Composition of Official Foreign Exchange Reserves (COFER); 

and Haver Analytics.

Change in FX Reserves/GDP IMF IFS; IMF WEO; and Haver Analytics.

Real interest rates IMF IFS; IMF WEO; national authorities; and Haver Analytics.

Real interest rates * K openness IMF IFS; IMF WEO; national authorities; Haver Analytics; Quinn database; and 

Chinn-Ito database.

Inflation (period average) IMF WEO; and Haver Analytics.

Inflation (period average; bounded index, 0 to 1) IMF WEO; Haver Analytics; and Treasury staff calculations. 12/

Share of urban population World Bank WDI; and Haver Analytics.

Young-age dependency ratio (YADR) UN World Population Prospects, 2019 Revision; and Haver Analytics.

Gini index World Bank WDI; and Haver Analytics.

Income share held by top ten percent World Bank WDI; and Haver Analytics.

Financial center dummy IMF External Balance Assessment dataset (2019 vintage).
26

Table B1. GERAF Data Sources

Variable* Sources** Notes

Fixed exchange rate regime dummy Ilzetzki, Reinhart, and Rogoff (2019); and Treasury staff calculations.

* Variable construction consistent with that of Cubeddu et al. (2019), unless otherwise noted. 

** Where necessary and applicable, any gaps in data are filled with data from the 2019 vintage of the IMF External Balance Assessment dataset.

1/ Uses IMF desk estimates where available, otherwise estimated using via HP filter with =100 (which closely replicates IMF desk estimates, per Grigoli et 

al. (2015)).

2/ Gap estimated using via HP filter with =100.

3/ Collected from archived WEO databases.

4/ Country-specific index reflecting the relative quality of safe assets. To capture price factors, we estimate time-varying monthly conditional correlations 

between a) local currency-to-SDR exchange rates and the inverse of the VIX index, and b) 10-year sovereign bond yields and the inverse of the VIX index for 

each country. Conditional correlations are derived from country-specific dynamic conditional correlation multivariate generalized autoregressive conditional 

heteroskedasticity (DCC-MGARCH) estimations (see Engle (2002); Engle and Sheppard (2001)). The sum of these two conditional correlations are then 

standardized relative to the entire panel of countries, collapsed to the annual level, and then, in order to reflect more structural dynamics, we take the trend 

component of this standardized index using an HP filter where =100. Lastly, to capture quantity factors, the price factor index is weighted by the country's 

currency share of foreign exchange reserves in the COFER database.

5/ Variable construction broadly consistent with that of Cubeddu et al. (2019), but uses fuel trade balance instead of oil trade balance due to limited data 

availability.

6/ Variable construction broadly consistent with that of Cubeddu et al. (2019). Uses IMF desk estimates where available, otherwise estimated by taking the 

residual of a regression of the overall fiscal balance on the output gap. Unlike Cubeddu et al. (2019), which uses a country-specific regression approach, we 

prefer a pooled Ordinary Least Squares (OLS) fixed-effect regression specification. Doing so allows us to control for country-specific factors while 

simultaneously exploiting a much larger regression sample.

7/ Variable construction broadly consistent with that of Cubeddu et al. (2019), using a two-stage regression approach for instrumentation. Here, the first-stage 

regression uses the VIX index instead of U.S. corporate spreads as an instrument to proxy global risk aversion, and uses the ICRG democratic accountability 

sub-index instead of the Polity democracy ranking index as an instrument to proxy country-specific degrees of democracy. In line with Cubeddu et al. (2019), 

the first-stage regression uses a pooled OLS approach, and also controls for the independent model regressors.

