Discussion Papers In Economics
And Business

Identifying the De Facto Exchange Rate Regime for
Moldova: A State-Space Approach
Vitalie Ciubotaru
Discussion Paper 12-10

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIdentifying the De Facto Exchange Rate Regime for
Moldova: A State-Space Approach
Vitalie Ciubotaru
Discussion Paper 12-10
May 2012

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIdentifying the De Facto Exchange Rate Regime for
Moldova: A State-Space Approach*
Vitalie Ciubotaru†

Abstract
It has been noted that there is an inconsistency between the Moldovan monetary authorities’
declared pursuit of price stability and the de facto exchange rate peg. This paper looks
into the exchange rate regime of the Moldovan leu (MDL) aiming to identify the defacto regime, to test whether it differes from the de jure regime stipulated by legislation,
whether it can be described by a basket peg (and, if so, to determine the composition ofthis basket), and whether the regime has been stable over time. The methodology usedin our analysis is the celebrated Frankel-Wei regression, to which we apply a Kalmanfilter algorithm. We show that the MDL generally follows a peg to the US dollar withvarying implicit weight and fluctuation bands. Surprisingly, despite the large share of
euro-denominated transactions on the Moldovan exchange market, and an even larger shareof euro-denominated assets, the euro has never exhibited any statistically signicant weight.
Keywords: Exchange Rate Regime, Moldovan Leu, Frankel-Wei Regression, Kalman
Filter, Empirical Fluctuation Process
JEL classification: E42, F31, P33* The author is grateful to Prof. Shinji Takagi for comments on an earlier draft.
† Vitalie Ciubotaru: Graduate School of Economics, Osaka University, 1-7, Machikaneyama, Toyonaka, Osaka

560-0043, Japan; vitalie ciubotaru@yahoo.com.

1

Introduction
Since the collapse of the USSR in 1991 and replacement of Soviet money with a national
currency in 1993, the Moldovan monetary authorities have been in search of an exchange rate
regime capable of controlling inflation, maintaining external competitiveness, and at the sametime providing sufficient flexibility and adaptability to economic developments in its main
trading partners.
The objective of this paper is to identify the de facto exchange rate regime for the Moldovanleu. In particular, we test whether it differs from the de jure regime stipulated by legislation,
whether it can be described by a basket peg (and, if so, to determine the composition of thisbasket), and whether the regime has been stable over time. The methodology used in our
analysis is the celebrated Frankel-Wei regression, to which we apply a Kalman filter algorithm.
The rest of this paper is organized as follows. Section 1 presents an overview of theframework and recent developments in Moldova’s foreign exchange and monetary policy. Section
2 reviews the economic literature on exchange rate regime identification. Section 3 describes
the data used in the analysis. Section 4 explains the methodologies. Section 5 provides aninterpretation of the results obtained. Section 6 provides an assessment of the existing regime
against the counterfactuals. Finally, Section 7 summarizes the main findings.

1

Framework of Exchange Rate and Monetary Policy

1.1

Exchange Rate Policy

Although the notion of official exchange rate is widely used in Moldova, the exchange
rate quoted daily by the National Bank of Moldova (NBM) represents a weighted average ofthe exchange rates on transactions that took place on the local exchange market during theprevious business day as reported by commercial banks. These rates only serve as a reference
point for market participants, but provide a set of credible indicators for computations, planningand research (for the exchange rate of the US dollar, see Figure 1).
The NBM has been actively intervening in the exchange market (see, for example, Figure 2)
either with the purpose of influencing the exchange rate, or in order to replenish the official
reserves, which had declined during the Russian crisis of 1998. The period before the autumn
of 2008 was characterized by a rising supply of foreign exchange, coming from exports, foreign
investment and remittances by Moldovan citizens working abroad. In order to smooth out
excessive fluctuations of the exchange rate, the NBM had intervened in the foreign exchange
market, using outright purchases, forward purchases and swaps. The main intervention currency
2

Figure 1: The exchange rate of MDL against the US dollar, daily data, January 1994 – April2012
had been the United States dollar (USD), while the amount of transactions in other currencies
was negligible. Even in 2006, when the Russian Federation suspended imports of wine from
Moldova and doubled the price of natural gas delivered to Moldova, the NBM was able toincrease the balance of foreign exchange reserves. Until September 2008, the Moldovan leu(MDL) followed an appreciation trend against the US dollar given the large inflow of foreignexchange through remittances, as well as inbound foreign investment, external loans and grants.
The trend was reinforced by partial de-dollarization, i.e. conversion of foreign exchange
denominated assets, primarily bank deposits, into MDL-denominated assets. Thus, there wasan inconsistency between inflation targeting, which was prescribed by legislation, and exchange
rate peg, which was the actual policy pursued.1
Starting from the fall of 2008, most of these trends reversed themselves: sales of foreignexchange declined (reflecting a decline in remittances), the net inflow of foreign investment
turned into a net outflow (reflecting the global crisis), and as a consequence re-dollarization of
the economy took place. As the currencies of all neighboring countries depreciated against the
US dollar, against which the MDL remained stable through early 2009, the NBM intervened
by selling US dollars in an attempt to contain the depreciation pressure. By the end of the
1 For a discussion of NBM activity in the context of general economic policy, see International Monetary Fund(IMF) (2008), pp. 12–14.

3

Figure 2: Foreign exchange market interventions, all currencies, monthly data, January 2006 –May 2012
year, however, the NBM had faced the deflationary pressure and reversed the direction ofintervention, thus increasing its foreign exchange reserves. In 2010 NBM intervened on bothsides of the market in order to smooth out the fluctuations of the exchange rate.
Another notable development over the last decade has been the rising role of the euro in
the local foreign exchange market, as well as in the denomination of bank deposits and loans.
Between 2001 and 2009 the share of the euro in local exchange market turnover rose from 7.3%
to 39.8% while that of the US dollar declined from 86.5% to 57.0%. By the end of 2009 theeuro had reached 70.2% of total foreign currency denominated bank deposits. It should be notedthat in its Reports on Monetary Policy during 2010 NBM recognized that its foreign exchangeinterventions were aimed at “smoothing out the excessive fluctuations of the exchange rate ofdomestic currency vis-à-vis the US dollar,” but gave no details as to why the US dollar was
selected as the intervention currency.

