Download The Duration of Fixed Exchange Rate Regimes

The duration of fixed exchange rate regimes
Sébastien Wälti∗
First version: April 2004
This version: January 2005
Abstract
This paper studies the duration of fixed exchange rate regimes. We argue that the
probability of an exit from a fixed exchange rate regime depends on the time spent
within this regime. In such a context models dealing with duration data are appropriate, especially because the pattern of duration dependence may be non-monotonic
over time. We make use of both the non-parametric Kaplan-Meier estimator and
a proportional hazard specification estimated by partial maximum likelihood. The
non-parametric results show that the pattern of duration dependence exhibits nonmonotonic behaviour. This pattern differs across types of economies. The nonmonotonic behaviour persists when we control for time-varying covariates in the
proportional hazard specification. Therefore, we conclude that how long a regime
has lasted will affect the probability that it will end, in a non-monotonic fashion.
JEL Classification: F30, F31, F41.
Keywords: Exchange rate regimes, currency crises, regime transitions, duration
models.
∗
Department of Economics, Trinity College Dublin, Dublin 2, Ireland. Email: [email protected]. I am
grateful to Hans Genberg, Roberto Rigobon and Charles Wyplosz for insightful comments and suggestions.
All remaining errors are mine.
1
1
Introduction and motivation
The widespread wave of currency and financial crises that has affected developed and
emerging market economies during the last decade has brought the issue of optimal exchange rate policy back to the forefront of the research agenda in international macroeconomics. In particular, much attention has been given to exits from fixed to flexible
exchange rate regimes, their nature, as well as their macroeconomic, financial and institutional determinants. In this context, most empirical studies make use of discrete-choice
econometric specifications, whereby the dependent variable takes a value of unity whenever
an exit occurs, and zero otherwise.
This paper is about the duration of exchange rate regimes. We argue that time is an
important concept for the analysis of transitions between exchange rate regimes. More
precisely, the time spent within a given regime is likely to determine the probability that a
regime will end. Klein and Marion (1997) and Duttagupta and Otker-Robe (2003) introduce duration as an explanatory variable in a logit specification. The statistical significance
of the attached regression coefficient indicates that time matters, and its sign whether it
contributes positively or negatively to the probability of an exit. On conceptual and analytical grounds we argue that this approach is limited and that a duration model approach
will be more appropriate.
At a conceptual level duration analysis deals with the conditional probability of an event
taking place, rather than with its unconditional probability1 . The key question is: ”What is
the probability that a given regime will end at time t, given that it has lasted up to time t?”
Mathematically, conditional and unconditional probabilities are related. The difference is
really conceptual. What is the natural way of thinking about the probability that a regime
will end at some point in the future when we believe that the time spent within the regime
affects this same probability? We will reason in terms of successive rounds. Suppose that a
regime starts in period 1. In period 1, we will consider the probability that the regime will
end in period 2. In period 2, we will consider the probability that the regime will end in
period 3, conditional on the fact that the regime has lasted up to period 2. In period t > 2,
we will consider the probability that the regime will end in period t + 1, conditional on the
fact that the regime has lasted up to period t. If we believe that duration is important then
the probability of an exit at some point in the future is naturally considered as a sequence
of simpler events. This natural view of the problem emphasizes conditional probabilities,
not unconditional ones.
At an analytical level duration models allow for the easy characterization of duration
dependence. If we introduce duration as an explanatory variable in a logit regression and
look at the sign of the estimated coefficient, the probability of an exit either increases or
decreases with duration. In reality, however, there could be a non-monotonic relationship
between the probability of an exit and the duration of a regime. It could be that the
probability of an exit increases at shorter durations but that it decreases beyond a certain
time threshold.
1
This discussion draws largely on Kiefer (1988).
2
Moreover, estimation procedures in the context of duration models are able to deal
with the important problem of right-censoring. It is likely that some regimes will not be
terminated at the end of the sample period. For example, at the time of writing Hong
Kong still has a currency board arrangement. Thus, the observation for Hong Kong will
be right-censored. We believe that such observations should be taken into account. In
their analysis of exits Duttagupta and Otker-Robe (2003) simply disregard regimes which
have not ended. Ignoring such censored observations usually results in biased estimates.
Therefore, duration models are the natural approach to take when we believe that the
time spent within a given regime affects the probability of an exit from this regime. But
why is it interesting to focus on duration at all? Firstly, empirical studies based on logit
specifications which include duration as an explanatory variable conclude that it has a
significant effect on the probability of an exit. Therefore, we believe that the effect of time
deserves further investigation. Secondly, from the perspective of a policymaker, the decision
to fix the exchange rate will likely be affected by the conditional probability of an exit.
Suppose that the probability of an exit is relatively high in the first few months of a peg
and then decreases as credibility is gradually achieved. If such information is not available
to the policymaker, an observed increasing probability of exit at the beginning of the peg
could induce the policymaker to devalue the currency or abandon the regime altogether.
However, if the policymaker is aware that the probability of an exit will decrease only after
a few months, then it will be less likely that the regime will be abandoned. In general,
decisions should be made on the basis of as much information as possible. Hence, knowing
the likely impact of duration on the probability of an exit is valuable. Thirdly, duration
analysis may cast some light on recent discussions about the viability of fixed exchange rate
regimes. Some authors have argued that in a world of high capital mobility, intermediate
exchange rate arrangements are not viable. We can relate this so-called bipolar hypothesis
to duration dependence. The combination of high capital mobility and an intermediate
regime should lead to positive duration dependence: the more the regime lasts, the more
likely it will end.
This paper makes use of duration models. We study duration dependence across types of
countries using the nonparametric Kaplan-Meier estimator. Moreover, in order to identify
the determinants of the probability of an exit we estimate a semi-parametric proportional
hazard specification that allows for time-varying independent variables. Regime durations
are constructed on the basis of the de facto classification of exchange rate regimes proposed
by Reinhart and Rogoff (2004).
The results show a clear non-monotonic pattern of duration dependence. Using the nonparametric Kaplan-Meier estimator we observe that the relationship between the probability of an exit and time spent within a regime differs significantly across types of countries.
Consequently, we include time-varying explanatory variables to control for country-specific
heterogeneity. We find that inflation, openness and current account balance affect the conditional probability of an exit. Nevertheless, even after introducing time-varying covariates
the pattern of duration dependence remains non-monotonic. In other words, it seems that
the probability of an exit from a fixed exchange rate regime is affected by the time spent
within this regime, other things being equal.
