Asymmetries and credibility in monetary policy of euro area.
Date: 2003-12-19
Institutions: Eurostat
Subjects: Economic and Monetary Union, euro area, financial stability, monetary policy

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Asymmetries and
credibility in monetary
policy of Euro Area

E U R O P E A N
C O M M I S S I O N

 
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4TH EUROSTAT AND DG ECFIN  
COLLOQUIUM ON MODERN TOOLS FOR BUSINESS CYCLE ANALYSIS 

"GROWTH AND CYCLE IN THE EURO-ZONE" 

20 TO 22 OCTOBER 2003 

Luxembourg, European Parliament 
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Asymmetries and credibility in monetary policy 
of Euro Area 

Kostas Mouratidis, National Institute of Economic and Social Research, London 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
asymmetries and credibility in
monetary policy of euro area

kostas mouratidisa
aNational Institute of Economic and Social Research, London

¤

October 1, 2003

Abstract

This paper uses the Markov regime-switching modelling in a bivari-
ate Autoregressive framework to study the credibility of monetary policy
in eight member countries of the European Monetary Union (EMU) and
of the euro area as a whole. The eight countries are: Austria, Belgium,
France, Finland, Italy, the Netherlands, Portugal and Spain; Germany,
and the US were used as benchmark cases. The output-gap and the in-
‡ation variables are incorporated in the determination of the preferences
of individual member countries of the EMU and of the euro area in the
conduct of monetary policy. Our empirical evidence shows that mone-
tary authorities in all countries and the euro area put more weight on the
deviation of in‡ation from its target than on output-gap.

Keywords: Credibility, EMU, Markov-switching.
JEL Classi…cation: C32, E52

¤I would like to thank Philip Aresti, Martin Weale, James Mitchell, Joseph Byrne and
Andrea Cipollini for very useful comments and suggestions. Corresponding author: Kostas
Mouratidis, National Institute of Economic and Social Research, 2 Dean Trench Street, Smith
Square, London SW1P 3HE, United Kingdom. Tel. +44 020 76541924, Fax: +44 020
76541924. E-mail: mkostas@niesr.ac.uk

1

1 Introduction

This paper utilises the Markov regime-switching modelling framework to study
the credibility of monetary policy by taking into account the asymmetric be-
havior of central banks in eight member countries of the European Monetary
System (EMS) throughout its life, i.e. 1979 to 1998. Moreover, we examine the
implications of this analysis for the euro area. To this end we apply a Markov
regime switching model using euro area data. Eight countries are examined
for this purpose: Austria, Belgium, Finland, France, Italy, the Netherlands,
Portugal and Spain. In addition, Germany and the US are used as benchmark
cases. A number of other studies have investigated the issue of credibility during
the EMS period (see for example, Arestis and Mouratidis, 2003; Dahlquist and
Gray, 2000; Olmedo 2002). One of the main conclusions of this body of literature
is that monetary policy may go through di¤erent stages of credibility through
time, in a way that it is perceived to be credible in some circumstances and may
lack credibility on other occasions. It is, thus, appropriate to use the Markov
regime-switching modelling framework to study this phenomenon. Moreover,
some of these studies allow the probability of switching between regimes to be
a function of macroeconomic variables (see Engel and Hakkio, 1996; Sarantis
and Piard, 2000; Gray, 1996; Dahlquist and Gray, 2000). In particular, most
of these studies attempt to explain the currency crises of 1992 and 1993 using
as information variables in the transition probabilities a number of alternatives:
the exchange rate within a band (i.e. a target zone model framework), real
exchange rates, budget de…cits, interest rate di¤erentials, and other variables.
None of these studies include in the transition probability the two main vari-
ables that determine the loss function of monetary authorities, namely domestic
output-gap variability and in‡ation variability (the exceptions are Arestis and
Mouratidis, 2003 ).

The aim of this paper is to evaluate the preferences of the eight EMS cen-
tral banks and of the euro area as a whole based on a state dependent Taylor
type rules.
In particular, we use two MRS-BVAR models where the …rst is
based on the relationship between the interest rate di¤erential (i.e. between
each country’s interest rate and that of Germany’s) and the in‡ation; and the
second is based on the relationship between the same interest rate di¤erential
and the output-gap. In the case of euro area we use the interest rate di¤er-
ential between the euro area interest rate and the interest rate of the US. In
this way we identify di¤erent regimes based on the behaviour of a large num-
ber of parameters, as explained below in the sub-section General Observations.
Asymmetries in preferences might re‡ect not only di¤erent weights in their loss
function but di¤erent economic structure. If this is so then asymmetric shocks
will implies strong pressure on the European Central Bank (ECB) regarding
the proper stance of monetry policy. To this end we evaluate monetary policy
preferences for the euro area.

We begin in the section that follows, with the theoretical and empirical un-
derpinnings of credibility in the EMS. The empirical methodology adopted is
explored in the subsequent section, with the empirical …ndings reported and dis-

2

cussed in the penultimate section. The …nal section summarises and concludes.