1.2

Monetary Policy

The origin of national monetary policy dates back to two events: (i) the creation of the
National Bank of Moldova, Moldova’s central bank, in 1991, and (ii) the introduction of theleu, Moldova’s national currency, in 1993. The International Monetary Fund (IMF) classifies
4the Moldovan leu as following a managed float “with no pre-determined path for the exchange
rate,” with monetary policy targeted at a monetary aggregate. In this section we will look into
the accuracy of this assessment.
The ultimate objective of monetary policy is set forth in the Law on the National Bankof Moldova. Before 2006, the law had stipulated that the bank should pursue “the stabilityof national currency,” which implied a dual objective – the internal stability of the currency,
usually interpreted as the stability of MDL purchasing power and the rate of inflation, as well as
external stability, commonly understood as the stability of the exchange rates of MDL vis-à-vis
those of its major trading partners. In June 2006 this prescription was modified, and at present
the “fundamental objective of the National Bank is to ensure and maintain price stability.”
It must be noted, however, that the mechanics of monetary policy (the channels linking the
instruments with the objectives) did not change until 2008 when the monetary authorities
abandoned monetary aggregates as the intermediate target of monetary policy.
According to Mishkin (2008), inflation targeting is a monetary policy setting that consistsof the following five elements:
1 Public announcement of numerical targets for inflation;
2 Institutional commitment to price stability;
3 Strategy relating variables to policy instruments;
4 Transparency concerning the plans, objectives, and decisions; and5 Accountability for attaining inflation objectives.
In what follows we use these elements to classify Moldova’s monetary policy framework.
Public announcement of numerical targets for inflation. For many years the NBM
has been routinely announcing, in its annual monetary policy prospects, figures on expected
inflation (among other macroeconomic and monetary indicators) for the coming year, based on
the government’s economic plans, annual budget laws, economic growth strategies and its own
forecasts. Also, for many years the monetary authorities have been publishing annual reportson their activity, including a detailed description of factors responsible for the deviation of
actual performance (in terms of inflation, monetary aggregates and exchange rates) from what
had previously been announced.
Institutional commitment to price stability. Since 2006 the legal mandate of the NBM
has included “ensuring and maintaining price stability.” NBM’s official definition of pricestability is essentially Greenspan (1996)’s definition. Quantitatively, price stability is definedin terms of the consumer price index, computed and published by the National Bureau of5

Statistics.2 Thus, a widely known and recognized measure of the price level is used and any
conflict of interests (between the institution that collects data and computes the index, and theinstitution that uses it as its objective) is avoided. In addition, NBM is mandated to pursue
other macroeconomic goals – ensuring economic growth and ensuring full employment – tothe extent that these secondary goals do not interfere with attaining the primary objective.
Information on the strategy of monetary policy. The body responsible for making andimplementing monetary policy decisions in Moldova is the Administrative Council of the
NBM. The council performs both the planning of monetary policy (by adopting annual prospects
on monetary policy) and ongoing decision-making (such as changing the official interest rates,
required reserve ratios and limits on bank credit facilities). The framework of targeting monetary
aggregates, which was in place until 2008, had a number of deficiencies:* The link between changes in monetary aggregates and changes in prices (or, equivalently,
changes in aggregates and changes in interest rates) was unstable and unpredictable inthe case of Moldova, as frequently pointed out by NBM in its annual reports.
* The instruments employed and the dimensions of monetary policy actions were determinedby an intermediary target and not by the final objective.
* Existing instruments, in general, were not able to ensure full control over money supply.
It is for these reasons that the NBM switched, in 2009, to inflation targeting as a framework ofmonetary policy.
Under the new framework, open-market operations are the main instrument of monetary
policy, aimed at keeping the short-term interest rates in the inter-bank market within a narrowcorridor of the “base” rate (interest rate on NBM’s credit).3 This, together with changes inthe base rate and other instruments, causes changes in interest rates on deposits and loans
in the entire banking system. The main problem in this transmission channel, however, is aweak link between the base rate and other interest rates, as well a low responsiveness of real
macroeconomic variables to changes in interest rates.
Transparency. Communication with the markets is performed in various forms, including
dissemination of electronic publications through the NBM’s website, press conferences, andTV interviews by NBM. Regular publications on monetary policy include “Annual Reports,”
annual “Monetary Policy Prospects,” quarterly “Reports on Monetary Policy,” quarterly “Reports
on Inflation,” and press releases. The “Monetary Policy Prospects” had been published every
year until 2009, giving an outline of the main macroeconomic indicators and the prospects
2 For CPI inflation, see Figure 3.
3 For the NBM’s base interest rate, called “short-term repo rate” prior to 2008, see Figure 4.

6

Figure 3: CPI year-on-year inflation (0.1=10%), monthly data, January 1994 – December 2011
thereof, description of monetary policy actions (instruments and objectives), and plans related
to bank supervision. It is apparent from these reports that NBM was not focusing on a single
indicator (such as inflation), but was instead pursuing a combination of objectives (price stability,
stability of monetary aggregates and liquidity of the banking system, exchange rate stability,
and the like).
In 2009 these annual prospects were succeeded by a medium-term “Strategy of Monetary
Policy for 2010-2012” with a similar content. The first report clearly stated that the central bankhad abandoned monetary-aggregate targeting in favor of inflation targeting. During 2010 NBM
continued to publish quarterly “Reports on Monetary Policy,” but this was followed by “Reports
on Inflation” from 2011. In the meantime, press releases remain the most flexible means ofpublic communication, as they contain the periodic decisions taken by the Administrative
Council (mostly pertaining to monetary policy instruments). Here, too, there has been an
innovation. Recent press releases also provide up-to-date information on monthly changes
in CPI and other indicators.
Accountability. There is some ambiguity with respect to the accountability of Moldova’smonetary authorities for achieving their objectives. The law states that NBM is “an autonomous
legal entity and is accountable to the Parliament.” This accountability involves an obligation by
NBM officials to “explain in front of the Parliament or its permanent commissions the policyof the bank” (Art. 8) and to “annually provide a report to the Parliament as to (a) evaluation of7

Figure 4: NBM’s short-term interest rate, percent per annum, monthly data, January 2001 –May 2012
economic and financial position of the country, description of monetary and foreign exchange ...
and its rationale, (b) revision and assessment of monetary and foreign exchange policy from thepreceding year”(Art. 69). Thus, the assessment of monetary policy is likely a self-assessmentby the NBM. The only sanction that the Governor or a member of the Administrative Council
can face is dismissal for an “undue attitude towards the job, which had caused substantial
damages to the bank’s interests” (Art. 27).
Assessment. On the basis of the considerations noted above, we conclude that there isenough ground to call Moldova’s monetary policy framework inflation targeting.

