Int. Fin. Markets, Inst. and Money 25 (2013) 163– 180

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Journal of International FinancialMarkets, Institutions & Money

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Real interest parity in Central and EasternEuropean countries: Evidence on integrationinto EU and the US markets

Ahmad Zubaidi Baharumshaha,∗, Siew-Voon Soona,Darja Borsic b

a Department of Economics, Faculty of Economics and Management, Universiti Putra Malaysia,43400 UPM Serdang Selangor, Malaysiab Department of Economic Policy, Faculty of Economics and Business, University of Maribor, 14 Razlagova,2000 Maribor, Slovenia

a r t i c l e i n f o

Article history:Received 8 October 2012Accepted 19 February 2013

Available online 27 February 2013

JEL classification:C10F36F41

Keywords:Real interest rate parityStructural breaksHalf-lives

a b s t r a c t

We investigate the validity of real interest parity (RIP) for the13 Central and Eastern European countries, over the period1996–2011. We consider a panel stationarity test that allows formultiple breaks advocated by Carrion-i-Silvestre et al. (2005) andconfirmed the strong version of RIP. We found that the eventsof the last two decades, including the recent global financial cri-sis affected most of the real interest differential series. Based onthe local-persistent model, we found that these countries take lessthan a year to converge to the RIP value. Financial market integra-tion in these countries is invariant with respect to the referencecountry—the US and EU.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Real interest rate parity (RIP) states that if economic agents form rational expectations in theabsence of trade barriers, then there is a tendency for real interest rates to equalize between countries.From a policy perspective, evidence in favor of RIP suggests that the impact of domestic authorities’

∗ Corresponding author. Tel.: +603 89467597; fax: +603 89486188.E-mail addresses: baharumshah@yahoo.com, zubaidi@putra.upm.edu.my (A.Z. Baharumshah).

1042-4431/$ – see front matter © 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.intfin.2013.02.001

164 A.Z. Baharumshah et al. / Int. Fin. Markets, Inst. and Money 25 (2013) 163– 180

stabilization policies might be limited because of monetary interdependence. The violation of realinterest rate equality is a necessary condition for domestic monetary authorities to influence policyvariables that are closely connected to the interest rate (Mark, 1985). This means that deepening finan-cial integration between countries tends to limit the ability of the monetary authorities to intervene indomestic economic activity through interest rate policy. The international parity condition is widelyused to gauge the degree of integration in the capital markets (Phylaktis, 1999). It has been shownthat the confirmation of RIP depends on the extent of uncovered interest (UIP), purchasing powerparity (PPP) and the Fisher equation in domestic and foreign countries. Hence, confirmation of RIPencompasses elements of both real and financial market integration and as such, it can be viewed asa more general indicator of integration or convergence (Holmes, 2002). In the present context, theinternational parity condition may be used as an indicator for countries joining a monetary union. Ashighlighted in Holmes and Wang (2008), real interest rates among member of countries of a union areexpected to equalize in the long-run (see also Arghyrou et al., 2009).

During the last two decades, the Central and Eastern European (CEE) countries have had signifi-cant economic and political transformation due to wide-ranging economic and financial reforms. Thedevelopment of the financial market has defined the transition process. The extent as well as the tim-ing of the reforms varies among these European transition countries. It is worth noting that at thebeginning of the transition process, most of the CEE countries relied on pegging the exchange ratewith a highly stable currency (i.e., the US dollar or the Deutsche mark). As an institutional device,“hard pegs” usually facilitate countries in their transition process from centrally planned to marketeconomies. In the early 1990s, however, a number of the countries gradually softened their peg andmove toward a more flexible exchange rate. The Albania, Czech Republic, Hungary, Poland, Romania,and Slovenia, for instance, have adopted inflation targeting (IT) as a monetary framework, while othercountries in the group are still operating on the “hard peg” (Arratibel et al., 2010). Thus, the exchangerate regime in these countries differs considerably, which may have some bearing on the speed ofadjustment to equilibrium in RIP.