2 Theoretical Underpinnings

In recent years, most explanations of positive average in‡ation rates are based
on the time-inconsistency problem. If the public expects a low in‡ation rate
then the central bank faces the temptation to in‡ate. However, the public
correctly anticipates this incentive and adjusts its in‡ationary expectation at a
higher level. This implies a positive average rate of in‡ation that has no e¤ect
on output since it is completely anticipated. There is, thus, an in‡ation bias.
The solution to the in‡ation bias is achieved in the form of lost reputation in a
repeated game version of the basic Barro-Gordon (1983a, 1983b) model. That
is, giving in to the temptation to in‡ate in the current period a¤ects negatively
the central banks’ reputation to achieve low in‡ation in the future. This leads
the public to expect higher in‡ation in the future, which is the punishment of
the central bank by the public, since higher future in‡ation lowers the central
bank’s reputation. However, the evolution of the public’s beliefs over time is
based on observed outcomes of in‡ation, and the central bank can a¤ect these
beliefs by its action. Consequently, a central bank that prefers to achieve some
output expansion at the cost of in‡ation, may …nd it optimal initially to build
an anti-in‡ation reputation. This behaviour of a central bank raises the issue
of the public’s uncertainty in relation to the ’type’ of central bank (see, for
example, Backus and Dri¢ll, 1985; Barro, 1986; Ball, 1995; Muscatelli, 1998).
More concretely, there is uncertainty as to whether the central bank prefers to
stabilise in‡ation variability (i.e. the ’dry’ type of central bank) or to stabilise
output-gap variability (i.e. the ’wet’ type of central bank).

We adopt this assumption in what follows, along with a theoretical argu-
ment stressed by Cukierman (1999), namely that central bank preferences are
asymmetric and depend on the state that the economy is in. If the economy is in
expansion, central bank react more to in‡ationary pressures than in recession.
Alternatively, central bank is less reactive to the deviation of output gap from
its target level when the economy is in recession than in expansion. In partic-
ular, the central bank is hypothesized as having an asymmetric loss function,
which can be minimized subject to the standard constraints of aggregate supply
and demand curves.1 The solution to this minimization will lead to a two-state
Taylor-type rule.

In what follows, we measure the uncertainty regarding the type of central
bank as a deviation of the interest rate policy from a target level, where this

1 A formulation of such a loss function can be presented as follows:

L(¼; y) =

¸1(¼
¸2(¼

½

¡
¡

¼¤) + (1
¼¤) + (1

¸1)(y
¸2)(y

y¤) if st is in expansion
y¤) if st is in recession

¡
¡

¾

¡
¡

where ¸11>¸2 , (1-¸1) < (1-¸2) and st denotes an unobserved state variable which follows

a Markov process.

3

target is the interest rate of a country with low in‡ation reputation (Ball, 1995).
Giavazzi and Pagano (1988) argue that during the 1980s, the EMS countries
designated the Bundesbank as their ’dry central bank’ by pegging their exchange
rate to the German mark, themselves becoming the ’wet central banks’. In our
case the domestic interest rate is the interest rate of each of the nine individual
countries and the target interest rate is the German interest rate. We, thus,
use Germany to represent the dry type of central bank and the nine countries
to represent the wet type of central bank. We concentrate on the variability of
the interest rate di¤erential between the rate of interest of the nine individual
countries and that of Germany, which re‡ects the risk premium for the wet
central bank to deviate from the policy pursued by the dry central bank. The
high volatility regime is associated with recession. This is so because in the
high volatility regime in‡ation expectations and the nominal interest rate are
high, which have negative e¤ects on economic growth. This implies that in
this regime economic growth is low. Alternatively, a low volatility regime, is
associated with high economic growth.

Preferences of the central bank regarding in‡ation and output gap could be
estimated in a time-varying transition probability model, where the probability
to switch between regimes is a function of in‡ation and output-gap. How-
ever, in a time-varying transition probability model, feedback e¤ects between
the transition probabilities and the information variables might exist (Filardo,
1998). Although in such a case one might use the dynamic transition proba-
bility model suggested by Chourdakis and Tzavalis (2001), we deal with this
econometric problem by modelling the dependent variable and the information
variables jointly, utilising a Markov Regime-Switching Bivariate Autoregres-
sive (MRS-BVAR) model. We prefer this solution since in the Chourdakis and
Tzavalis (2001) speci…cation of the dynamic transition probability model, no
information variable can be included in the transition probability other than its
lagged value and the lagged error term. It, thus, makes it impossible to evalu-
ate whether any macroeconomic variables a¤ect the transition probability. The
MRS-BVAR model, by contrast, does not su¤er from this weakness.

3 Econometric Methodology

We utilise MRS-BVAR models where the information variables and the interest
rate di¤erential are jointly estimated. Under such speci…cation we avoid any
econometric problems due to endogeneity between the information variables
and the unobserved state variable. Moreover, we can still evaluate the e¤ects
of in‡ation and output-gap on the interest rate di¤erential. We use two MRS-
BVAR models: the …rst draws on the relationship between the interest rate
di¤erential and in‡ation; and the second draws on the relationship between the
interest rate di¤erential and output-gap. The estimation procedure of the MRS-
VAR model is an extension of the basic VAR model. More precisely, consider a
p

th order vector autoregression for the vector yt:

¡

4

yt = c0 + A1yt

1 + ::::::Apyt

p + ut

¡

¡

(1)

where ut~IID(0;
a M-state MRS(M )-VAR model can be presented as follows:

) and y0; ::::yt

p are …xed. The most general speci…cation of

¡

P

yt = v(st) + A1(st)yt

1 + A2(st)yt

2 + ::: + Ap(st)yt

¡

¡

p +

¡

u(st)

(2)

where st = 1; 2:::m is a (m
1) vector of state variable, u~N ID(0; In),
(st)1=2 is the square root of state dependent variance covariance matrix and
n) matrix of the autoregressive coe¢cients at
2) matrix, and the order of the VAR is one.