2

A Brief Overview of the Literature
The collapse of the Bretton-Woods system in the early 1970s and the subsequent debate

on the merits of floating versus fixed exchange rates led to the emergence of a vast literatureon exchange rate regimes. In applying this literature to Moldova we approach the issue of
exchange rate regime choice by asking the following three questions:
Regime identification – what has been the de facto, as opposed to de jure, exchange rate
regime of a country?
8Regime stability – has it changed over time?
Structural breaks – if so, how can we identify the timing of any regime shift?

2.1

Regime identification

Studies that attempt to classify exchange rate regimes fall into two types: (1) those which
focus on exchange rate variability; and (2) those which focus on exchange rate co-movements.
First, studies that fall into the first group have classified exchange rate regimes by assessing
their degree of actual flexibility, thereby offering a de facto classification scheme (see, forexample, Ghosh, Gulde, and Wolf (2002); Shambaugh (2004); Reinhart and Rogoff (2004);
Levy-Yeyati and Sturzenegger (2005)). These and other similar studies often suggest a discrepancy
between the “de jure” and “de facto” exchange rate regimes in many countries. As noted byBénassy-Quéré, Coeure, and Mignon (2006), however, different studies have applied different
methodologies, thereby often giving a different de facto assessment of the same de jure regime.
This strand of research shares a common limitation, namely, the choice of numerate currency(usually, the US dollar), against which variability is measured, is arbitrary.
Second, studies that fall in the second group have focused on finding the co-movementof the currency concerned with other currencies. These works are based on a simple linear
regression model popularized by Frankel and Wei (1994) and attempt to estimate the implicit
weights of potential anchor currencies (such as the US dollar, the Japanese yen, the euro
and other major currencies) in a currency basket framework (for recent contributions, seeBénassy-Quéré, Coeure, and Mignon (2006); and Frankel (2009)). The limitation of this type ofstudies is that, while inferring the anchor currency or currencies, they fail to measure how thecurrency concerned moves against the reference value established by the inferred anchor(s).
Some recent works employ both metrics of exchange rate regime identification; some evencombine the two (for a model incorporating both methodologies, see, for example, Frankel andWei (2007)).

2.2

Regime stability

Another aspect of the literature relevant to our case concerns whether the exchange rate
regime (however defined) is stable over time. Various methodologies have been applied to test
the stability of the regime in place, including (1) running a rolling regression, (2) runninga regression with separate intervals and (3) using Kalman-type filters to allow for varying
coefficients. First, a rolling, or recursive, regression is a procedure of repetitively computing
parameter estimates over fixed-length intervals (so-called “rolling window”) through the whole9

sample period. The resulting estimates actually assess not the “true” parameters themselves, but
the moving average of these parameters. Among studies of exchange rate regimes employing
a rolling regression are McKinnon and Schnabl (2004) for nine Asian currencies, Cavoli and
Rajan (2006) for the Indian rupee, and Ogawa and Kudo (2007) for 14 Asian currencies.
Second, regression with separate intervals is used when the sample period is marked byevents that are likely to cause a structural shift. Then, the entire period is split into shorter
sub-periods and regressions are run separately for each sub-period. Examples of such events
include an announcement of devaluation or a financial crisis. Cavoli and Rajan (2005) andFrankel and Xie (2010) have used this type of approach.
Third, another method of testing the stability of an exchange rate regime is based on
space-state modeling and Kalman filters (Kalman (1960)). In this method, a certain part of
the Frankel-Wei model is allowed to follow a dynamic process, described by the so-called
transition, or state, equation. For instance, the exchange rate in question can follow an autoregressivepath, thus modelling a crawling peg against a single currency (e.g., Moosa (2008)), or theweights of the anchor currencies can follow an autoregressive path, thus allowing for smoothchanges in the basket composition (e.g. Ogawa and Sakane (2006)).

2.3

Structural breaks

A classic test of structural changes is due to Chow (1960). However, the applicability ofthis test is limited to cases where the timing of a break is either pre-determined or known
in advance. Other cases call for different testing techniques. One group of such techniques
utilizes F-statistics, developed to test for the existence of one break when its exact location is
not known. Another group includes tests based on cumulative sums (CUSUM) or moving sums
(MOSUM) of least-square residuals, especially designed for statistical change detection. An
example of procedure for estimating the timing of breaks, based on the sum of squared residuals
in the ordinary least squares (OLS) framework, can be found in Bai and Perron (2003).
An entirely different approach to the problem of identifying the timing of structural breaks
employs regime switching models, also referred to as hidden Markov models (or chains).
This approach, originally developed by Hamilton (1989) and Engel and Hamilton (1990), andfollowed in a number of subsequent works, treats the break points (or the duration of eachregime) as random variables and not deterministic events. This is why the breaks are estimatedin terms of probability of transition from one state (exchange rate regime) to another. A specificfeature of this approach is that the number of breaks is also exogenous, given a priori and not
determined by the model.
All these approaches only look at parameters (such as the weights of currencies in a basket
10

framework), so that only a change in basket composition is considered to be a regime shift.
In reality, a change in the fluctuation band should also be considered as a regime shift. For
this reason, we follow the approach taken by Zeileis, Shah, and Patnaik (2010b). That is tosay, we estimate a regression model that includes variance as a full parameter by the maximum
likelihood (ML) method.