8/ Uses methodology consistent with Treasury’s Macroeconomic and Foreign Exchange Policies of Major Trading Partners of the United States. Estimates are 

normally based on publicly available data for intervention on foreign asset purchases by authorities, or estimated based on valuation-adjusted foreign exchange 

reserves. This adjustment requires assumptions about both the currency and asset composition of reserves in order to isolate returns on assets held in reserves 

and currency valuation moves from actual purchases and sales, including estimations of transactions in foreign exchange derivatives markets. Estimates can 

also be based on alternative data series when they provide a more accurate picture of foreign exchange balances than estimates derived from changes in 

valuation-adjusted reserves. 
27

Table B1. GERAF Data Sources

Variable* Sources** Notes

9/ Variable construction broadly consistent with that of Cubeddu et al. (2019), using a two-stage regression approach for instrumentation. Here, the first-stage 

regression uses the same measures of global accumulation of reserves and reserve adequacy, but they are expressed relative to the global weighted-average 

instead of the emerging market average. In line with Cubeddu et al. (2019), the first-stage regression includes a dummy for emerging markets to control for 

emerging market-specific dynamics. The first-stage regression uses a pooled OLS approach, and also controls for the independent model regressors.

10/ Defined as instrumented FXI/GDP interacted with capital account openness.

11/ Variable construction broadly consistent with Turner and Van’t dack (1993).

12/ Defined as inflation rate divided by one plus the rate of inflation.
28

Appendix C: List of Countries

Argentina Malaysia

Australia Mexico

Austria Morocco

Belgium Netherlands

Brazil New Zealand

Canada Nigeria

Chile Norway

China Pakistan

Colombia Peru

Costa Rica Philippines

Czech Republic Poland

Denmark Portugal

Egypt Russia

Finland South Africa

France Spain

Germany Sri Lanka

Greece Sweden

Guatemala Switzerland

Hungary Thailand

India Tunisia

Indonesia Turkey

Ireland United Kingdom

Israel United States

Italy Uruguay

Japan Vietnam

Korea

Table C1. List of Countries
29

Appendix D: Robustness Checks and Regression Extensions

(1) (2) (3) (4) (5) (6) (7) (8)

GERAF 

Baseline 

(PCSE)

Pooled 

OLS

Pooled 

OLS

Pooled 

OLS

Pooled 

OLS PCSE PCSE PCSE

Cyclical factors

Output gap # -0.344*** -0.189*** -0.307*** -0.193*** -0.313*** -0.355*** -0.350*** -0.355***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Commodity TOT gap 0.258*** 0.129 0.255*** 0.130 0.291*** 0.331*** 0.260*** 0.356***

(0.000) (0.106) (0.003) (0.112) (0.001) (0.000) (0.000) (0.000)

Macroeconomic Fundamentals

Trade openness/GDP # 0.019*** 0.017*** 0.054*** 0.015*** 0.050*** 0.035*** 0.018*** 0.033***

(0.002) (0.000) (0.000) (0.000) (0.000) (0.003) (0.004) (0.003)

L. NFA/GDP 0.027*** 0.038*** 0.028*** 0.035*** 0.023*** 0.007 0.024*** 0.001

(0.000) (0.000) (0.000) (0.000) (0.000) (0.294) (0.000) (0.900)

L. NFA/GDP * (Dummy if L.NFA/GDP < -60%) 0.004 -0.003 -0.026* 0.002 -0.026* 0.003 0.012 0.008

(0.712) (0.766) (0.088) (0.836) (0.093) (0.812) (0.325) (0.530)

L.Output per worker, relative to top 3 economies 0.034 0.035** 0.050* 0.039** 0.034 0.036 0.037* 0.015

(0.105) (0.034) (0.100) (0.020) (0.243) (0.299) (0.082) (0.675)

Real GDP growth, forecast in 5 years # -0.254*** -0.508*** -0.492*** -0.516*** -0.522*** -0.278*** -0.251*** -0.278***

(0.004) (0.000) (0.000) (0.000) (0.000) (0.003) (0.004) (0.003)

Safe asset index -0.039** -0.039*** -0.022 -0.045*** -0.051*** -0.019 -0.051*** -0.056***

(0.029) (0.001) (0.170) (0.001) (0.003) (0.319) (0.007) (0.008)

Structural Fundamentals

Demographic block

Old-age dependency ratio # -0.114*** -0.101*** -0.194*** -0.111*** -0.189*** -0.132** -0.120*** -0.106*