Ai(st) is a state dependent (n
P
i
¡
Therefore, we can write a two state bivariate …rst order MRS as follows:

£
th lag2 . In our case A is a (2

£

£

X

yt = c01;i + A11;iyt

1 + §2
¡

i=1(ut;i)

(3)

or

iD
xt
· ¡

iG

t

¡

=

¢

¸

·

®11;i ®21;i
®12;i ®22;i

iD
xt
¸ · ¡

¡

¡
1

iG

1

t
¡

+

¢

¸

·

§11;i §21;i
§12;i §22;i

ut

¸

(4)

where i = 1; 2 indicates the current regime and xt denotes the vector of infor-
mation variables3.

The MRS-VAR are reduced-form models, and, therefore, no structural in-
tepretation can be given to the coe¢cients of output-gap and in‡ation on the
interest rate di¤erential. However, the signi…cance of these variables in the equa-
tions of interest rate di¤erential is based on some theoretical models regarding
the e¤ects of in‡ation expectations on economic growth, and the uncertainty
surrounding the type of central bank behaviour. This leads to certain conclu-
sions about the preferences of the central bank.
In particular, high in‡ation
expectations increase the ex-ante nominal interest rate which a¤ects negatively
economic growth. Under such circumstances, the incentive of monetary author-
ities to in‡ate become stronger. In the framework of Ball (1995) concerning the
uncertainty in terms of the type of central bank, this means that in the high
volatility regime the risk premium for the wet type of central bank to deviate
from the policy pursued by the dry type, is so intense that it prefers to reveal its

2 Krolzig (1997) describes some particular speci…cations of MRS-BVAR models where the
autoregressive parameters, the mean or the intercept are state dependent and the error term
is homoscedastic or heteroscedastic.

3 In the case of regime dependent heteroscedasticity (i.e. §1

= §2), Krolzig (1997) gives
a comprehensive description of the Maximum Likelihood Estimation (MLE) of (11) for m
number of states.

5

6
identity as a wet type.4 Under such circumstances where in‡ation expectations
a¤ect the policy makers’ preferences and, therefore, the risk premium to deviate
from a low in‡ation country, we interpret evidence of the signi…cance of in‡ation
and output-gap in the equation of interest rate di¤erential in the following way.
In the low credibility regime (i.e. high volatility regime), the wet type of
central bank has an incentive to in‡ate. Therefore, in this regime we expect
the central bank under consideration to deviate from the German monetary
policy emphasising more domestic issues such us growth and unemployment
than cerdibility gains. Output-gap, then, is expected to be signi…cant in the
low credibility regime. This is also consistent with the asymmetric behaviour
of the central bank.
In particular, within this framework the central bank
reacts stronger to the deviation of output-gap from its target, in recession than
in expansion. Assuming as we mentioned in the last section, that the high
volatility regime is associated with recession, we expect the e¤ect of output-gap
on interest rate di¤erential to be stronger in the high volatility regime than in the
low volatility regime. Alternatively, if in the high volatility regime output-gap is
not signi…cant, but in‡ation is signi…cant, then monetary authorities put more
weight on in‡ation than on output-gap. When both in‡ation and output-gap are
signi…cant in the high volatility regime, then the credibility of monetary policy
regarding the objective of price stability, depends on whether the coe¢cient of
in‡ation is higher than the coe¢cient of output-gap. If the coe¢cient of in‡ation
is higher than the coe¢cient of output-gap variability, then monetary policy is
considered credible regarding the stabilisation of in‡ation.

In the high credibility state (i.e.

low volatility regime) output-gap is ex-
pected to be insigni…cant. This is so because in a free-shock environment the
wet type of central bank has no incentive to deviate from the low in‡ation policy
pursued by the dry type of central bank. Therefore, monetary authorities will
react to deviation of in‡ation from its target level. This is also in line with the
asymmetric behaviour of the central bank, which prefers to be more reactive
to deviation of in‡ation from its target in expansion than in recession. Using
the assumption that expansion is associated with the low volatility regime, we
expect in‡ation variability to have stronger e¤ects on the interest rate di¤eren-
tial in the low volatility regime, than in the high volatility regime. However, if
output-gap variability is signi…cant in the high credibility regime, while in‡ation
variability is not, then the monetary authority prefers to stabilise the output-
gap variability. Finally, if in‡ation variability and output-gap variability are
signi…cant at the same time in the high credibility state, then we compare the
size of their coe¢cient to reach a conclusion regarding policy preferences.5

4 Ball (1995) shows that the wet type of central bank sets in‡ation equal to zero so long
as supply shocks are absent, and in‡ates at the discretionary level (see text for its meaning)
when supply shocks occur.

5 Signi…cance of output-gap in the high credibility state might be due to an increase in the
correlation between the business cycle in an individual EMS member country and in that of
Germany. The e¤ect of business-cycle correlation on interest rate policy can be implemented
in a multivariate MRS model, if a proxy of business-cycle correlation is available. This would
be an interesting exercise for future research.

6

4 Data and Data Sources

We employ monthly data for interest rates, in‡ation and industrial production
for the nine EMU countries included in our sample. The interest rate data were
taken from line 60b of the International Financial Statistics data base (Datas-
tream). They are monthly averages of day-to-day money rates de…ned by the
IFS as the rate at which short-term borrowing is e¤ected by …nancial institu-
tions. The Consumer Price Index (CPI) is taken from line 64 and industrial
production from line 66 of the same publication. Data are used over the whole
period of the EMS, i.e. from March 1979 to December 1998. Exceptions are the
cases of Luxemburg and Portugal where data are available from January 1986
and January 1983 respectively.