3

Data Description
The present paper employs daily data (5-day week, Monday to Friday, excluding holidays)on the exchange rate of the Moldovan leu (MDL) vis-à-vis the US dollar (USD), the euro(EUR), the Romanian leu (ROL), the Russian ruble (RUR) and the Ukrainian hrynvia (UAH)(Table 1 summarizes how the exchange rate regimes for these currencies are classified by
the International Monetary Fund (IMF) (2010)). The data cover the period from November
31, 2005 to November 30, 2010, and are obtained from the website of the National Bankof Moldova.4 The bilateral exchange rates between the potential anchor currencies and thenumeraire currency are computed as the cross rates of the respective currencies in terms of
MDL. The sample period is dictated by data availability.
Table 1: IMF classification of the exchange rate arrangements and monetary policy frameworks
for the Moldovan leu’s potential anchor currencies, 2010
Currency

Exchange rate arrangement

Monetary policy framework

EUR

independently floating

inflation targeting

ROL

exchange rate within crawling bands

exchange rate anchor

RUR

managed floating with no pre-determined

no explicitly stated nominal

path for the exchange rateanchor

UAH

other conventional fixed peg arrangementi

against a single currency

USD

independently floating

no explicitly stated nominal
anchorNote: (i) Fixed peg dropped in May 2008
Source: International Monetary Fund, Annual Report on Exchange Arrangements andExchange Restrictions, 2010

4 http://www.bnm.md

11

Four numeraires were alternatively used: the Australian dollar (AUD), the Swiss franc(CHF), the Japanese yen (JPY) and the IMF’s special drawing right (SDR). The overall characteristics
of data series are similar across the three national currencies (for example, in terms of standard
deviation, skewness and kurtosis), while with respect to the SDR the data behaved somewhat
differently. It is for this reason that only the results based on CHF are reported, though theestimation was performed using all four numeraires. The log returns of all currencies are
depicted in Figures 16 through 21 in the appendix. Descriptive statistics for the exchange
rate of MDL and the candidate currencies are presented in Table 2.
Table 2: Summary statistics for the bilateral exchange rates of the Moldovan leu and thepotential anchor currencies against the Swiss franc, December 1, 2005 – November 30, 2010
Variable

MeanMedianMinimum

Maximumdlog(MDL/CHF) −0.000162525

0.00000 −0.0585855

0.0490655

dlog(USD/CHF)−0.000205492

0.00000 −0.0577380

0.0495559

dlog(EUR/CHF)−0.000122528

0.00000 −0.0263092

0.0345140

dlog(RUR/CHF)−0.000271710

0.00000 −0.0453926

0.0415648

dlog(ROL/CHF)−0.000248152

0.00000 −0.0547658

0.0347819

dlog(UAH/CHF)−0.000557000

0.00000 −0.162692

0.201137

Variable

Std. Dev.

C.V.

Skewness

Ex. kurtosis

dlog(MDL/CHF)

0.00741475

45.6222−0.436013

7.83965

dlog(USD/CHF)

0.00718148

34.9477−0.182145

8.07215dlog(EUR/CHF)

0.00410041

33.4651−0.0394786

8.58042dlog(RUR/CHF)

0.00628769

23.1412−0.552588

8.26906

dlog(ROL/CHF)

0.00708952

28.5693−0.942009

6.94112dlog(UAH/CHF)

0.0152032

27.2948−0.609505

55.2554

Note: The sample period covers 1304 observations.

4

The Model

4.1

Regime identification

In order to identify the implicit weights of the potential anchor currencies, a Frankel andWei (1994) model was applied. It is a linear regression model where the rate of return (log-difference)of a given currency is related to the returns of reference currencies. Recent examples of thisapproach include Frankel and Wei (2007) and Zeileis, Shah, and Patnaik (2010b).
12

In particular, we postulate the following equation relating the value of MDL to those ofother currencies:nyk (t) = β0 + ∑ βi · xi,k (t) + ε(t)

(1)

i=1

where y is the log-return of MDL in terms of CHF (or another numeraire currency k), β0
is the average rate of depreciation, xi is each of the potential anchor currencies (i.e., USD,
EUR, ROL, RUR, and UAH), and βi(i>0) are the implied weights of these currencies. If β0
is significantly different from zero, it can signal a crawling peg. If one of the other β’s is not
significantly different from unity, it is an indication of a simple peg to that currency. If none of
the coefficients are significantly different from zero, the Moldovan leu is a floating currency.
All other cases imply a managed float for the Moldovan leu.
The rationale behind the choice of anchor and numeraire currencies. The list of
potential currencies for the basket is dictated by their use as an international means of payment(hence the US dollar and the euro), or as legal tender in Moldova’s neighbors and main trading
partners (hence the Romanian leu, Russian ruble, and Ukrainian hryvna). The numeraire
currencies were selected in such a way that they are not correlated with the model variables.
The literature suggests that typical choices are basket currencies, so-called remote currencies(freely floating currencies, to which the model variables are unlikely to be pegged) and real
prices. Based on these considerations, we selected one basket currency (SDR) and three remotefreely floating currencies (CHF, AUD and JPY). Another option would be to use a real basket,
e.g. CPI, but since such data are not available on daily basis, we cannot take advantage of thedaily data on exchange rates.
With CHF as numeraire, we use the following specific version of equation (1) for testing
purposes:
d log

MDL
USD
EUR
= β0 + β1 · d log+ β2 · d log
CHF
CHF
CHF
RUR
UAH
ROL
+ β3 · d log+ β4 · d log+ β5 · d log+ ε (2)
CHF
CHF
CHF

for the case of the Swiss franc used as numeraire, where the time index is removed for convenience.
Caution must be exercised when including the Ukrainian currency in equation (2), as Ukraine
experienced a discrete change in its exchange rate regime. That is to say, although the currency
was pegged at the rate of USD1 = UAH5.05 from April 21, 2005 to May 21, 2008, it subsequently
USD
and d log UAH
became a floating currency. This means that the two variables – d log CHF
CHF –were perfectly collinear in the first sub-period, requiring two separate regressions to be run.
Accordingly, the first regression excludes UAH from the model and coveres the first sub-period13

(December 1, 2005 to May 21, 2008), while the second regression includes UAH and coveres
the second sub-period (May 22, 2008 to November 30, 2010).