3
The paper is organised as follows. Section 2 reviews the literature on exits from fixed
exchange rate regimes. Section 3 introduces important concepts for duration analysis and
presents the estimation methods. Section 4 deals with the identification of regimes, exits
and regime durations. It discusses the various classifications of exchange rate regimes
that are available and motivates our particular choice. We also describe the explanatory
variables that are included in the estimation of the semi-parametric model. Section 5
provides the results and section 6 concludes.
2
Review of the literature
This section discusses recent empirical work which aims to uncover the determinants of
exits from fixed exchange rate regimes. These studies differ along several dimensions:
exchange rate regime classification, identification of an exit, type of exit, time period,
sample of countries, econometric methodology, and explanatory variables.
The source of data varies greatly across studies. Moreover, the procedure for the
identification of an exit depends largely on the data that are used. Klein and Marion
(1997) focus on official end-of-month exchange rates retrieved from the IMF’s International
Financial Statistics and define an exit as the end of a period during which there exists
a particular fixed value of the currency with respect to the U.S. dollar. Tudela (2004)
combines data for the nominal exchange rate, the short-term interest rate and international
reserves to build an indicator variable of exchange market pressure. In this context, an
exit occurs whenever the constructed indicator exceeds a given threshold value, thereby
ending a tranquil period. Setzer (2004) uses the de facto exchange rate regime classification
proposed by Levy-Yeyati and Sturzenegger (2002).
These three studies do not focus on exits from fixed exchange rate regimes per se.
The first two are clearly more related to the literature dealing with the determinants of
currency crises, along the lines of Eichengreen, Rose and Wyplosz (1995), and with the
quest for early warning indicators of such crises, such as Kaminsky, Lizondo and Reinhart
(1998). Moreover, as we discuss in the next section, the de facto classification used by
Setzer (2004) does not identify regimes properly. Looking at the stability of the exchange
rate and other variables over time may identify an exit when some parameter describing the
exchange rate regime changes, while the underlying regime remains the same. For example,
these studies would capture an exit when the central parity of a target zone arrangement is
devalued to a new level. One parameter of the regime changes, but the regime itself has not
changed: it is still a target zone! This problem also affects the empirical evidence obtained
by Duttagupta and Otker-Robe (2003) which relies on a hybrid de facto classification of
regimes. However, the pure de facto classification proposed by Reinhart and Rogoff (2004)
and used by Asici and Wyplosz (2003) deals with the problem explicitly. Reinhart and
Rogoff (2004) focus on a five-year window to identify exchange rate regimes on the basis of
market-determined nominal exchange rates. Taking such a perspective allows for a better
definition of regime changes as opposed to changes in some regime parameter.
Empirical studies also differ in that some authors distinguish between orderly and
4
crisis-driven exits, whereas others do not. Duttagupta and Otker-Robe (2003) and Asici
and Wyplosz (2003) make such a distinction. It turns out that the determinants for these
types of exits are usually different and hence, there are good reasons to separate out orderly
and crisis-driven exits. The sample of countries is also varied, including either only OECD
countries, only Latin American countries, a selection of developed, emerging market and
other economies, or all countries.
Most of the literature makes use of logit or probit regressions, whereby the dependent
variable takes a unit value whenever there is an exit. Masson and Ruge-Murcia (2003)
estimate time-varying transition probabilities, which are specified as nonlinear functions
of explanatory variables. There are few papers that rely on duration models. Tudela
(2004) studies the determinants of currency crises. Meissner (2002) uses duration analysis
to explain the decision of countries to join the classical gold standard. Sosvilla-Rivero,
Maroto-Illera and Perez-Bermejo (2002) focus on the determinants of realignments and
exits within the ERM. In an independent contribution on the durability of fixed exchange
rate regimes, Setzer (2004) applies a duration approach. However, as we noted above, the
classification of regimes that is used remains inadequate. Moreover, Setzer (2004) uses
annual data while some regimes exhibit durations shorter than one year.
Independent variables are usually taken from two strands of literature: the prediction of
currency crises, and optimum currency area criteria. These variables can be classified under
three main headings: macroeconomic variables, such as the real exchange rate, openness,
trade concentration, fiscal policy, monetary policy, inflation, growth; financial variables,
such as international reserves, foreign assets, domestic credit, foreign direct investment;
and institutional and political variables, such as regular and irregular executive transfers,
IMF program, corruption, political freedom. Moreover, as noted above, two studies using
logit regressions include the duration of the exchange rate peg as a determinant of the likelihood of an exit. Some explanatory variables are significant across most studies: inflation,
competitiveness as measured by the evolution of the real exchange rate, and openness.
However, comparability remains difficult since the various studies do not include the same
regressors.
The vast majority of existing research ignores the issue of censoring systematically,
except for those papers using duration models. Suppose that we are focusing on exits
between time t1 and time t2 . We will observe some exits between t1 and t2 without being
able to compute the duration when the regime has started before time t1 . Conversely,
some regimes will still be in place after time t2 , so that we do not observe an exit and
are again not able to compute a duration. The literature on duration models recognizes
this issue as being very important and proposes ways to take censoring into account. To
our knowledge, the studies which include duration as an explanatory variable in a logit
framework disregard the problem. Duttagupta and Otker-Robe (2003) exclude incomplete
spells explicitly. For example, Hong Kong is excluded from the analysis since its currency
board regime is still in place as we write: there is no exit. Disregarding information is not
a solution; censoring must be taken into account.
5
3
Econometric methodology
We define the nonnegative random variable T as the length of time during which a fixed
exchange rate regime is in place2 . Such a length of time is usually referred to as a duration
or a spell. In most econometric applications this random variable would be described by its
cumulative distribution function F (t) = Pr(T < t), where 0 < t < +∞. This distribution
function specifies the probability that the exchange rate regime will last less than some
given value t. An equivalent description for continuous random variables is given by the
probability density function, given by
Pr (t < T < t + dt)
dt→0
dt
dF (t)
=
dt
f (t) = lim
(1)
This view emphasizes the unconditional probability. In duration analysis use is often
made of the survivor function. It describes the probability that the regime will last t
periods or longer, and is given by S(t) = 1 − F (t) = Pr(T ≥ t). Duration analysis focuses
on conditional probabilities. The central concept is the hazard function that describes the
instantaneous probability that the regime will end at time T = t, conditional upon survival
to time t. It is given by
Pr (t < T < t + dt|T ≥ t)
dt→0
dt
f (t)
=
S(t)
λ(t) = lim
(2)
Both representations in terms of the hazard function and the probability density function contain exactly the same information3 . The usefulness of the hazard function stems
from the fact that its shape provides an easy definition of duration dependence. Positive
duration dependence exists at the point t∗ when dλ(t)/dt > 0 at t = t∗ . It implies that
the probability that the regime will end increases as the regime increases in length of time.