The annualised in‡ation and output-gap are measured by (CPI-CPI12)/CPI12
and (IP-IP12)/IP12 respectively, where CPI is the consumer price index, and IP
stands for industrial production; the subscript denotes the lag order. We use the
twelve order di¤erence mainly because central banks concentrate on the annual
in‡ation rate. The output-gap series have been measured by assuming that the
trend follows a random walk plus drift process. We also experimented with the
following additional assumptions relating to the trend: (i) a smooth stochastic
process uncorrelated with the cyclical component (Hodrick-Prescott detrending
method); (ii) a log-linear trend; and (iii) a linear deterministic trend uncorre-
lated with the cyclical part. The empirical results under di¤erent measures of
output gap were qualitatively similar.

5 Modelling Strategy

We specify a model where the autoregressive coe¢cients and the variance-
covariance matrix are state dependent, while the state vector follows a Markov
process. In this general MRS-BVAR model, an increase in the number of states
leads to a statistical model with an excessively large number of coe¢cients.
This makes the estimation di¢cult and problems of convergence of the MLE
arise.6 Moreover, a number of lags higher than one, does not increase the value
of the likelihood function. We, therefore, retain only one lag. It is for these rea-
sons that we retain eventually a …rst order Bivariate model with two regimes,
MRS(2)-BVAR(1).

We allow the autoregressive coe¢cient to be state dependent because under
such speci…cation we can test for asymmetric e¤ects of in‡ation and output-gap
in the high and low credible states. More concretely, we can account for the fact

6 Moreover, Mouratidis and Spagnolo (2002) show that the interest rate di¤erentials in a
number of EMS countries is characterised by the presence of only two states. In particular,
they examine the number of states for the interest rate di¤erential and, also, they employ
Hansen’s (1992 and 1996) standarised likelihood ratio to test the hypotheses of one state
against two states, and two against three states. Moreover, they consider selection procedures
based on the ARMA representation of Markov regime-switching models. Evidence both from
Hansen tests and from the ARMA representation selection procedures strongly support the
hypothesis of two states.

7

that the impact of in‡ation and output-gap on monetary policy preferences is
regime dependent. We also allow the variance-covariance matrix to be regime
dependent for two reasons. First, we can distinguish between a high volatility
and a low volatility regime. Second, if the variance is regime dependent then
a regime generating process a¤ects the contemporaneous relationship of the er-
ror term ut and, therefore, the orthogonilised impulse response function.
In
this sense, regime switching a¤ects the relationship between the interest rate
di¤erential and in‡ation as well as the output-gap. In general, in our speci…-
cation we incorporate the e¤ects that unobserved state variables have on the
autoregressive dynamics.

6 General Observations

We begin this section by clarifying the coe¢cients in Tables 1-4. In Tables 1
and 2, the estimated equations describe the relationship between the interest
rate di¤erential and the in‡ation. In Tables 3 and 4, the estimated equations
refer to the relationship between the interest rate di¤erential and the output-
gap. The subscripts of each coe¢cient have the following meanings. The …rst
subscript before the dot indicates the equation and the second the regressor
under consideration. The subscript after the dot indicates the regime. The
coe¢cient ci0:1 denotes the constant coe¢cient in the i = 1; 2 equation in the
…rst regime (i.e. high credible regime); the coe¢cient ij:1 is the coe¢cient of
the j = 1; 2 regressor in the i = 1; 2 equation in the …rst regime. Similarly,
ci0:2 is the constant coe¢cient in equation i = 1; 2 in the second regime (i.e.
low credible regime); and ij:2 the coe¢cient of j = 1; 2 regressor in the second
regime. The variance-covariance matrix ij:1 denotes the covariance between
i = 1; 2 and j = 1; 2 in the …rst regime, and the variance-covariance matrix ij:2
is the covariance matrix in the second regime. The transition probability p12
denotes the transition from the high credible regime to the low credible regime.
The transition probability p21 is the transition from the low credible regime to
the high credible regime.

The model is a reduced form and we concentrate only on the signi…cance of
the information variables in the equations of interest rate di¤erential. In all the
cases reported in Tables 1-4 there are three main characteristics that separate
the two regimes. First, the variance of the interest rate di¤erentials in the low
credible regime [i.e. §11:2] is many times greater than that in the high credible
regime [i.e. §11:1]. Exception to this is the case of Austria where the variability
of interest rate di¤erential in the low credibility regime is slightly higher than the
variability of interest rate di¤erential in the high credibility regime [i.e. §11:1 >
§11:2]. Moreover, this is not always true for the equations of in‡ation variability
and output-gap variability. In particular, in the cases of Austria and Finland
the variance of in‡ation in the high credible state is higher than the variance
in the low credible state (i.e. §22:1> §22:2). Moreover, in the cases of Finland,
Italy, the Netherlands and Portugal the variance of output-gap variability in
the high credible state is also higher than the variance in the low credible state.