4.2

Regime stability

In order to ascertain whether the parameters obtained from the Frankel-Wei model werestable over the entire period, we introduce an additional equation, which allows the weights tovary over time in an autoregressive manner:
βi,t = βi,t−1 + ηi,t(3)

where i represents each of the potential anchor currencies. Thus, a state-space model is constructed,
with equation (1) being the signal and equation (3) being the state equation.
In order to extract the dynamics of the implicit currency weights, the state-space model
was first estimated by means of minimal (log) likelihood. Once all the system parameters are
estimated, the Kalman filter is applied and the “true,” but otherwise unobservable, weights are
obtained. The Kalman filter is a recursive method of estimating the “true” value of currency
weights at date t, given the information (the mean and the covariance, obtained by ML in theprevious step) available at date t − 1. The evolution of the implicit currency weights over time
can be seen in Figures 5 through 9.

4.3Structural breaks

In order to identify any structural breaks, we follow the approach taken by Zeileis, Shah, andPatnaik (2010b). In the first step a regression model that includes variance as a full parameter isestimated by the maximum likelihood (ML) method. The estimating functions for parametersare:ψβ (y, x, β) = (y − xT β)x(4a)ψ2σ (y, x, β, σ2 ) = (y − xT β)2 − σ2(4b)

To detect a structural break, the initial exchange rate regime should be estimated. For thispurpose, the regression is run on a short sub-period and the parameters (the currency weights
and the variance) are estimated.
In the second step, an empirical fluctuation process (EFP) is constructed, reflecting thedeviations from the estimating functions (set to be zero in ML estimation). The same fluctuation
process is extrapolated over the remaining sub-period. A change in any parameter, including
the variance, leads to a deviation of the EFP from zero. A statistical test is developed in orderto verify whether a change in parameter, and hence a change in regime (structural break), is
14

Figure 5: The weight of USD (numeraire:

Figure 6: The weight of EUR (numeraire:

Swiss franc)

Swiss franc)

Figure 7: The weight of ROL (numeraire:

Figure 8: The weight of RUR (numeraire:

Swiss franc)

Swiss franc)

Figure 9: The weight of UAH (numeraire:
Swiss franc)
significant. Finally, setting a minimal duration of one regime, it is possible to determine theoptimal number of regimes during the sample period and to estimate the probable timings (andconfidence intervals) of regime changes. The timings of regime changes are determined basedon the Bayesian information criterion.

15

5

Regression Results
The results of the Frankel-Wei regressions are presented in Tables 3 and 4. In the first

sub-period, two potential anchor currencies proved to have weights significantly different fromzero: the US dollar with a weight of 0.937 and the Russian ruble with 0.094. This is anindication of exchange rate pegging on the part of the Moldovan leu. However, the weights do
not sum up to unity, which means that the peg is not tight, but allows for some margins. The
R2 of 0.957 is another indication of a margin of fluctuation. This result is surprising in so far as
the weight of the euro was found statistically not significant, when it was the currency of manyof Moldova’s major trading partners as well as an important means of international payments
for Moldova.
Table 3: OLS estimates of the Frankel-Wei regression, December 1, 2005 through May 21,
2008
Dependent variable: dlog(MDL/CHF)
coefficientstd. error

t-ratiop-value

const

0.000320096

9.18569e–05

3.48470.0005***dlog(USD/CHF)

0.937470

0.0276439

33.91230.0000***dlog(EUR/CHF)-0.0433231

0.0358396-1.2088

0.2272dlog(ROL/CHF)

0.0129818

0.0107667

1.20570.2284dlog(RUR/CHF)

0.0938480

0.0461921

2.03170.0426R2**

0.957304

Notes: HAC standard errors; UAH is excluded as an independent variable; the Swiss franc is used as
numeraire; the subperiod includes 645 observations; *** and ** denote statistical significance at the 99
and 95 percent level, respectively.

In the second sub-period, when the Ukrainian monetary authorities allowed UAH to fluctuate,
UAH though included was not significant. Neither was the Russian ruble, while the weight of
the US dollar, the only significant currency, rose to 0.982. The overall strength of the model
declined, however, with R2 falling to 0.88.
The application of the Kalman filter has uncovered instability in the implicit weights of thepotential anchor currencies. Large fluctuations in the weights of all currencies observed at the
beginning of the sample period is probably due to the absence of initial data (with diffuse priordata, the filter “calibration” takes some time), so the results for the first month or two may need
to be disregarded.
After the initial period of one or two months, the weight of the US dollar remained close
to or above unity until mid-2008, when it fell to the range of 0.9–0.95. By the end of 200916

Table 4: OLS estimates of the Frankel-Wei regression, May 22, 2008 through November 30,
2010
Dependent variable: dlog(MDL/CHF)
coefficientstd. error

t-ratiop-value

const-0.000245990

0.000219518-1.121

0.2629dlog(USD/CHF)

0.982246

0.0179140

54.83

1.41e–246

dlog(EUR/CHF)-0.00247823

0.0311975-0.07944

0.9367dlog(ROL/CHF)

0.00195484

0.0153824

0.12710.8989dlog(RUR/CHF)

0.0138570

0.0265583

0.52180.6020dlog(UAH/CHF)

0.00475704

0.00327540

1.452

0.1469R2***

0.880691

Notes: HAC standard errors; independent variables include all candidate currencies; the Swiss franc isused as numeraire; the subperiod includes 659 observations; *** denotes statistical significance at the
99 percent level.

it had increased again and remained around 0.97 through the end of the sample period. The
Russian ruble was a counterpart to this movement: its weight was close to or below zero until
mid-2008, when it sharply increased to about 0.1. By the end of 2009 it had gradually declined
and then fluctuated around zero through the end of the sample period. The weights of theremaining three currencies gravitated around zero, which is consistent with the results fromthe Frankel-Wei model. These results seem to hold regardless of which numeraire was used:
Figures 37 trough 41 in the appendix show the weights of each anchor currency when different
numeraires are used. They all show that the results are quite robust.
The strategy to test for identifying the regime shift was applied as follows. First, to estimate
the parameters of the initial exchange rate regime, a period of four months (December 1, 2005
to March 31, 2006) was chosen. The results from this regression are summarized in Table 5. We
can see that only the USD coefficient was significantly different from zero (but not significantly
different from unity), thus indicating a tight peg to the US dollar during these four months. The
R2 of the regression is 0.997, given the extremely low standard deviation (σ = 0.03). If wecompare this result to the average weights for the entire period and to the results from the
Kalman filter, it becomes clear that this four-month period is not typical, that the regime is very
likely to change soon, and that multiple breaks will be detected.
Second, an empirical fluctuation process was constructed for this period (see Figure 10). For
this, on every observation date the stability of parameter was tested, and test scores obtained.
An empirical fluctuation process is the resulting time series, capturing deviations from the null
hypothesis of parameter stability. This and all remaining computations were performed in R
17