Conversely, negative duration dependence exists at the point t∗ when dλ(t)/dt < 0 at
t = t∗ . The condition that dλ(t)/dt = 0 for every t defines a so-called memoryless system.
In practice, it is possible that the hazard function evolves with time in a non-monotonic
fashion. Finally, some applications require the use of the integrated hazard function which
is given by
2
3
See Kiefer (1988) and Lancaster (1990) for a thorough and complete discussion of duration analysis.
We can see that
λ(t) = −
d ln S(t)
dt
6
Λ(t) =
Z
t
λ(s)ds
(3)
0
In particular, the integrated hazard function is often used to discuss duration dependence. Moreover, it involves some smoothing so that it is sometimes easier to interpret
than the hazard function itself. For a memoryless system, the integrated hazard is simply
a straight line:
Λ(t) =
Z
t
λds
(4)
0
= λt
Moreover, a convex integrated hazard corresponds to positive duration dependence,
whereas concavity of the integrated hazard implies negative duration dependence.
3.1
Estimation methods
We now turn to the estimation methods for the hazard function. There are three broad
approaches which involve different degrees of parametrization.
3.1.1
Non-parametric estimation
The Kaplan-Meier estimator is a completely non-parametric approach. It is a graphical tool
which allows for a preliminary study of duration dependence. Assuming a homogeneous
sample of durations of size n and no censoring, the empirical survivor function would simply
be estimated as
number of T ≥ t
(5)
n
However, a modification is required to allow for censoring. We write the random time
of the end of a regime in the absence of censoring as T ∗ , and the censoring time as C.
We also define a variable δ that takes the value 1 if the observation is censored, and zero
otherwise. Then, the observed random variable is T = min(T ∗ , C).
Suppose that there are k completed durations in our sample of size n, where k < n
since some observations are censored, and because two or more observations can have the
same duration. We assume that if T = t and δ = 1, censoring happens immediately after
time T . We can order the completed durations from smallest to largest, t1 < t2 < ... < tk .
We denote the number of durations that end at time ti by di , and the number of durations
censored between ti and ti+1 by mi . The risk set is the set of durations that are eligible to
end at time ti and is defined as
b
S(t)
=
ni =
k
X
(mj + dj )
j≥i
7
(6)
The scalar ni is really the number of durations neither completed nor censored before
duration ti . Then, the probability of ending a regime in the interval t + dt given that the
spell lasts up to time t is given by
di
ni
The corresponding estimator for the survivor function is given by
b )=
λ(t
i
b ) =
S(t
i
i
Y
b )
(1 − λ
i
(7)
(8)
j=1
=
i
Y
nj − dj
j=1
ni
The Kaplan-Meier estimator is a very flexible tool. We can graph hazard functions
for various dimensions of the data. For example, we can estimate a hazard function for a
sample of countries. Then, we can split the full sample into various sub-samples according
to the type of country or to the type of exit, whether orderly or driven by a crisis.
3.1.2
Parametric estimation
The parametric approach is more demanding in that it requires making an assumption
about the underlying distribution of the random variable T . It is sometimes possible to
infer this distribution from a theoretical model. For the sake of the presentation, suppose
that the distribution of durations is known up to a vector of parameters θ. We can write
the density of a duration as f (t, θ). Assuming that we have a sample of n uncensored observations and that each duration is independent, then we can form the usual log-likelihood
function
ln L∗ (θ) =
n
X
ln f (ti , θ)
(9)
i=1
The log-likelihood function is simply the joint probability distribution of the sample as
a function of the vector of parameters θ. The vector of unknown parameters θ is estimated
by maximum likelihood. Suppose now that some observations are censored. The only
information available on a censored duration at time tj is that this duration was at least
tj . The contribution to likelihood is then given by the value of the survivor function,
S(tj , θ). We can again construct a dummy variable δ, taking a value of unity when the
observation is censored and zero otherwise. The log-likelihood function is then given by
ln L∗ (θ) =
n
X
(1 − δ i ) ln f (ti , θ) +
i=1
n
X
δ i ln S(ti , θ)
i=1
which can be rewritten in terms of the hazard and integrated hazard functions
8
(10)
ln L∗ (θ) =
n
X
(1 − δi ) ln λ(ti , θ) −
i=1
n
X
Λ(ti , θ)
(11)
i=1
In practice, the sample of durations is rarely homogeneous and is affected by various
explanatory factors. In general, duration models do not allow for an easy interpretation
of the coefficients on explanatory variables as partial derivatives. However, there are some
notable exceptions, notably the proportional hazard (PH) specification. In this paper, we
make use of such a specification. The typical PH model is specified as
λ(t, x, β, λ0 ) = λ0 (t)φ(x, β)
(12)
where x is a vector of explanatory variables, and β is a vector of unknown parameters
to be estimated. The so-called baseline hazard λ0 represents the hazard function for the
mean individual. It corresponds to the case where φ(.) = 1. Explanatory variables affect
the hazard function by multiplying the baseline hazard by a time-invariant factor φ(.).
This explains the terminology ”proportional hazard model”.
A convenient functional form for φ(.) is given by
φ(x, β) = exp(x0 β)
(13)
This specification is convenient for two reasons. First, to the extent that both λ(.)
and λ0 are conditional probabilities, they are both nonnegative numbers. Adopting a
specification such as (13) implies nonnegativity of φ(.) and thus, it does not imply any
necessary restriction on the vector of coefficients β. Second, this proportional hazard
specification allows us to interpret the estimated coefficients on explanatory variables as
the constant proportional effect of x on the conditional probability λ(.). Indeed,
∂ ln φ(x, β)
∂ ln λ(t, x, β, λ0 )
=
∂x
∂x
∂ ln(exp(x0 β))
=
∂x
= β
So long as we know the parametric form of the baseline hazard (up to a parameter), we
can write down the likelihood function, possibly allowing for censored observations, and
estimate the unknown parameters by maximum likelihood. In many practical applications,
however, the underlying distribution is unknown. When we are not willing to make an
arbitrary assumption about the form of this distribution, we can resort to semi-parametric
estimation.