8

An important implication is that in these cases the regime generating process of
in‡ation variability and output-gap variability might be independent from the
regime generating the process of the interest rate di¤erential. Second, subject to
some exceptions, the coe¢cient of the lagged value of the interest rate di¤erential
is greater in the high credible regime (i.e. ®11:1) than in the low credible regime
(i.e. ®11:2). Also, the transition probability from the low credible regime to the
high credible regime (i.e. p21) is higher than the transition probability from the
high credible regime to the low credible regime (i.e. p12). This implies that high
credible regimes are more persistent than low credible regimes. This is consistent
with the Friedman and Laibson (1989) …ndings that small to moderate shocks
(i.e. regime of low volatility) were more persistent than very large shocks (i.e.
regime of high volatility). Third, in the majority of cases, the covariances §12:i
and §21:i (with i=1, 2) are both equal to zero. This result does not indicate that
there is no relationship between the two variables, since ®12:i
= 0. However, it
implies that the unobserved state variables for each equation in the BVAR-MRS
model are not perfectly correlated.7

7 Detailed discussion of results by country

The second column of Table 1 indicates that in case of Austria in‡ation is signi…-
cant only in the high credible state (i.e. ®12:1):This is consistent with the theory
of state-dependent behavior of central bank.8 This implies Austrian monetary
authorities followed a policy consistent with the objective of price stability. This
is reinforced by the evidence that output-gap variability is insigni…cant in all
states (see the second column of Table 3). In particular, in the case of output-
gap the values of transition probabilities show that there is no regime switching
and that the economy most of the time stays in the high credibility regime.
This might be due to the high credibility of Austrian monetary policy in view
of their success to shadow the German monetary policy.9

In Belgium in‡ation is signi…cant only the high volatility regime (i.e. ®12:1in
Table 1). Moreover, output-gap is not signi…cant in any state. These results
show the determination of Belgian authorities to meet the objective of price
stability stated in Article 3 of the Maastricht Treaty. The third column of
Table 1 show that in the case of France in‡ation is signi…cant in both regimes.
In contrast to our theoretical priors the e¤ects of in‡ation are stronger in the
low credibility state than in the high credibility state (i.e. ®12:1 < ®12:2). This

7 An interesting question that arises is whether the unobserved state variables are intertem-
porally perfectly correlated (Philips, 1991; Ravn and Sola, 1995; and Sola et al., 2002). Under
P2 where P i, with i = 1; 2, is the
such a framework, there would be four states, i.e. P1
transition probability matrix of each variable in the system. It should be sensible to estimate
them but we leave this for a future work.

­

8 Although the coe¢cient of in‡ation in the low credible state is higher than that in thehigh
credibility state it is not signi…cant. Under such curcumastances we can argue that Austrian
monetary authorities had an asymmetric loss function developed by Cukierman (1999).

9 The Austrian interest rate di¤erential in relation to the German interest rate was almost

close to zero during the period under investigation.

9

6
might be due to the evidence that the variance of in‡ation is higher in the high
credibility state than the variance of in‡ation in the low credibility state (i.e.
§22;1 > §22;2). This indicates that the unobserved state variable of in‡ation
is independent of the unobserved state variable if interest rate di¤erential. In
general the results in the case of France show that monetary policy in this
country put strong emphases on the objective of low and stable in‡ation. This
is reinforced by the evidence that output-gap variability is insigni…cant in all
states (see the fourth column of Table 3).

In Finland both in‡ation and output-gap are signi…cant only in the high
credible state. Regarding the output-gap there is inconsistency between the
theory induced by Cukierman (1999) and the evidence that is signi…cant only in
the high credibility state. As in the case of France, this might be due to indepen-
dence of regime generating process of output-gap and interest rate di¤erential.
This is implied by the higher variance of in‡ation in the high credibility state
than in the low credibility state. The last column of Tables 1 and 3 show that
in the case of Italy only in‡ation is signi…cant and in the high credibility state.
Therefore, of monetary policy in Italy regarding the objective of price stability
was high. In the case of the Netherlands in‡ation is signi…cant in both regimes
and output-gap in the low credibility regime (see ®12;1 and ®12;2 in Tables 2
and 4).
It is interesting to note that the coe¢cient of in‡ation is higher in
the low credibility than in the high credibility state and that the coe¢cient of
output-gap in the low credibility state is higher than the corresponding coef-
…cient of in‡ation. This implies that Dutch monetary policy emphasises more
deviation of output from its trend than in‡ation from its target. It is rather
strange result but it might be due to high credibility that Dutch monetary au-
thorities enjoy because of small interest rate di¤erential between Germany and
the Netherlands.

In Portugal only in‡ation is signi…cant in the low credibility state (i.e. co-
e¢cient ®12:2 in the third column of Table 2). The fact that output-gap is not
signi…cant in any state, shows the determination of the central bank to stabilise
in‡ation irrespective of the cost that this policy implies for the output-gap. In
Spain none of in‡ation and output-gap coe¢cients are signi…cant in any regime
(see column 5, Tables 2 and 4 respectively). This is rather strange because
Spain adopted an in‡ation targeting regime on 28 November 1994.10
In par-
ticular, Spain moved gradually from an exchange rate targeting regime to an
in‡ation-targeting regime. Spain experienced a trade-o¤ between retaining an
exchange rate peg and concentrating on the domestic in‡ation target. Spain
did not choose to respond to exchange rate changes that partly re‡ected in-
ternational macroeconomic movements, and to keep both in‡ation expectations
and interest rate stable (see Bernanke et al., 1999). Therefore, the response
of the Spanish interest rate to domestic in‡ation rather than to changes in
the German interest rates, might be the reason that the regimes of in‡ation
has not signi…cant e¤ects on interest rate di¤erential. This transition from an

10 The announcement was made in line with the law of the Autonomy Banco de Espana,

introduced on January 1995, and it was e¤ectively applied in June of the same year.

10

exchange-rate-targeting regime to an in‡ation-targeting regime was accompa-
nied by a substantial increase in transparency and accountability. This helped
the central bank to make clear to the public the consequences of …scal consolida-
tion that took place. Spanish monetary authorities adopted a ‡exible in‡ation
target accompanied by better communication to the public. They, thus, man-
aged to increase ‡exibility and transparency, without experiencing any cost in
terms of credibility, higher in‡ation and even exchange rate instability.