(0.005) (0.000) (0.000) (0.000) (0.000) (0.028) (0.004) (0.083)

Population growth # -0.622* -0.601*** -1.156*** -0.566** -0.792** -0.726* -0.520 -0.314

(0.059) (0.007) (0.000) (0.010) (0.012) (0.079) (0.117) (0.463)

Prime savers share # 0.227*** 0.236*** 0.071* 0.243*** 0.078** 0.083 0.243*** 0.092

(0.000) (0.000) (0.069) (0.000) (0.049) (0.151) (0.000) (0.109)

Life expectancy at prime age # -0.007*** -0.006*** -0.002 -0.006*** -0.003 -0.000 -0.006*** -0.002

(0.000) (0.000) (0.329) (0.000) (0.130) (0.930) (0.000) (0.475)

Life expectancy at prime age # * Future OADR 0.017*** 0.014*** 0.016*** 0.013*** 0.014*** 0.014* 0.016*** 0.010

(0.000) (0.000) (0.003) (0.000) (0.008) (0.058) (0.000) (0.161)

Institutional/political environment (ICGR-12) # -0.058*** -0.076*** -0.026 -0.075*** -0.027 -0.025 -0.065*** -0.033

(0.001) (0.000) (0.232) (0.000) (0.245) (0.251) (0.000) (0.147)

Oil and natural gas trade balance * Resource temporariness # 0.515*** 0.527*** 0.715*** 0.522*** 0.624*** 0.814*** 0.506*** 0.739***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Policy Variables

Cyclically-adjusted fiscal balance (instrumented) # 0.527*** 0.756*** 0.405*** 0.726*** 0.296* 0.272** 0.524*** 0.207

(0.000) (0.000) (0.010) (0.000) (0.057) (0.032) (0.000) (0.105)

L.Public health spending/GDP # -0.254* -0.254** -0.646*** -0.230** -0.651*** -0.602*** -0.237* -0.612***

(0.064) (0.011) (0.000) (0.024) (0.000) (0.001) (0.088) (0.001)

FX Intervention

FXI/GDP (instrumented) # 0.700*** 1.082*** 0.522** 1.219*** 0.610** 0.559** 0.694*** 0.532**

(0.003) (0.000) (0.035) (0.000) (0.033) (0.012) (0.006) (0.027)

FXI/GDP (instrumented) # * K openness -0.510* -0.667** -0.077 -0.631** -0.087 -0.358 -0.437 -0.294

(0.071) (0.018) (0.800) (0.031) (0.792) (0.180) (0.135) (0.290)

Detrended private credit/GDP # -0.095*** -0.108*** -0.071*** -0.102*** -0.064*** -0.078*** -0.092*** -0.072***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Capital Controls

L.Relative output per worker * K openness 0.021 0.015 0.012 0.016 0.020 0.004 0.025 0.014

(0.359) (0.383) (0.650) (0.352) (0.455) (0.876) (0.277) (0.628)

L.demeaned VIX * K openness 0.028** 0.015 0.040*** -0.048 -0.041 0.044*** -0.031 -0.038

(0.033) (0.335) (0.004) (0.372) (0.387) (0.001) (0.455) (0.350)

L.demeaned VIX * K openness * Safe asset index -0.064 -0.012 -0.116 0.049 -0.060 -0.156* -0.017 -0.105

(0.554) (0.940) (0.236) (0.753) (0.545) (0.073) (0.882) (0.237)

Constant -0.019*** -0.016*** -0.040** -0.025*** -0.039** -0.037* -0.031*** -0.031

(0.000) (0.000) (0.017) (0.008) (0.028) (0.056) (0.000) (0.123)