3.1.3
Semi-parametric estimation
The semi-parametric approach potentially allows for circumventing the problems of both
the non-parametric estimator (no explanatory variables) and parametric estimation (arbitrary assumptions). We can introduce explanatory variables while leaving the form of the
9
baseline hazard function λ0 unspecified. The unknown parameters are estimated by partial
likelihood. In this section we sketch the intuition for this approach and show why it does
not require any assumption on the baseline hazard function.
To keep the discussion simple we assume that all n observations are uncensored. In
practice, allowing for censoring is relatively straightforward. We can order observed durations from smallest to largest, t1 < ... < tn . The conditional probability that the first
observation concludes a spell at time t1 , given that all of the n durations could have ended
at time t1 , is
λ(t1 , x1 , β, λ0 )
i=1 λ(t1 , xi , β, λ0 )
(14)
Pn
This quantity is the contribution of the first observation to partial likelihood. The
numerator is the hazard for the individual whose spell completes at time t1 , while the
denominator is the sum of the hazards for individuals whose spells could have ended at
time t1 . If we adopt the specification λ(t, x, β, λ0 ) = λ0 (t)φ(x1 , β) this ratio becomes
λ0 (t)φ(x1 , β)
φ(x1 , β)
= Pn
Pn
λ0 (t) i=1 φ(xi , β)
i=1 φ(xi , β)
(15)
When we use the partial likelihood approach, only the order of completed durations
provides information on the unknown coefficients. The baseline hazard λ0 vanishes and
therefore, it does not require an assumption on its underlying distribution. In general, the
contribution of the jth shortest observation to partial likelihood is given by
φ(xj , β)
i=j φ(xi , β)
(16)
Pn
and the log-likelihood function is obtained as
∗
ln L (β) =
n
X

ln φ(xj , β) − ln
j=1
n
X

φ(xi , β)
(17)
i=j
The partial likelihood framework can also incorporate censored observations as well
as multiple spells4 . Finally, we note that although the baseline hazard is not directly
estimated, we can recover it from the partial likelihood estimation.
3.1.4
Summary
To the extent that the parametric approach requires an assumption about the underlying
distribution of durations, and since the non-parametric Kaplan-Meier estimator does not
allow to include constant or time-varying variables, the semi-parametric model is often
4
Multiple spells occur when there are several durations for a single individual. For example, a country
can fix the exchange rate for some time, then float, and then fix again, perhaps to another currency, or at
another par value.
10
used as a reasonable compromise. It allows to control for individual-specific regressors
while the distribution of durations is left unrestricted.
In this paper we proceed in two steps. Firstly, we estimate the hazard function using the
non-parametric estimator for the whole sample and sub-samples of the data. This graphical evidence can illustrate differences in duration dependence across types of economies.
Secondly, we introduce time-varying explanatory variables to assess how these affect the
conditional probability that a given exchange rate regime will end. Indeed, it may be
that the pattern of duration dependence obtained with the non-parametric estimator is
explained by the time-varying behaviour of explanatory factors. In the extreme, we could
observe that the time spent within a fixed exchange rate regime could have no effect on
the conditional probability of an exit, once that the appropriate time-varying factors are
taken into account.
4
Data
The identification of the duration of an exchange rate regime requires the definition of
a time origin, a time scale, and an event that ends the duration. As we discussed in
the review of the literature, most studies do not deal with exchange rate regimes but
rather with spells of exchange rate stability. Focusing exclusively on the behaviour of the
nominal exchange rate or on an index of exchange market pressure may lead us to conclude
that a regime changes, when in fact only some parameter describing this regime changes.
Therefore, we need a classification that takes this problem into account. We present a
short summary of the recent attempts to build de facto classifications of exchange rate
regimes. We motivate our choice of the classification by Reinhart and Rogoff (2004) as
it allows for a five-year window which identifies regimes as opposed to changes in regime
parameters. We provide an operational definition for the identification of regime durations
and also introduce potential explanatory variables that could affect these durations.
4.1
Exchange rate regime classification
Until recently, empirical studies on exchange rate regimes relied on the classification available from the Annual Report on Exchange Arrangements and Exchange Restrictions published by the IMF. This de jure classification is based on official declarations made by
national governments once a year. It has been criticized, in particular on the ground that
countries do not always do what they say they are doing. Countries that declare a flexible
exchange rate regime often intervene in the foreign exchange market to such an extent
that in practice there is little difference in the behaviour of the nominal exchange rate
relative to countries that have explicit fixed exchange rates. Calvo and Reinhart (2002)
refer to this phenomenon as ”fear of floating”. Moreover, repeated devaluations of fixed
exchange rate parities, especially in countries prone to high inflation, make the exchange
rate regime look more like a flexible arrangement. Consequently, it is likely that a de facto
classification will differ significantly from the de jure classification.
11
Recent new classifications have been constructed to address the shortcomings of the
IMF’s de jure classification. Bubula and Otker-Robe (2002) combine qualitative and quantitative information to provide a monthly classification that covers all IMF members since
1990. Most information is obtained through bilateral consultation discussions with member
countries, as well as through regular contacts with IMF desk economists. This information
is supplemented by other sources of information, including press reports, news articles, and
other relevant papers, and supported by an analysis of the observed behaviour of the nominal exchange rate and international reserves. Ghosh, Gulde and Wolf (2002) make use of
the volatility of the nominal exchange rate and the de jure relative frequency distribution
of exchange rate regimes. The approach starts with the construction of an annual index
of flexibility based on the mean and the variance of monthly depreciation rates. Higher
values of the index should correspond to more flexible exchange rate regimes. Then, for
each year, the continuous index is mapped into a discrete number of regime categories
using the relative frequency distribution of the de jure annual classification.
Some approaches rely exclusively on observed macroeconomic time series. Shambaugh
(2003) constructs a binary classification with only fixed and flexible regimes, on the basis
on the monthly behaviour of the nominal exchange rate. A fixed exchange rate regime
is identified if the exchange rate has remained within ±2 percent bands against the base
currency during the year5 . Levy-Yeyati and Sturzenegger (2002) provide a yearly classification with five types of regimes, on the basis of the nominal exchange rate and changes in
international reserves. A fixed exchange rate regime is typically characterized by volatility
in international reserves in order to stabilize the exchange rate at the announced parity. In
contrast, floating exchange rates feature significant exchange rate volatility while foreign
exchange reserves remain rather stable6 .