In general results show that monetary policy followed by the eight EMU
countries during the EMS period was consistent with objective of price stability
induced by the ECB. In particular, in the majority of the cases in‡ation had
signi…cant e¤ects on the interest rate di¤erential while this was not the case for
the output-gap. Now we extend this study to examine the implication of these
results for the euro-area. Therefore, we apply MRS-BVAR model to euro-area
data. However, in this case we don’t use Germany as the dry type of central
bank but the US. Therefore, we use the interest rate di¤erential between the
euro-area interest rate and the interest rate of the US.11 The sample of data
start from January 1986 to August 2002. This is so because euro-area data of
short-term interest rate are available only from January 1986. Moreover, we use
producer prices data instead of CPI to compute euro-area in‡ation. This is so
because data of the Harmonised Index of Consumer Prices (HICP) for the euro
area are available after 1990. Producer price index data were taken from New
Cronos, EUROSTAT, based on ESA95 National Accounts.

The last columns of Tables 2 and 4 show that both in‡ation and output-
gap are signi…cant in high credibility state. However the coe¢cient of in‡ation
is much higher than that of output-gap. Therefore, monetary polcy for euro
area was more inclined to correct deviation of in‡ation from its target than
output from its trend.12 This in line with the …ndings from the analysis of
individual countries. Therefore, the issue of di¤erent preferences from national
central banks regarding the proper stance of monetary policy pursued by ECB
might not be raised frequently. However, this not necessarily implies that in
case of asymmetric shocks pressure will not be raised by the individual EMU
member countries on the ECB. It will be very interesting to examine the e¤ects
of business cycles correlation on the interest rate policy both for euro-area data
and for individual EMU countries. The topic of business cycle correlation on
monetary policy is better left for a future research.

8 Conclusions

We have used two di¤erent MRS(2)-BVAR(1) models to analyse the credibility
of monetary policy in a subset of EMU countries and for the euro area.
In
the …rst speci…cation we use a MRS(2)-BVAR(1) model between the interest

11 The euro area interest rate is the three-month money market rate taken from Datastream.
The interest rate of the US is the three-month certi…cate of deposit rate taken from the OECD
data base.

12 Here, we have assume a zero i‡ation as a taget level.

11

rate di¤erential of individual EMU countries and the German interest rate and
in‡ation. In the case of euro are we use the interest rate di¤erential between
the euro area short term interest rate and the interest rate of the US. In the
second model we use a MRS(2)-BVAR(1) model between the same interest rate
di¤erential and the output-gap. We have employed a MRS(2)-BVAR(1) model
to avoid the problem of endogeneity from which time varying transition proba-
bility models might su¤er. We have covered the entire EMS period in the case
of eight EMU countries.
In that case of euro area we cover the period from
January 1986 to August 2002.

The evidence adduced indicates the existence of asymmetric e¤ects, with the
output-gap and in‡ation having di¤erent e¤ects in each state. In general results
from the analysis of the eight EMU countries show that in the majority of cases
in‡ation had signi…cant e¤ects on the interest rate di¤erential while output-
gap found no signi…cant. This implies that monetary authorities of the eight
EMU member countries under consideration put more weight on the deviation
of in‡ation from a target level than on output-gap. Under such circumstances
the objective of price stability induced by ECB will not be in contrast to the
national preferences of any individual country. Moreover, we study whether the
results from individuals countries were in line with the results of an analysis
based on euro area data. Therefore, we estimate monetary policy preferences
for the euro area. Evidence from euro area analysis was consistent with those
of individual countries. In particular, the e¤ects of in‡ation on the interest rate
di¤erential were stronger than the e¤ects of output-gap.

Although results in this study show that monetary policy preferences of in-
dividual member countries might not undermine the objective of price stability.
This study does not take into account di¤erences across countries regarding the
structure of the economy (i.e. aggregate demand and supply curves). Pressure
on ECB might arise when asymmetric shocks occur. Therefore, it will be an
interesting exercise for future research to study the e¤ects of business cycle cor-
relation on interest rate policy both for euro-area data and for individual EMU
countries

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12

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[13] Friedman, B.M. and Laibson, D.L. (1989) ”Economic Implications of Ex-
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13

[17] Hansen, B.E., (1996), Erratum: ”The Likelihood Ratio Test Under Non-
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[19] Muscatelli, A. (1998), ”Optimal In‡ation Contracts and In‡ation Targets
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14

Table 1
Parameters estimates and related statistics for MS(2)-BVAR regime-switching model: In‡ation
Par.
c10;1

Italy

France
0:060
[0:035]

Finland
0:009
[0:069]

Austria
0:020
[0:172]
0:727
[0:000]

0:509
¡
[0:058]
0:080
[0:216]
0:747
[0:000]

1:117
¡
[0:523]
0:009
[0:003]
0:001
[0:439]

0:716
[0:000]
0:0002
[0:579]

¡

0:0001
[0:630]
0:980
[0:000]
0:184
[0:000]
0:015
[0:000]
0:001
[0:000]
0:178
[0:856]
0:0001
[0:008]
0:00007
[0:000]
0:017
[0:016]
0:334
[0:000]

Belgium
0:017
¡
[0:731]
0:624
[0:000]
1:334
¡
[0:581]
0:266
¡
[0:205]
0:596
[0:000]
11:02
¡
[0:010]
0:0009
[0:270]
0:0007
[0:339]
0:937
[0:000]
0:001
[0:091]
0:0006
[0:0009]
0:998
[0:000]
0:022
[0:000]
0:0007
[0:691]
0:0007
[0:000]
1:485
[0:000]
0:0004
[0:901]
0:0001
[0:000]
0:040
[0:071]
0:031
[0:036]