Country-fixed effects No No Yes No Yes Yes No Yes

Time-fixed effetcs No No No Yes Yes No Yes Yes

Observations 1,279 1,279 1,279 1,279 1,279 1,279 1,279 1,279

Number of countries 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 1

R-squared 0.392 0.613 0.743 0.627 0.757 0.540 0.419 0.566

RMSE 0.019 0.030 0.025 0.030 0.024 0.018 0.019 0.018

Table D1. GERAF Current Account Model: Alternative Estimators

"L." denotes variables expressed using a one year lag. "#" denotes variables expressed relative to the annual world GDP-weighted average. P-values in parentheses. OLS standard 

errors are robust to heteroskedasticity; PCSE standard errors are robust to heteroskedasticity, autocorrelation and cross-sectional dependence. PCSE regressions include a panelwide AR(1) correction to control for potential autocorrelation in the dependent variable. ***, **, * next to a number indicate statistical significance at 1, 5 and 10 percent, 

respectively.

Source: U.S. Treasury staff calculations.
30

(1) (2) (3) (4) (5) (6) (7)

GERAF 

Baseline

Cyclical factors

Output gap # -0.344*** -0.372*** -0.368*** -0.380*** -0.380*** -0.369*** -0.370***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Commodity TOT gap 0.258*** 0.298*** 0.309*** 0.246* 0.239*

(0.000) (0.000) (0.000) (0.073) (0.083)

Commodity TOT gap * Trade openness 0.446*** 0.069 0.488*** 0.121

(0.000) (0.784) (0.000) (0.633)

Macroeconomic Fundamentals

Trade openness/GDP # 0.019*** 0.020*** 0.022*** 0.021*** 0.021***

(0.002) (0.001) (0.000) (0.001) (0.001)

L. NFA/GDP 0.027*** 0.029*** 0.028*** 0.030*** 0.030*** 0.028*** 0.029***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

L. NFA/GDP * (Dummy if L.NFA/GDP < -60%) 0.004 0.005 0.004 0.003 0.002 0.006 0.005

(0.712) (0.707) (0.768) (0.806) (0.878) (0.626) (0.691)

L.Output per worker, relative to top 3 economies 0.034 0.022 0.025 0.032 0.032 0.025 0.026

(0.105) (0.286) (0.228) (0.133) (0.122) (0.238) (0.220)

Real GDP growth, forecast in 5 years # -0.254*** -0.223** -0.225*** -0.249*** -0.250*** -0.228*** -0.229***

(0.004) (0.011) (0.010) (0.004) (0.004) (0.009) (0.009)

Safe asset index -0.039** -0.028 -0.051*** -0.050*** -0.034* -0.033*

(0.029) (0.129) (0.008) (0.009) (0.071) (0.075)

Reserve currency status -0.046***

(0.006)

Structural Fundamentals

Demographic block

Old-age dependency ratio # -0.114*** -0.103** -0.095** -0.123*** -0.121*** -0.107** -0.105**

(0.005) (0.013) (0.021) (0.004) (0.004) (0.011) (0.012)

Population growth # -0.622* -0.714** -0.743** -0.659** -0.657** -0.701** -0.697**

(0.059) (0.032) (0.028) (0.050) (0.050) (0.037) (0.037)

Prime savers share # 0.227*** 0.207*** 0.199*** 0.229*** 0.224*** 0.216*** 0.211***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Life expectancy at prime age # -0.007*** -0.006*** -0.007*** -0.006*** -0.006*** -0.006*** -0.006***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Life expectancy at prime age # * Future OADR 0.017*** 0.017*** 0.018*** 0.014*** 0.014*** 0.017*** 0.017***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Institutional/political environment (ICGR-12) # -0.058*** -0.051*** -0.052*** -0.047*** -0.046*** -0.052*** -0.051***

(0.001) (0.004) (0.003) (0.009) (0.009) (0.003) (0.003)

Oil and natural gas trade balance * Resource temporariness # 0.515*** 0.510*** 0.487*** 0.472*** 0.473*** 0.508*** 0.508***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Policy Variables

Cyclically-adjusted fiscal balance (instrumented) # 0.527*** 0.517*** 0.489*** 0.573*** 0.568*** 0.522*** 0.518***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

L.Public health spending/GDP # -0.254* -0.338** -0.347** -0.259* -0.256* -0.336** -0.333**