Table 5: OLS estimates of currency weights in the initial exchange rate regime, using
observations December 1, 2005 – March 31, 2006
coefficient Estimate Std. Error t value
Prob.
(Intercept)-0.015501

0.003375-4.5931.61e–05***

USD

1.019833

0.020342

50.1350.0000***EUR-0.011086

0.028874-0.3840.702

ROL-0.008864

0.011094-0.7990.427

RUR-0.011456

0.019986-0.5730.568

UAH-0.011412

0.012326-0.926

0.357

Residual standard error: 0.03029 on 80 degrees of freedom
Multiple R2 : 0.9969

Adjusted R2 : 0.9967

Notes: The Swiss franc is used as numeraire; the subperiod includes 87 observations; *** denotes
statistical significance at the 99.9 percent level.

language using Fxregime software package.5
Third, this process is extended to cover one year (see Figure 11) and critical values (theboundaries) of test scores are calculated. As expected, in less than two months the varianceof most parameters increases and approaches the theoretical boundaries (variance of ROL andvariance of σ even crossing the upper boundary). This suggests that a transition to a muchsofter peg is taking place. The number and dates of breakpoints are reported in Table 6. Two
structural breaks are detected, and thus three distinct regimes are identified, within a one-year
period. The first break is likely to have taken place between the May 8 and May 19, 2006,
with May 18 being the most probable date. The second one is dated as June 15, with the 90%
confidence interval between June 14 and June 26.
Finally, the segments enclosed between these dates are estimated by ordinary least squares
and changes between regimes are clearly visible. During the first regime (Dec 1, 2005 to May
18, 2006) MDL was following a tight peg to the US dollar, the weight of the US dollar being
1.014244 and the R2 equal to 0.9979. In the second regime (May 19, 2006 to June 15, 2006)
the peg was much more relaxed, with the weight of 0.98313 and the R2 of only 0.9508. The
third period (June 16, 2006 to November 30, 2006) is a return to a tight peg, with the weight of
the US dollar at 1.0063857, and the R2 of 0.998.

5 See software manual in Zeileis, Shah, and Patnaik (2010a)

18

Figure 10: The empirical fluctuation processes for potential basket currencies with their 95%
confidence intervals, December 2005 – March 2006

6

Assessment of Moldova’s Exchange Rate Regime
In order to assess the economic implications of Moldova’s exchange rate policy, we consider

its effects upon Moldova’s external economic position. The key indicator describing the valueof domestic currency (relative to a set of foreign currencies) and the competitive position of acountry is the real effective exchange rate. Thus we analyze the evolution of the MDL’s realeffective exchange rate (REER) over the selected period in order to gain an understanding of
how Moldova’s exchange rate regime influenced the competitiveness of exports.

19

Figure 11: Monitoring the fluctuation processes from the potential basket currencies, April
2006 – November 2006

6.1

Effective exchange rates

According to its Annual Reports, the National Bank of Moldova calculates the nominal andthe real effective exchange rates of the MDL, but does not disclose the data series, necessitatingthat we compute our own measures of nominal and real effective exchange rates (EER). To
do so, we used end-of-month bilateral exchange rates of the MDL against the currencies ofMoldova’s main trading partners (11 countries comprising 10 different currencies) for theperiod between November 2005 and December 2010. The data were obtained from the NBM

20

Table 6: Probable dates and confidence intervals of structural breaks between December 1,
2005 and November 30, 2006
Breakpoints at observation number:5%

breakpoints

95%

1

112

120

121

2

139

140

147

Breakpoints at observation date:5%

breakpoints

95%

1

2006-05-08

2006-05-18

2006-05-19

2

2006-06-14

2006-06-15

2006-06-26

and central banks of the respective countries. For the real effective exchange rate, we usedmonthly consumer price indexes for Moldova and the same 11 countries, covering the same
period, which we obtained from the countries’ statistical bodies.
Table 7: The weights of foreign currencies in MDL’s effective exchange rate
Country
Currency code
Weight, %UkraineUAH

21.38162641

RomaniaRON

21.05031987

Russian Federation RUR

20.66624455

Italy

EUR

8.78678942

GermanyEUR

8.52213383

Belorussia

BYR

5.46723930

TurkeyTRY

4.61107211

Poland

PLN

3.91049590

China

CNY

2.97903706

United Kingdom

GBP

1.80128388

Kazakhstan

KZT

0.82375767--

100.00The weighting scheme used is as follows. Moldova’s main trading partners were orderedby trade share and the first 11 countries, covering about 80% of Moldova’s trade in goods,
were selected. The weight of each country was computed, based of the share of that country
in Moldova’s total exports and imports during 2005–2009. The resulting weights are presented
in Table 7. Though two countries (Germany and Italy) use the same currency, it was necessary
to disaggregate them in order to take into account the difference in their price levels. Their
21

combined shares do not include Moldova’s trade with the other euro-zone countries.
The nominal effective exchange rate (NEER) was computed according to the following
formula:NEERt = ∏(i

Eit wi)Ei0(5)where Eit is the bilateral exchange rate between the currency of partner i and the MDL in montht, Ei0 is the exchange rate in the base period, and wi is the weight of the respective currency.
All bilateral exchange rates are in indirect quotation, i.e. the price of one unit of the MDL interms of foreign currency. The resulting NEER index shows a change in the value of the MDL
vis-à-vis a weighted basket of foreign currencies from the base period. Values higher thanunity mean a nominal effective appreciation of the MDL, while values lower than unity meana nominal effective depreciation. For illustrative purposes, the base period is set to coincidewith the beginning of the dataset (end of November 2005), so that the NEER and REER for
November 2005 are equal to unity.
As to the real effective exchange rate, it is obtained by deflating the NEER according to thefollowing formula:
REERt = NEERt