¡

¡

¡

0:959
[0:000]
2:774
¡
[0:007]
0:872
¡
[0:080]
0:325
[0:048]
20:96
¡
[0:006]
0:0004
[0:130]
0:0003
[0:008]
0:944
[0:000]
0:0001
[0:842]
0:0007
[0:684]
1:007
[0:000]
0:054
[0:000]
0:00005
[0:192]
0:00006
[0:001]
3:869
[0:000]
0:002
¡
[0:003]
0:000004
[0:000]
0:063
[0:002]
0:304
[0:0001]

¡

¡

0:966
[0:000]
1:861
[0:000]
0:183
[0:480]
0:769
[0:000]
0:990
[0:848]

0:020
[0:0001]
0:0001
[0:958]
0:786
[0:000]
0:003
¡
[0:084]
0:002
[0:001]

1:012
[0:000]
0:235
[0:000]
0:014
[0:000]
0:0009
[0:000]
0:286
[0:056]
0:014
¡
[0:114]
0:00002
[0:264]
0:0432
[0:000]
0:0435
[0:124]

0:095
¡
[0:000]
0:962
[0:000]
4:133
[0:000]
0:773
[0:000]
0:981
[0:000]
1:120
[0:518]

¡

0:0006
[0:202]
0:0002
[0:975]
0:979
[0:000]
0:006
¡
[0:925]
0:0009
[0:932]
1:176
[0:000]
0:179
[0:000]
0:00007
[0:390]
0:00007
[0:000]
2:750
[0:000]
0:044
¡
[0:035]
0:001
[0:003]
0:016
[0:114]
0:583
[0:000]

®11;1

®12;1

c10;2

®11;2

®12;2

c20;1

®21;1

®22;1

c20;2

®21;2

®22;2

§11;1

§12;1

§22;1

§11;2

§12;2

§22;2

p12

p21

756:45

LogLik
Notes: 1) P-values are reported in squared brackets.
2) In‡ation on the title indicates that the estimation has been implemented for the case where in‡ation is used
as a regressor

1053:04

916:959

985:64

844:17

15

Table 2
Parameters estimates and related statistics for MS(2)-BVAR regime-switching model: In‡ation
Spain
Par.
0:100
c10;1
[0:236]

Netherlands

0:071
¡
[0:186]
0:820
[0:000]
3:758
[0:067]

Portugal
0:091
¡
[0:058]
0:992
[0:000]
0:357
[0:806]

0:501
¡
[0:010]
0:598
[0:000]

9:893
[0:011]
0:002
[0:000]

0:001
¡
[0:013]
0:910
[0:000]
0:0009
¡
[0:360]
0:0001
[0:746]
1:004
[0:000]
0:041
[0:000]
0:0005
[0:896]
0:0005
[0:000]
0:313
[0:000]
0:00001
[0:948]
0:00008
[0:000]
0:040
[0:055]
0:104
[0:014]

¡

¡

0:778
[0:323]
0:629
[0:000]
21:13
[0:002]
0:0006
[0:388]
0:0003
[0:302]
1:048
[0:000]
0:008
[0:856]
0:0002
[0:299]
0:986
[0:000]
0:064
[0:000]
0:0004
[0:703]
0:00001
[0:000]
5:787
[0:000]
0:0003
[0:074]
0:00005
[0:002]
0:083
[0:005]
0:103
[0:029]

¡

¡

¡

Euro-Area
0:190
¡
[0:069]
0:641
[0:000]
8:668
[0:013]
0:113
¡
[0:080]
0:129
[0:000]
3:494
[0:191]

0:008
[0:052]
0:001
¡
[0:200]
0:845
[0:000]
0:005
¡
[0:000]
0:008
[0:000]

0:908
[0:000]
0:351
[0:000]
0:002
[0:000]
0:00003
[0:000]
3:041
[0:056]
0:033
¡
[0:000]
0:0004
[0:264]
0:019
[0:008]
0:025
[0:000]

0:957
[0:000]
0:418
[0:877]
1:497
[0:092]

0:804
[0:000]
2:518
[0:813]
0:0008
[0:154]
0:0001
[0:232]
0:965
[0:000]
0:0006
[0:802]
0:0001
[0:561]
0:969
[0:000]
0:170
[0:000]
0:00007
[0:550]
0:00001
[0:000]
6:181
[0:000]
0:001
¡
[0:617]
0:00001
[0:000]
0:015
[0:103]
0:041
[0:033]

¡

®11;1

®12;1

c10;2

®11;2

®12;2

c20;1

®21;1

®22;1

c20;2

®21;2

®22;2

§11;1

§12;1

§22;1

§11;2

§12;2

§22;2

p12

p21

756:45

LogLik
Notes: 1) P-values are reported in squared brackets.
2) In‡ation on the title indicates that the estimation has been implemented for the case where in‡ation is used
as a regressor.