(0.064) (0.014) (0.012) (0.058) (0.060) (0.015) (0.015)

FX Intervention

FXI/GDP (instrumented) # 0.700*** 0.664** 0.668*** 0.686*** 0.643** 0.661**

(0.003) (0.011) (0.009) (0.007) (0.014) (0.012)

FXI/GDP (instrumented) # * K openness -0.510* -0.488 -0.515 -0.515* -0.488 -0.489

(0.071) (0.119) (0.101) (0.099) (0.124) (0.122)

(FX Reserves)/GDP (instrumented) # 0.287***

(0.000)

Detrended private credit/GDP # -0.095*** -0.098*** -0.096*** -0.100*** -0.100*** -0.097*** -0.097***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Capital Controls

L.Relative output per worker * K openness 0.021 0.035 0.034 0.027 0.026 0.032 0.031

(0.359) (0.118) (0.138) (0.235) (0.253) (0.159) (0.172)

L.demeaned VIX * K openness 0.028** 0.023* 0.020 0.017 0.021 0.021 0.024*

(0.033) (0.074) (0.178) (0.189) (0.116) (0.116) (0.070)

L.demeaned VIX * K openness * Safe asset index -0.064 -0.031 -0.008 -0.029 -0.023 -0.042

(0.554) (0.777) (0.946) (0.806) (0.837) (0.703)

L.demeaned VIX * K openness * Reserve currency status 0.106

(0.347)

Constant -0.019*** -0.020*** -0.019*** -0.015*** -0.015*** -0.020*** -0.020***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Observations 1,279 1,291 1,291 1,303 1,303 1,291 1,291

Number of countries 5 1 5 1 5 1 5 1 5 1 5 1 5 1

R-squared 0.392 0.381 0.379 0.363 0.369 0.376 0.382

RMSE 0.019 0.019 0.019 0.019 0.019 0.019 0.019

Table D2. GERAF Current Account Model: Robustness

Instrumented variables in each alternate specification control for the independent model regressors in their respective specification. "L." denotes variables 

expressed using a one year lag. "#" denotes variables expressed relative to the annual world GDP-weighted average. P-values in parentheses. Standard errors 

are robust to heteroskedasticity, autocorrelation and cross-sectional dependence. Regressions include a panel-wide AR(1) correction to control for potential 

autocorrelation in the dependent variable. ***, **, * next to a number indicate statistical significance at 1, 5 and 10 percent, respectively.

Source: U.S. Treasury staff calculations.
31

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

GERAF 

Baseline

Cyclical factors

Output gap # -0.344*** -0.356*** -0.356*** -0.367*** -0.359*** -0.368*** -0.372*** -0.398*** -0.398*** -0.383*** -0.369***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Commodity TOT gap 0.258*** 0.302*** 0.243*** 0.303*** 0.243*** 0.288*** 0.294*** 0.304*** 0.298*** 0.282*** 0.276*** 0.346*** 0.295***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Macroeconomic Fundamentals

Trade openness/GDP # 0.019*** 0.022*** 0.026*** 0.023*** 0.026*** 0.020*** 0.021*** 0.021*** 0.020*** 0.015** 0.015** 0.025*** 0.020***

(0.002) (0.000) (0.000) (0.000) (0.000) (0.001) (0.001) (0.001) (0.001) (0.021) (0.020) (0.000) (0.001)

L. NFA/GDP 0.027*** 0.029*** 0.025*** 0.029*** 0.025*** 0.028*** 0.028*** 0.028*** 0.029*** 0.028*** 0.028*** 0.035*** 0.029***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

L. NFA/GDP * (Dummy if L.NFA/GDP < -60%) 0.004 0.005 0.007 0.005 0.007 0.006 0.006 0.004 0.004 0.007 0.008 0.004 0.005

(0.712) (0.699) (0.596) (0.704) (0.600) (0.632) (0.650) (0.717) (0.710) (0.550) (0.491) (0.803) (0.696)