Pt∑i wi Pit

(6)

where Pit is the CPI of partner i in month t, and Pt is the CPI of Moldova in month t. The
resulting REER index shows the real average value of the MDL, relative to Moldova’s partnercountries. Values higher that unity mean a real effective appreciation of the MDL, and viceversa. CPI was chosen as the basis to construct the NEER deflator, because it was the only
price index available for all countries and for all periods.
6.1.1

MDL’s NEER and REER during 2005–2010

The evolution of the Moldovan leu’s NEER is presented in Figure 12. Since the beginningof the period the Moldovan leu had been depreciating in nominal effective terms until November
2006. At its trough, the MDL lost 10% of its value from the base period. Starting from
December 2006, the depreciation tendency reversed itself: during 2007 and especially during
2008 the MDL gained almost 40% in value. At its peak, in February 2009, it was priced about
44% more then in the base period. Subsequently, the MDL generally followed a depreciation
trend, but remained higher than it was in the base period.
The movements of the REER for the most part reflect the movements of the NEER (seeFigure 12). However, large fluctuations from November 2005 to February 2009 were muchless pronounced given the compensating changes in the EER deflator: whenever the currency
depreciated, the domestic prices rose more quickly, while the reverse held true when it appreciated.

22

As a result, between November 2005 and the beginning of 2009, the domestic prices changed
almost in proportion to the trading partners’ prices. During the rest of the period, the domestic
prices did not deviate much from the foreign prices, and, as a consequence, all changes in the
REER reflected the changes in the NEER.

Figure 12: The monthly effective exchange rates of the MDL, November 2005 to November
2010 (November 2005 = 1.00, higher values mean MDL appreciation)

6.2

Nominal exchange rate under a hypothetical peg to EUR

In this section we analyze the effect of exchange rate regime choice on the value of the
MDL under alternative scenarios. Given the structure of Moldova’s foreign trade, a peg to the
EUR seems to be a viable alternative. Since the NBM was successful in maintaining a peg tothe US dollar, we assume it should be able to maintain a fixed peg to the EUR as well.
We also assume that changing the exchange rate regime of the MDL does not influence thevalue of other currencies, the price levels in the trading partners or the structure of Moldova’s
trade. Thus, the nominal exchange rate of the MDL under a fixed peg to the EUR is influenced
solely by the bilateral exchange rates between the EUR and other trading partners’ currencies.
The evolution of the NEER of the MDL under such a regime is shown in Figure 13.
The NEER under a hypothetical peg to the EUR would have behaved differently from theactual movement of the NEER :23

Figure 13: The monthly nominal effective exchange rate of the MDL under a fixed-peg-to-EUR
assumption, November 2005 to November 2010 (November 2005 = 1.00, higher values mean
MDL appreciation)* There was no depreciation period. Instead, the nominal value of the MDL remained
relatively stable until mid-2007.
* The appreciation was less pronounced. Starting from mid-2007, the MDL generally
followed an upward trend against the trading partners’ currencies.
* There was no “peak” (a sharp rise followed by a sharp fall) in the value of the MDL.
* In general, the nominal value of the MDL under a hypothetical peg to the EUR was
higher than was the case in practice.

6.3

Real effective exchange rate under alternative scenarios

Unlike nominal effective exchange rates, the real effective rates are influenced by prices.
This is why in order to assess the influence of the exchange rate regime on the REER, we needto know the behavior of price levels. The large deviations of the REER from unity that took
place under the actual regime do not allow us to accept the purchasing power parity hypothesis(at least in its simplest form) and calls for a set of assumptions about the relationship between
Moldova’s prices and bilateral exchange rates, as well as between domestic and foreign prices.
24

The economy of the Republic of Moldova is much smaller than any of its trading partners, so
we assume that foreign prices are exogenous. As to the factors influencing domestic prices, westart by assuming that they are exogenous. We will therefore use actual data. In what follows
we consider two alternative counterfactuals: (i) a rigid peg to the EUR with an invariant CPI,and (ii) a crawling peg to the EUR with an invariant CPI
6.3.1

A rigid peg to EUR with an invariant CPI

First, we rely on the assumption that all CPIs are invariant to the value of the MDL. The
REER computed in this way for a fixed peg to the EUR is presented in Figure 14. Unlike
under the actual regime, under a hypothetical peg to the EUR there would have been no initial
downward swing in the real effective rate, though we see a stable appreciation tendency fromthe beginning of the period until the beginning of 2008. The first half of 2008 recorded a decline(not observed under the actual regime) followed by a sharp rise in the real value of the MDL
in the second half of the year. After reaching the maximal value of 1.38 at the end of 2008,
the REER exhibited a slightly downward trend until the end of the period, still remaining well
above unity. Thus, the actual regime resulted in a less stable real effective value of the MDL,
while it remained undervalued most of the time.

Figure 14: The monthly real effective exchange rate of the MDL under a fixed-peg-to-EUR
assumption, November 2005 – November 2010 (November 2005 = 1.00, higher values mean
MDL appreciation)25

6.3.2

A crawling peg to EUR with an invariant CPI

We now assume the alternative scenario of a crawling peg to the EUR, setting the rate ofchange in the value of the MDL against the EUR (the crawling pace) to be equal to the actual
rate of appreciation of MDL against the USD, adjusted for the actual depreciation of the USD
against the EUR.
Between November 31, 2005 and November 30, 2010 the Moldovan leu gained 5.67%
while the EUR gained 11.17% against the US dollar. This means that, under a hypotheticalcrawling peg to EUR, the real effective value of MDL would have depreciated by 1.1% peryear (Figure 15), a pattern very similar to the one actually observed under the dollar peg. In
summary, switching to a crawling peg to euro would have produced a similar movement in
the real value of the Moldovan currency to the actual path observed, although it would haveprovided lower volatility.