916:959

829:98

511:64

16

Table 3
Parameters estimates and related statistics for MS(2)-BVAR regime-switching model: IP
Par.
c10;1

Italy

Austria
0:051
[0:270]
0:775
[0:000]
0:029
[0:805]
0:164
[0:997]
0:920
[0:985]
0:300
[0:999]
0:036
[0:000]

0:029
¡
[0:000]
0:001
¡
[0:905]
0:073
[0:999]

0:059
¡
[0:999]
0:414
¡
[0:999]
0:171
[0:000]

0:001
¡
[0:584]
0:002
[0:000]
0:839
[0:992]
0:050
[0:999]
4:161
[0:994]
0:047
[0:077]
0:033
[0:052]

Belgium
0:044
¡
[0:067]
0:651
[0:000]
0:197
[0:722]

France
0:018
[0:498]
0:989
[0:000]
0:501
[0:464]

0:526
¡
[0:003]
0:648
[0:000]

4:275
¡
[0:463]
0:001
[0:001]
0:0008
[0:000]
0:954
[0:000]
0:0006
[0:332]
0:0007
[0:776]
0:987
[0:000]
0:020
[0:000]
0:002
[0:002]
0:0002
[0:000]
1:511
[0:000]

0:0003
¡
[0:514]
0:00002
[0:000]
0:047
[0:077]
0:031
[0:052]

1:116
¡
[0:011]
0:632
[0:000]

2:385
¡
[0:820]
0:007
[0:000]
0:0008
[0:063]
0:743
[0:000]

0:006
¡
[0:126]
0:003
¡
[0:762]
0:761
[0:000]
0:051
[0:000]
0:0001
[0:811]
0:0002
[0:000]
4:479
[0:000]
0:016
¡
[0:005]
0:0003
[0:000]
0:072
[0:001]
0:031
[0:000]

Finland
0:027
¡
[0:061]
0:996
[0:000]
0:637
[0:000]
0:135
[0:145]
0:970
[0:000]
3:321
¡
[0:251]
0:004
[0:136]
0:0003
[0:958]
0:875
[0:000]
0:017
[0:000]
0:001
[0:384]
0:182
[0:097]
0:016
[0:000]
0:0001
[0:706]
0:0008
[0:000]
0:254
[0:000]
0:001
¡
[0:575]
0:0006
[0:000]
0:053
[0:031]
0:089
[0:022]

¡

0:129
¡
[0:036]
1:010
[0:000]
0:479
[0:483]
0:973
[0:078]
0:847
[0:000]
7:132
[0:190]

¡

0:013
[0:014]
0:0001
[0:162]
0:628
[0:000]
0:030
¡
[0:016]
0:004
[0:048]

0:495
[0:001]
0:106
[0:000]
0:001
¡
[0:241]
0:001
[0:000]
1:334
[0:002]
0:002
¡
[0:580]
0:0005
[0:005]
0:052
[0:013]
0:275
[0:013]

®11;1

®12;1

c10;2

®11;2

®12;2

c20;1

®21;1

®22;1

c20;2

®21;2

®22;2

§11;1

§12;1

§22;1

§11;2

§12;2

§22;2

p12

p21

354:49

639:65
LogLik
Notes: 1) P-values are reported in squared brackets.
2) IP on the title indicates that the estimation has been implemented for the case where industrial
production used as a regressor.

845:469

657:68

506:65

17

Table 4
Parameters estimates and related statistics for MS(2)-BVAR regime-switching model: IP
Par.
c10;1

Netherlands

®11;1

®12;1

c10;2

®11;2

®12;2

c20;1

®21;1

®22;1

c20;2

®21;2

®22;2

§11;1

§12;1

§22;1

§11;2

§12;2

§22;2

p12

p21

0:019
¡
[0:348]
0:908
[0:000]
0:168
[0:680]

0:191
¡
[0:007]
0:576
[0:000]

12:84
¡
[0:014]
0:017
¡
[0:000]
0:001
[0:770]

0:317
[0:000]
0:026
¡
[0:000]
0:005
¡
[0:256]
0:140
¡
[0:424]
0:051
[0:000]
0:001
¡
[0:026]
0:001
[0:000]
0:447
[0:000]
0:0003
[0:912]
0:007
[0:000]
0:012
[0:092]
0:090
[0:091]

¡

Portugal
0:077
¡
[0:127]
0:989
[0:000]
0:169
[0:454]
2:205
[0:007]
0:712
[0:000]

3:990
¡
[0:226]
0:005
¡
[0:722]
0:003
[0:244]

0:851
[0:000]
0:024
[0:372]

0:004
¡
[0:116]
0:698
[0:000]
0:061
[0:015]
0:002
[0:419]
0:007
[0:000]
5:892
[0:000]
0:0178
[0:320]
0:006
[0:003]
0:081
[0:022]
0:095
[0:134]

Spain
0:041
[0:749]

0:979
[0:000]
0:590
¡
[0:618]
1:491
[0:000]

0:809
[0:000]
7:704
[0:350]
0:003
¡
[0:001]
0:0004
[0:004]
1:027
[0:000]
0:004
[0:000]

¡

¡

0:0002
[0:017]
0:959
[0:000]
0:117
[0:000]
0:0003
[0:032]
0:00001
[0:000]
5:124
[0:000]
0:001
¡
[0:074]
0:00001
[0:000]
0:063
[0:002]
0:304
[0:0001]

Euro-Area
0:0182
[0:525]
0:977
[0:000]
0:016
[0:000]
0:309
[0:134]
0:872
[0:000]
0:022
[0:435]

0:118
¡
[0:136]
0:078
[0:022]

0:913
[0:000]
1:084
¡
[0:114]
0:422
[0:084]

0:706
[0:000]
0:028
[0:000]
0:009
¡
[0:337]
0:376
[0:000]
0:146
[0:000]
0:026
[0:605]

1:480
[0:000]
0:071
[0:058]
0:235
[0:007]

756:45

LogLik
Notes: 1) P-values are reported in squared brackets.
2) IP on the title indicates that the estimation has been implemented for the case where industrial
production used as a regressor.

112:831

802:96

21:385

¡

18