Hybrid classifications such as Bubula and Otker-Robe (2002) and Ghosh, Gulde and
Wolf (2002) are problematic along various dimensions. Including qualitative information
is valuable to the extent that an exclusive focus on the nominal exchange rate, such as
Shambaugh (2003), can be misleading. It remains difficult to disentangle the respective
roles of interventions in the foreign exchange market and of the absence of shocks in
explaining the stability of the nominal exchange rate. However, qualitative analysis requires
an element of judgment about the nature of the exchange rate regime. Consequently,
the classifying procedure is not as systematic as one which focuses solely on observed
macroeconomic time series.
Despite the improvements brought about by new hybrid and pure de facto classifications, these feature two important problems that are addressed by Reinhart and Rogoff
5
In order to prevent breaks in the peg status due to periodic realignments, any exchange rate that has
had a percentage change of zero in eleven out of twelve months is also considered as fixed. Shambaugh
(2003) also computes monthly categories and identifies a fixed exchange rate for a given month whenever
the exchange rate has remained within ±2 percent bands over the previous twelve months.
6
The methodology consists of two steps. Firstly, three reference variables are computed for each year,
namely exchange rate changes, the volatility of these exchange rate changes, and changes in international
reserves. Secondly, a cluster analysis is used to classify countries into different types of regimes according
to the values of the three reference variables.
12
(2004). Firstly, the existence of dual or multiple exchange rates, and/or parallel markets, implies that market-determined exchange rates can differ significantly from officially
reported exchange rates. Failing to look at market-determined rates leads to misleading
perceptions about the underlying monetary policy and the ability of the economy to adjust
to shocks. For example, suppose that the exchange rate in a parallel market undergoes
continuing depreciation while the official rate remains fixed at some predefined level. The
underlying monetary policy is inflationary, but looking at the latter rate does not allow
to capture the policy stance. Eventually, the likely outcome is a devaluation of the official parity, which validates the gradual depreciation of the exchange rate in the parallel
market. Secondly, other de facto classifications identify short-term spells of exchange rate
stability within a regime rather than longer-term regimes. The realignment of a central
parity in a target zone would be captured as a regime change in other classifications. This
is misleading since it is only a parameter of a regime that changes, and not the regime per
se.
Reinhart and Rogoff (2004) use historical chronologies to assess whether there are dual
or multiple exchange rates, and/or parallel markets. They then use a battery of descriptive
statistics applied to the behaviour of the market-determined nominal exchange rate and
classify countries according to fourteen categories on a monthly basis. In order to avoid
focusing on short-term spells they consider a five-year horizon. Table A1 in the appendix
replicates table 4 of their paper. Interestingly, ”whether the official (exchange rate) regime
is a float or a peg, it is virtually a coin toss whether the natural algorithm will yield the
same result”7 . In other words, the constructed de facto classification differs significantly
from the officially reported exchange rate arrangements. Many countries which declare
having floating exchange rates are best characterized by ”fear of floating”. Moreover,
many announced pegged regimes turn out to be de facto flexible regimes.
To summarize, we adopt the de facto classification constructed by Reinhart and Rogoff
(2004)8 . It is available at a monthly frequency, avoids identifying short spells of exchange
rate stability as regimes, takes account of the fact that countries may have dual or multiple
exchange rates, and/or parallel markets, and is not based on official declarations but rather
on the actions of monetary authorities.
4.2
Definition of exits and durations
In line with Asici and Wyplosz (2003) we define an exit from a fixed to a flexible exchange
rate regime as a shift from categories 1 to 11, to categories 12 to 149 . We consider the
period from January 1974 until December 2001. The time origin is defined as the first
month during which the classification index lies between 1 and 11. The event that defines
the end of a duration is the shift from categories 1 to 11, to categories 12 to 14. We then
count the number of months from the time origin until the ending event. If a regime is still
7
Reinhart and Rogoff (2004), p. 22.
See Genberg and Swoboda (2004) for a careful discussion of the relative merits of de jure and de facto
classifications.
9
See table A1 in the appendix to this paper.
8
13
in place by December 2001 we count the number of months until that date and register
the observation as being censored.
This operational definition runs into a problem of left truncation. A number of exchange
rate regimes will obviously start before January 1974. This does not affect the estimations
of non-parametric and semi-parametric specifications. The Kaplan-Meier calculation of
the hazard function makes use of the number of regimes that are eligible to fail, and this
number will capture the fact that these regimes are not at risk of failing (since they all
survived) until they come under observation. The semi-parametric approach uses only
observations at times of failure. The simple fact that a regime is observed in January 1974
means that it did not fail before. This is true for all regimes observed in January 1974,
so none of these regimes can have failed before that date. Hence, we would not have any
observation to contribute to partial likelihood.
4.3
Explanatory variables
The estimation of the proportional hazard model allows for time-varying explanatory variables. We select such variables on the basis of empirical studies dealing with the determinants of the onset of currency crises, and with the more structural factors underlying the
optimal choice of an exchange rate regime.
The explanatory variables can be classified into three groups10 . Our macroeconomic
variables include inflation, GDP growth, changes in international reserves, the budget
deficit, current account balance, and openness. We also control for the level of financial
development by including liquid liabilities of the financial sector, including the central
bank. Finally, it is likely that countries with a stronger institutional framework will be
able to sustain a fixed exchange rate regime for a longer period of time. Therefore, we
include an index of political rights as a proxy for the quality of institutions.
The introduction of time-varying covariates in the context of a cross-section of durations
can seem difficult at first sight. Indeed, Klein and Marion (1997) argue that ”we cannot
simply correlate the length of each of the 61 spells in our data set with some constant measure of each explanatory variable for any one spell using ordinary least squares or duration
analysis”11 . Yet, it remains feasible to introduce time-varying covariates in a proportional
hazard specification. The partial likelihood estimation procedure then implies that we will
use data only at times of failures. In turn, since our durations are calculated in months,
we should use monthly values of explanatory variables at times of failures. However, we
could not collect data at the monthly frequency for each time-varying covariate. When
such data are not available, we use the value for the year before that which contains the
month during which a failure occurs. This choice is problematic. Suppose that a failure
time is recorded in March 1986. Should we then use the annual value of the current year
or of the previous year? And how should we proceed when the failure occurs in July? In
September? Using the value of the previous year is an arbitrary choice. Yet, our empirical
10
11
We discuss the source and measurement of our variables in the appendix.
Klein and Marion (1997), p. 393.
14
results are broadly in line with similar studies such as Asici and Wyplosz (2003).