L.Output per worker, relative to top 3 economies 0.034 0.034 0.030 0.034 0.030 0.024 0.021 0.020 0.026 0.048** 0.047** -0.003 0.027

(0.105) (0.160) (0.239) (0.163) (0.243) (0.248) (0.314) (0.354) (0.211) (0.036) (0.040) (0.911) (0.214)

Real GDP growth, forecast in 5 years # -0.254*** -0.244** -0.289*** -0.244** -0.289*** -0.235*** -0.229** -0.231*** -0.260*** -0.259*** -0.265*** -0.168 -0.232***

(0.004) (0.012) (0.005) (0.013) (0.005) (0.009) (0.010) (0.009) (0.003) (0.005) (0.004) (0.145) (0.008)

Safe asset index -0.039** -0.030 -0.019 -0.030 -0.019 -0.032* -0.031* -0.031* -0.028 -0.033* -0.036** -0.037* -0.032*

(0.029) (0.118) (0.296) (0.119) (0.300) (0.080) (0.099) (0.093) (0.135) (0.073) (0.049) (0.056) (0.079)

Structural Fundamentals

Demographic block

Old-age dependency ratio # -0.114*** -0.111** -0.089* -0.111** -0.089* -0.101** -0.098** -0.105** -0.089** -0.137*** -0.149*** -0.129*** -0.106**

(0.005) (0.013) (0.054) (0.013) (0.054) (0.016) (0.020) (0.011) (0.033) (0.002) (0.001) (0.005) (0.011)

Population growth # -0.622* -0.799** -1.012** -0.799** -1.012** -0.680** -0.704** -0.689** -0.090 -0.656* -0.668* -1.535*** -0.698**

(0.059) (0.042) (0.015) (0.042) (0.015) (0.046) (0.040) (0.039) (0.828) (0.060) (0.055) (0.000) (0.035)

Prime savers share # 0.227*** 0.194*** 0.157*** 0.195*** 0.158*** 0.210*** 0.207*** 0.212*** 0.182*** 0.140** 0.148** 0.206*** 0.211***

(0.000) (0.001) (0.007) (0.001) (0.007) (0.000) (0.000) (0.000) (0.001) (0.026) (0.019) (0.001) (0.000)

Life expectancy at prime age # -0.007*** -0.008*** -0.008*** -0.008*** -0.008*** -0.006*** -0.006*** -0.007*** -0.008*** -0.006*** -0.006*** -0.005*** -0.006***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000)

Life expectancy at prime age # * Future OADR 0.017*** 0.020*** 0.022*** 0.020*** 0.022*** 0.017*** 0.017*** 0.018*** 0.019*** 0.013*** 0.014*** 0.017*** 0.017***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.003) (0.001) (0.000) (0.000)

Institutional/political environment (ICGR-12) # -0.058*** -0.055*** -0.098*** -0.055*** -0.098*** -0.052*** -0.045** -0.053*** -0.062*** -0.052*** -0.051*** -0.064*** -0.050***

(0.001) (0.005) (0.000) (0.005) (0.000) (0.004) (0.011) (0.002) (0.001) (0.005) (0.006) (0.002) (0.004)

Oil and natural gas trade balance * Resource temporariness # 0.515*** 0.522*** 0.552*** 0.522*** 0.552*** 0.516*** 0.523*** 0.511*** 0.518*** 0.463*** 0.471*** 0.535*** 0.505***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Policy Variables

Cyclically-adjusted fiscal balance (instrumented) # 0.527*** 0.480*** 0.563*** 0.477*** 0.562*** 0.554*** 0.546*** 0.512*** 0.552*** 0.602*** 0.614*** 0.562*** 0.524***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

L.Public health spending/GDP # -0.254* -0.317** -0.149 -0.318** -0.149 -0.325** -0.353** -0.355** -0.329** -0.123 -0.107 -0.379** -0.325**

(0.064) (0.035) (0.340) (0.034) (0.339) (0.021) (0.012) (0.013) (0.016) (0.402) (0.467) (0.022) (0.017)