Figure 15: The monthly real effective exchange rate of the MDL under a crawling-peg-to-EUR
assumption, November 2005 – November 2010 (November 2005 = 1.00, higher values mean
MDL appreciation)

6.4

Assessment of exchange rate volatility

In order to assess the effect of exchange rate regime choice on the volatility of the MDL,
we consider the counterfactual paths of the exchange rate under two alternative counterfactual26

arrangements: (i) a hypothetical peg to a basket of currencies, composed of the US dollar andthe euro with equal weights, and (ii) a hypothetical peg to the euro. For the exchange rate
path under each alternative, we employ a Monte Carlo simulation method to generate multiple
data series in order to examine their properties. To ensure comparability, the alternative pegs
are constructed so as to preserve as many features of the original exchange rate regime as
possible. Specifically, the average rate of depreciation6 and the residual term (β0 and u(t) fromthe Frankel-Wei regression, see equation (1)) are preserved in the simulation.
The data generation procedure in the Monte Carlo simulation takes the following steps.
First, we set the sample size. In our case, this is the same as the sample size of our dataset.
Second, we model the data generation process by specifying a parametric model. In ourcase, this means specifying the weights of basket currencies. In the first alternative regime
the weights are 0.5 for the USD and 0.5 for the EUR, while in the second they are 1.0 for
the EUR and 0 for the USD. Third, we specify a probability distribution for the unobserved
components. In particular, we assume that the error terms follow a normal distribution. Fourth,
we generate values for errors, u(t), by means of a random number generator. Fifth, we computethe dependent variable (the exchange rate of MDL vis-à-vis the chosen numeraire(s)). In
our case it means generating a synthetic time series for (the log first difference of) the MDL
exchange rate. Finally, we estimate the model and obtain the volatility of the synthetic exchange
rate.
Because the error terms are generated randomly, we have different series of the MDL
exchange rate every time we repeat this procedure. By doing this a large number of times(i) we ensure that we obtain a statistically meaningful value for the standard deviation of theseries (our proxy for volatility), and (ii) we can set the upper and lower boundaries of theconfidence interval. In total, the Monte Carlo simulation was repeated 3200 times (400 runs for2 alternative regimes and 4 numeraire currencies). The overall results are reported in Table 8.
Comparing the results under alternative regimes, we observe that pegging to a basket would
have resulted in lower exchange rate volatility for the MDL, regardless of the choice of numeraire.
Pegging to the EUR would have also resulted in lower volatility (except when the JPY was usedas numeraire), as indicated by Figures 42 through 49 in the Appendix.

6 If we estimate equation (1) subject to the constraint that the weights add up to unity, the exact rate ofdepreciation will be slightly different from the figure reported in Tables 3 and 4.

27

Table 8: Standard deviations of ∆ ln(MDL) under a Monte Carlo simulation
RegimeNumeraire
A hypothetical peg toan equally weighted
ActualA hypothetical peg tobasket of USD and

EUR

EUR
CHF

0.0080715

0.0041661

0.0049012

AUD

0.015424

0.0097794

0.010014

SDR

0.0042295

0.0012941

0.0032031

JPY

1.08430.898131.1001

7

Conclusions
Between December 2005 and November 2010, the movement of the Moldovan leu reflected

the varying weights of foreign currencies with varying degrees of flexibility. While this isbroadly consistent with the IMF’s classification of Moldova’s exchange rate regime as “amanaged float with no predetermined path for the exchange rate,” a detailed look at how theexchange rate was managed from day to day revealed a different picture of the country’s defacto regime. In fact, the regimes could be better described as a soft peg to the US dollar, with
its weights ranging between 0.9 and unity. The weight of the Russian ruble was also significant,but only during the first part of the sample period and its weight was never large. In contrast,
and somewhat surprisingly, the weight of the euro was never statistically significant, despite thelarge share of euro-denominated transactions in the Moldovan exchange market, and an evenlarger share of euro-denominated assets. This puzzle is yet to be resolved.

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Appendix

Figure 16: MDL/CHF log returns

Figure 17: USD/CHF log returns

Figure 18: EUR/CHF log returns

31

Figure 19: ROL/CHF log returns

Figure 20: RUR/CHF log returns

Figure 21: UAH/CHF log returns

32

Figure 22: The weight of USD (numeraire:

Figure 23: The weight of EUR (numeraire:

SDR)

SDR)

Figure 24: The weight of ROL (numeraire:

Figure 25: The weight of RUR (numeraire:

SDR)

SDR)

Figure 26: The weight of UAH (numeraire:
Swiss franc)

33

Figure 27: The weight of USD (numeraire:

Figure 28: The weight of EUR (numeraire:

Australian dollar)

Australian dollar)

Figure 29: The weight of ROL (numeraire:

Figure 30: The weight of RUR (numeraire:

Australian dollar)

Australian dollar)

Figure 31: The weight of UAH (numeraire:
Australian dollar)

34

Figure 32: The weight of USD (numeraire:

Figure 33: The weight of EUR (numeraire:

Japanese yen)Japanese yen)

Figure 34: The weight of ROL (numeraire:

Figure 35: The weight of RUR (numeraire:

Japanese yen)Japanese yen)

Figure 36: The weight of UAH (numeraire:
Japanese yen)35

Figure 37:

The weight of USD (allnumeraires)

Figure 38:
numeraires)

Figure 40:
Figure 39: The weight of ROL (all numeraires)

Figure 41:

The weight of EUR (allThe weight of UAH (allnumeraires)

36

numeraires)

The weight of RUR (all

Figures 42 to 45 depict the simulation results for a hypothetical peg to the equally weighted
basket of USD and EUR, where the middle red line represents the most likely path, and theupper and lower lines represent the upper and lower boundaries of the 50% confidence interval.
Likewise, Figures 46 to 49 depict the same thing for a hypothetical peg to the EUR.

Figure 42: Simulation of a “50:50” currency

Figure 43: Simulation of a “50:50” currency

basket (numeraire: SDR)

basket (numeraire: CHF)

Figure 44: Simulation of a “50:50” currency

Figure 45: Simulation of a “50:50” currency

basket (numeraire: AUD)

basket (numeraire: JPY)37

Figure 46: Simulation of a peg to the euro

Figure 47: Simulation of a peg to the euro

(numeraire: SDR)

(numeraire: CHF)

Figure 48: Simulation of a peg to the euro

Figure 49: Simulation of a peg to the euro

(numeraire: AUD)

(numeraire: JPY)

38