5
Empirical results
This section presents the empirical results in three steps. Firstly, we provide some descriptive statistics and graphs on the computed durations. We disaggregate the full sample
according to the type of countries and significant differences appear. Then, we make use
of the non-parametric estimator and calculate estimates of the hazard function, thereby
allowing for a discussion of duration dependence. Finally, we show the estimates on the
proportional hazard model which includes explanatory variables.
5.1
Descriptive analysis
Taking the full sample between January 1974 and December 2001 yields 87 durations for
58 countries. Of these, 51 observations correspond to regimes that have ended before or in
December 2001, and the remaining 36 observations are right-censored. We can disaggregate
this number between developed and emerging economies12 . Table 1 reports the number of
durations (expressed in months) for the different types of countries, while table 2 provides
summary statistics on durations. Figure 1 shows the frequencies of these durations.
Table 1: Number and type of durations (expressed in months)
Failed Censored Total
Developed
16
17
33
Emerging
35
19
54
Total
51
36
87
Several remarks are noteworthy. Regimes can last for a very long period of time.
Fourteen countries exhibit a fixed exchange rate during our entire sample. Regimes can
also be very short, lasting three months. The mean for the full sample of countries equals
140 months, about 12 years. This number is in line with that reported by Rogoff et
al. (2003) and Husain, Mody and Rogoff (2004), and contradicts previous studies on the
evolution of exchange rate regimes. For example, Klein and Marion (1997) report a mean
duration of 32 months. This difference reflects the underlying definition of an exchange
rate regime. We look at the exchange rate regime per se whereas previous studies focus
on short-term exchange rate spells.
12
Developed economies include Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland,
France, Germany, Greece, Hong Kong, Iceland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Slovenia, Spain, Sweden, Switzerland, United Kingdom, and United States. Emerging
markets include Argentina, Brazil, Bulgaria, Chile, China, Colombia, Czech Republic, Ecuador, Egypt,
Estonia, Hungary, India, Indonesia, Israel, Jordan, Korea, Latvia, Lithuania, Malaysia, Mexico, Pakistan,
Peru, Philippines, Poland, Romania, Russia, Slovak Republic, Thailand, Turkey, Uruguay, and Venezuela.
15
Table 2: Summary statistics for durations (expressed in months)
Full sample Developed Emerging
Mean
140
188
111
Median
105
196
78
Stdev
176
126
99
Min
3
3
3
Max
336
336
336
Figure 1: Duration frequencies, full sample
The median is typically smaller than the mean, reflecting the fact that the distribution
of durations is skewed to the left (see figure 1). The median for the full sample equals
105 months, about 9 years. Interestingly, disaggregating across types of countries reveals
a noticeable difference (see figure 2). The median for regimes of developed economies is
equal to 196 months, or 16 years, while emerging markets and other countries show a
smaller median value of six years and a half. In other words, fixed exchange regimes in the
developed world last significantly longer than those of emerging market economies.
We should warn the reader that our summary statistics are computed under the implicit
assumption that regimes start in January 1974 or later, and that they end in December
2001 or before. This is obviously misleading since some observations are left-truncated and
others are right-censored. However, these numbers indicate some country heterogeneity
and overall trends which are useful information for the construction of the semi-parametric
specification. When we distinguish between developed and emerging economies, the standard deviation in both sub-samples is lower than that in the full sample.
16
Figure 2: Duration frequencies, by country type
5.2
Non-parametric estimation
We can now use the non-parametric Kaplan-Meier estimator described in section 3 to deal
with duration dependence. We are especially interested in assessing whether the conditional
probability of an exit evolves over time in a non-monotonic fashion. For example, it could
be that this probability rises initially, and then decreases beyond a certain time threshold.
Figure 3 graphs the estimated hazard function (7) for the full sample13 . Clearly, it exhibits a non-monotonic pattern, initially increasing, then decreasing, then increasing again
and finally decreasing again. In other words, duration dependence cannot be qualified as
being either positive, or negative: it depends upon survival time. One possible interpretation can be cast in terms of credibility. A fixed exchange rate regime does not bring
immediate credibility and thus, the conditional probability rises. Conditional on survival
up to a certain time threshold, however, this probability will then start declining as credibility is gradually achieved. In the longer run, it is likely that the exchange rate regime
will be abandoned altogether, possibly reflecting the evolutionary nature of the optimality
of exchange rate regimes.
Our descriptive analysis shows that regime durations vary across types of countries.
Therefore, it seems appropriate to estimate separate hazard functions. Figure 4 shows that
indeed, there are pronounced differences in the shape of the hazard functions depending
on country type. Developed economies exhibit a mildly non-monotonic pattern, rising
modestly and then declining gradually. Emerging market economies are characterised by
an initial increase followed by a significant decrease. The final portion of the curve shows a
further increase and decrease of the conditional probability. This evidence is confirmed by
the graphs of the Kaplan-Meier estimates of the survivor functions (figure 5). We note that
the integrated hazard functions in figure 6 are somewhat difficult to interpret. Convexity
would reflect positive duration dependence, whereas concavity implies negative duration
dependence. From figure 6 we could conclude that these functions are close to being straight
lines, thereby indicating a memoryless system. However, a closer look reveals short-term
concave and convex portions in these curves. For example, the integrated hazard for
emerging economies seems to exhibit some convexity followed by a concave portion. These
13
The horizontal axis represents analysis time. It is defined as the first point in time when a country
becomes at risk of an exit from a fixed exchange rate regime.
17
Figure 3: Estimated hazard function, all countries
Figure 4: Estimated hazard functions, by country type
18
Figure 5: Kaplan-Meier estimates of survivor functions, by country type
Figure 6: Estimated integrated hazard functions, by country type
19
convex and concave portions correspond to the rising and then declining hazard function
in figure 4.
The bipolar hypothesis of exchange rate regimes claims that in a world of highly integrated financial markets, fixed exchange rate regimes are not sustainable in the long run
and will inevitably collapse at some point. In terms of duration dependence, the hypothesis can be interpreted as positive duration dependence. In other words, the conditional
probability of an exit will not decrease over time. Our results for emerging markets could
suggest that this hypothesis is not supported by the data since a significant portion of
the hazard function is decreasing. However, such a conclusion is doubtful for at least two
reasons. Firstly, we should control for other factors which are likely to affect the viability
of a fixed exchange rate regime, such as the degree of openness of the economy, the respective stance of fiscal and monetary policies, etc. Secondly, the bipolar hypothesis holds
that countries which are financially integrated will move to the corners, either hard pegs
or floats. Since we have aggregated hard and intermediate regimes together, we can only
examine the move from fixing to floating. In future research we intend to disaggregate the
data across different types of pegging strategies.