FX Intervention

FXI/GDP (instrumented) # 0.700*** 0.689** 0.837*** 0.688** 0.837*** 0.654** 0.643** 0.665** 0.622** 0.720*** 0.702*** 0.293 0.671**

(0.003) (0.012) (0.003) (0.012) (0.003) (0.013) (0.015) (0.011) (0.016) (0.007) (0.008) (0.294) (0.010)

FXI/GDP (instrumented) # * K openness -0.510* -0.505 -0.663* -0.504 -0.663* -0.478 -0.464 -0.485 -0.438 -0.553* -0.532 -0.052 -0.496

(0.071) (0.132) (0.053) (0.133) (0.053) (0.132) (0.144) (0.126) (0.161) (0.090) (0.100) (0.881) (0.117)

Detrended private credit/GDP # -0.095*** -0.094*** -0.106*** -0.094*** -0.106*** -0.100*** -0.099*** -0.097*** -0.099*** -0.094*** -0.094*** -0.093*** -0.098***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Capital Controls

L.Relative output per worker * K openness 0.021 0.024 0.029 0.024 0.029 0.032 0.035 0.035 0.026 0.003 0.005 0.058* 0.030

(0.359) (0.371) (0.292) (0.368) (0.289) (0.164) (0.131) (0.134) (0.255) (0.891) (0.854) (0.051) (0.192)

L.demeaned VIX * K openness 0.028** 0.028** 0.020 0.028** 0.020 0.024* 0.023* 0.025* 0.025* 0.011 0.008 0.023 0.024*

(0.033) (0.048) (0.161) (0.047) (0.158) (0.069) (0.075) (0.053) (0.052) (0.413) (0.524) (0.122) (0.069)

L.demeaned VIX * K openness * Safe asset index -0.064 -0.056 -0.019 -0.057 -0.019 -0.036 -0.039 -0.045 -0.057 -0.022 -0.014 0.064 -0.039

(0.554) (0.618) (0.855) (0.615) (0.851) (0.736) (0.719) (0.679) (0.597) (0.837) (0.892) (0.557) (0.726)

Real interest rates # -0.001* -0.001*

(0.074) (0.052)

Real interest rates * K openness # -0.001* -0.001*

(0.093) (0.066)

Inflation # -0.000

(0.923)

Inflation (bounded index, 0 to 1) # 0.025*

(0.063)

Share of urban population # 0.010

(0.450)

Young-age dependency ratio # -0.025**

(0.015)

Gini # -0.095***

(0.000)

Income share held by top ten percent # -0.134***

(0.000)

L.Financial center -0.014

(0.116)

Fixed exchange rate regime 0.001

(0.716)

Constant -0.019*** -0.021*** -0.025*** -0.021*** -0.025*** -0.020*** -0.021*** -0.020*** -0.018*** -0.012*** -0.011*** -0.008 -0.020***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.004) (0.009) (0.311) (0.000)

Observations 1,279 1,138 1,138 1,138 1,138 1,276 1,276 1,291 1,291 1,158 1,156 892 1,291

Number of countries 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 0 5 0 4 0 5 1

R-squared 0.392 0.379 0.327 0.379 0.327 0.379 0.378 0.381 0.384 0.403 0.405 0.417 0.386

RMSE 0.019 0.019 0.020 0.019 0.020 0.019 0.019 0.019 0.019 0.019 0.019 0.019 0.019

Instrumented variables in each alternate specification control for the independent model regressors in their respective specification. "L." denotes variables expressed using a one year lag. "#" denotes variables expressed relative to 

the annual world GDP-weighted average. P-values in parentheses. Standard errors are robust to heteroskedasticity, autocorrelation and cross-sectional dependence. Regressions include a panel-wide AR(1) correction to control 

for potential autocorrelation in the dependent variable. ***, **, * next to a number indicate statistical significance at 1, 5 and 10 percent, respectively.

Source: U.S. Treasury staff calculations.

Table D3. GERAF Current Account Model: Extensions