Are the differences between types of countries significant? In addition to our visual interpretation we can use the log-rank test of equality of survivor functions. This test belongs
to the class of non-parametric tests used to compare empirical distributions, extended to
allow for censored data. Intuitively, at each distinct exit time in the data, the contribution
to the test statistic is obtained as a weighted standardized sum of the difference between
the observed and expected number of exits in each of our three groups of countries. The
expected number of exits is obtained under the null hypothesis of no differences between
the survival histories of the three country types. The log-rank test assigns an identical
weight to each difference. Table 3 reports the results. The null hypothesis of equality of
survivor functions is rejected (p-value 0.0054). Therefore, we conclude that different types
of countries exhibit different patterns of duration dependence.
Table 3: Log-rank test for equality of survivor functions
Country type Observed exits Expected exits
Emerging
35
25.19
Developed
16
25.81
Total
51
51
To conclude the non-parametric analysis of durations we examine the hazard function that results when censored observations are excluded. The sample reduces to 51
observations. Figure 7 shows the hazard functions for developed and emerging economies
separately. Both functions indicate (close to) positive duration dependence at all times,
in particular for developed economies. Therefore, excluding censored observations affects
the nature of duration dependence dramatically. Moreover, and not surprisingly, the estimated values for the hazards increase, reflecting the bias that obtains when the problem
20
Figure 7: Estimated hazard functions, by country type, only observed exits
of right-censoring is ignored.
In general, it seems therefore that hazard functions are affected by a variety of countryspecific factors, potentially varying over time. Consequently, it is desirable to control for
such factors directly. We now turn to the estimation of the proportional hazard model by
means of partial likelihood.
5.3
Semi-parametric estimation
This section presents the results from the estimation of the proportional hazard specification. Given the presence of heterogeneity both across countries and over time, it is
desirable to control directly for time-varying explanatory factors. We start by constructing
a non-parametric estimation of the hazard function. Figure 8 shows the estimated hazard
function and a clear non-monotonic pattern of the conditional probability with a strong
initial increase followed by alternating decreases and increases. Is this pattern due to time
per se or will it change significantly when we control for time-varying explanatory factors?
We estimate two proportional hazard specifications. The more parsimonious model
includes inflation, changes in international reserves, GDP growth, current account balance,
the budget deficit, and openness. The full model consists of the same explanatory variables
to which we add the level of financial development and a proxy variable for the quality of
institutions. Table 4 summarizes the results.
The null hypothesis that the estimated coefficients are jointly zero is rejected at the
1% level in both models. A lower rate of inflation, a great degree of openness and a better
current account balance contribute to lower the likelihood of an exit. A lower budget
deficit is associated with a longer duration of a regime in the parsimonious model but this
significant effect disappears in the full model. GDP growth and changes in international
21
Table 4: Cox proportional hazard models
Variable
Model 1
Model 2
∗∗
Inflation
0.0023
0.0022∗∗
GDP growth
−0.0470
−0.0597
Reserves
−0.0586
−0.0413
∗∗∗
Openness
−0.0202
−0.0235∗∗∗
Budget deficit
0.0758∗
0.0593
∗∗∗
Current account −0.1551
−0.1789∗∗∗
Liabilities
0.6448
Political rights
−0.0742
No. of obs.
Prob(χ2 )
1250
0.0001
1086
0.0013
reserves exhibit the expected negative signs but are not significant14 . A better quality of
institutions tends to reduce the probability of an exit, as expected, but the coefficient is not
significant. A greater degree of financial development induces a higher likelihood of an exit
but again, the effect is not significant. To the extent that greater financial market depth
may coincide with enhanced capital mobility, the positive coefficient should be expected.
We can retrieve the estimated baseline hazard function from the full model. Figure
9 presents a smoothed version of this function. We conclude that even after having controlled for macroeconomic, financial and institutional variables, the shape of the hazard
function remains relatively unaffected. It still remains that it increases initially and then
alternates between decreases and increases. Two different interpretations are possible. On
the one hand, we could argue that time matters per se and that theoretical models should
thus investigate the role of duration as a potential factor affecting optimal exchange rate
policy. On the other hand, one may argue that we have not controlled for every possible
time-varying variable. This alternative view would imply that time may not matter after
all, and that we have simply omitted important covariates. We cannot state which of these
two views is correct. One possible extension would be to take account of unobserved heterogeneity explicitly, so as to control for the effect of omitted variables. Time would matter
per se if the estimated baseline hazard function still remains different from a horizontal
line. We leave this issue for further research.
14
We also ran regressions with reserves in levels, calculated as total reserves in months of imports. The
results are not affected.
22
Figure 8: Estimated hazard function, non-parametric estimator
Figure 9: Baseline hazard function, proportional hazard model
23
6
Concluding remarks
This paper studies the conditional probability of an exit from a fixed exchange rate regime.
We have argued that when time is an important dimension, the natural view of exits
emphasizes conditional probabilities instead of unconditional ones. What is the probability
that a fixed exchange rate regime will end, given that it has lasted until now? Duration
models are an appropriate tool when we believe that the time spent within a regime is an
important determinant of the probability of exiting this regime.
We use both non-parametric and semi-parametric techniques to obtain estimates of the
hazard functions. The application of the non-parametric Kaplan-Meier estimator uncovers
significant non-monotonic patterns of duration dependence. We also present evidence that
these patterns differ across types of economies. To the extent that duration dependence
may be driven by time-varying covariates, we also estimate a semi-parametric proportional
hazard specification by partial maximum likelihood. Having controlled for macroeconomic,
financial and institutional variables, we conclude that the non-monotonic pattern of duration dependence remains relatively unaffected. Therefore, it seems that the time spent
within a regime is itself a significant determinant of the probability of an exit.
The issue of duration dependence deserves further investigation. Firstly, our results
based on the estimation of the proportional hazard specification can be interpreted in two
ways. On the one hand, we can argue that time spent within a peg matters. On the other
hand, we could also claim that we have not controlled for every possible time-varying
explanatory factor, so that the pattern of duration dependence persists because of omitted
variables. We believe that this problem should be studied further by introducing more
explanatory variables, and by taking explicit account of unobserved heterogeneity.
Secondly, this paper aggregates all possible types of fixed exchange rate regimes together
into a single category. It is doubtful, however, that hard pegs such as dollarization or
currency boards exhibit the same properties as soft target zones or even moving bands of
fluctuation. Therefore, the analysis should be refined by disaggregating the fixed exchange
rate regimes themselves into two or more categories. In turn, this would increase the
number of possible exits between regimes. We could study exits from hard fixing to soft
fixing, soft fixing to hard fixing, hard fixing to floating, and soft fixing to floating. In
turn, we could also focus on exits from floating regimes, either to hard fixing or soft fixing.
There are several possible combinations of exits and these may exhibit different patterns
of duration dependence.
Disaggregating across finer categories of exchange rate regimes would also allow for
a discussion of the bipolar hypothesis in terms of duration dependence. In this paper,
we have cautioned against the interpretation of positive duration dependence as evidence
in favour of the bipolar hypothesis. In particular, the aggregation of all types of fixing
strategies within a single category implies that we can only study one corner solution, the
move towards floating exchange rates.
Thirdly, moving from a fixed exchange rate regime to floating can follow different paths.
Some exits have been orderly and characterised by little market turmoil, whereas other
exits have occurred in the form of speculative attacks with more destabilizing effects.
24
Recent research shows that different types of exits are associated with different explanatory
variables. Therefore, it would be desirable in future work to also separate exits according
to whether they are orderly or crisis-driven.
25
References
[1] Asici, A. and Wyplosz, C. (2003), ”The art of gracefully exiting a peg”, The Economic
and Social Review 34(3), 211-228
[2] Beck, T., Demirguc-Kunt, A. and Levine, R. (1999), ”A new database on financial
development and structure”, manuscript, The World Bank
[3] Bubula, A. and Otker-Robe, I. (2002), ”The evolution of exchange rate regimes since
1990: evidence from de facto policies”, IMF Working Paper 155
[4] Calvo, G. and Reinhart, C. (2002), ”Fear of floating”, The Quarterly Journal of Economics CXVII(2), 379-408
[5] Duttagupta, R. and Otker-Robe, I. (2003), ”Exits from pegged regimes: an empirical
analysis”, IMF Working Paper 147
[6] Eichengreen, B. (1994), International Monetary Arrangements for the 21st Century,
Brookings Institution, Washington D.C.
[7] Eichengreen, B., Rose, A. and Wyplosz, C. (1995), ”Exchange market mayhem: the
antecedents and aftermath of speculative attacks”, Economic Policy 21, 249-312
[8] Fischer, S. (2001), ”Exchange rate regimes: is the bipolar view correct?”, Journal of
Economic Perspectives 15(2), 3-24
[9] Genberg, H. and Swoboda, A. (2004), HEI WP
[10] Ghosh, A., Gulde, A. and Wolf, H. (2002), Exchange Rate Regimes, Choices and
Consequences, MIT Press
[11] Husain, A., Mody, A. and Rogoff, K. (2004), ”Exchange rate regime durability and
performance in developing versus advanced economies”, NBER Working Paper 10673
[12] Kaminsky, G., Lizondo, S. and Reinhart, C. (1998), ”Leading indicators of currency
crises”, IMF Staff Papers 45(1), 1-48
[13] Kiefer, N. (1988), ”Economic duration data and hazard functions”, Journal of Economic Literature 26(2), 646-679
[14] Klein, M. and Marion, N. (1997), ”Explaining the duration of exchange-rate pegs”,
Journal of Development Economics 54, 387-404
[15] Lancaster, T. (1990), The Econometric Analysis of Transition Data, Cambridge University Press
[16] Levy-Yeyati, E. and Sturzenegger, F. (2002), ”Classifying exchange rate regimes:
deeds vs. words” manuscript, Universidad Torcuato Di Tella
26
[17] Masson, P. and Ruge-Murcia, F. (2003), ”Explaining the transition between exchange
rate regimes”, CIREQ Working Paper 15
[18] Meissner, C. (2002), ”A new world order: explaining the emergence of the classical
gold standard”, NBER Working Paper 9233
[19] Obstfeld, M. and Rogoff, K. (1995), ”The mirage of fixed exchange rates”, Journal of
Economic Perspectives 9(4), 73-96
[20] Reinhart, C. and Rogoff, K. (2004), ”The modern history of exchange rate arrangements: a reinterpretation”, forthcoming in Quarterly Journal of Economics, also available as NBER Working Paper 8963
[21] Rogoff, K., Husain, A., Mody, A., Brooks, R. and Oomes, N. (2003), ”Evolution and
performance of exchange rate regimes”, IMF Working Paper 243
[22] Setzer, R. (2004), ”The political economy of exchange rate regime duration: a survival
analysis”, manuscript, University of Hohenheim
[23] Shambaugh, J. (2003), ”The effect of fixed exchange rates on monetary policy”, forthcoming in The Quarterly Journal of Economics
[24] Sosvilla-Rivero, S., Maroto-Illera, R. and Perez-Bermejo, F. (2002), ”Duration analysis
of regime changes in the Exchange Rate Mechanism”, manuscript,
[25] Tudela, M. (2004), ”Explaining currency crises: a duration model approach”, Journal
of International Money and Finance 23, 799-816
27
Appendix
Explanatory variables: measurement and source
Inflation: International Financial Statistics, IMF, line 64.
GDP growth: World Development Indicators, World Bank.
International reserves: International Financial Statistics, IMF, line 1L.
Openness: measured as the sum of exports and imports divided by gross domestic product.
World Development Indicators, World Bank.
Budget deficit: measured as budget deficit divided by gross domestic product. World
Development Indicators, World Bank.
Current account balance: measured as a percentage of gross domestic product. World
Development Indicators, World Bank.
Liquid liabilities divided by gross domestic product: measures the size of financial intermediation services provided by the entire financial sector, including the central bank, relative
to GDP. Beck, Demirguc-Kunt and Levine (1999).
Political rights: Freedom in the World survey, Freedom House.
28
Table A1: Exchange rate regimes, Reinhart and Rogoff (2004)
Regime
No separate legal tender
Pre announced peg or currency board
arrangement
Pre announced horizontal band that is
narrower than or equal to ± 2 %
De facto peg
Pre announced crawling peg
Pre announced crawling band that is
narrower than or equal to ± 2%
De facto crawling peg
De facto crawling band that is narrower
than or equal to ± 2%
Pre announced crawling band that is
wider than or equal to ± 2%
De facto crawling band that is narrower
than or equal to ± 5%
Moving band that is narrower than or
equal to ± 2%
Managed floating
Freely floating
Freely falling
29
Fine grid Coarse grid
1
1
2
1
3
1
4
5
6
1
2
2
7
8
2
2
9
2
10
3
11
3
12
13
14
3
4
5