To date, the European Union (EU) consists of 27 member countries. Eight of the 13 countries inour sample (the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia)successfully joined the EU in May 2004 and all have completed the transition process that began in theearly 1990s. Generally, these countries have experienced more stable real interest rate, compared tothe early years of 1990s. Bulgaria and Romania also acceded in 2007, and it is of interest from a policyperspective to determine whether this episode had any impact on the real interest rate. Thus, a success-ful testing strategy should consider among others, tests that accommodate multiple structural breaksin the real interest rates analysis in order to accommodate the specific events experienced by the CEEcountries.

The main goal of this study is to ascertain whether RIP is upheld for a group of CEE countries thatincludes 10 new EU members. The fact that some of these countries are undergoing numerous insti-tutional [EU and European Monetary Unit (EMU) enlargement] and economic reforms might suggesta case of structural breaks in convergence. Unlike past studies, our sampling period extends overthe recent global financial crisis, sovereign debt crisis and extreme volatility in oil prices that endedin 2011: M11. By doing so, we hope to gain a broader understanding about the effects of member-ship in the EU (monetary union) on the international parity condition when capital controls havebeen abolished. In addition, the empirical analysis will also reveal if convergence between the realinterest rates may have been jeopardized by the recent economic events. The literature has over-looked the potential effects of structural breaks for the new EU members and countries preparing tojoin the union; the sole exceptions are Arghyrou et al. (2009) and Su et al. (2012). The former useddummy variables while the latter used Fourier function proposed by Enders and Lee (2012) to cir-cumvent the low power problem associated with the conventional unit root tests.1 Arghyrou et al.(2009), who research is based on two-break tests, find some evidence of support while Su et al. (2012)

1 As shown in Enders and Lee (2012), the test can has more power to detect breaks of unknown form than the standard Baiand Perron (1998) test. While the evidence from the nonlinear unit root test by Su et al. (2012) suggests mean reversion. Weare uncertain if the half-life estimates are consistent with the unit root test.

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find even stronger support for RIP in the CEE countries based on a nonlinear framework. Maican andSweeney (2013), who applied nonlinear models and compared them with piece-wise linear breakmodels, showed that break models appear superior in detecting mean-reversion in the PPP for theCEE countries.

We are aware the rejection of the stationary null may be due to the failure to allow for more thanone break. In light of this, we propose a new testing strategy that accounts for both cross-sectionaldependence and heterogenous multiple structural breaks in panels. Another major contribution ofthis study is that it considers an in between process—the local persistent process—to complement tra-ditional methods commonly used to characterize persistence in real interest rate differentials (RIDs).To this end, the model suggested by Phillips et al. (2001) is used to calculate the half-lives of devia-tions from the parity. This is to account for Sekioua’s (2008) critique that says “. . .it is possible thatunit root tests reject the nonstationarity hypothesis but the process is still persistent in the sense thatdeviations are slow to die out” (p. 78). In that paper, the author showed the unit root tests appliedto annual data over almost a century for the UK, Japan and France are all in favor of RIP. Anotherimportant finding highlighted in the study is that the half-life estimates are incompatible with theunit root results. Specifically, the half-life estimates range from 30 (UK) to 46 (Japan) months, all ofthem above the benchmark of 24 months. Many other studies have provided puzzling outcomes ofmean reversion real interest rates for a number of countries. In our study, we not only provide thepoint estimates, but also construct the confidence intervals (CIs) for the half-lives to measure thedegree of persistence in RID. Critics have claimed that wide CIs as reported in previous studies pro-vide little information regarding the speed of convergence (integration); see Rossi (2005) and Sekioua(2008). They also highlighted a puzzling result—the outcome of the unit root tests is inconsistentwith the high persistence of RID. We apply the local-persistent model, which is robust to high persis-tence, to show that the upper bounds of the CIs in the CEE countries are all consistent with the RIPhypothesis.

The fulfillment of RIP has been tested by using a variety of univariate and cointegration tests,including nonlinear models (see e.g., Moosa and Bhatti, 1996; Holmes, 2002; Sekioua, 2008; Jenkinsand Madzharova, 2008; Baharumshah et al., 2011). It is widely acknowledged that the standard unitroot tests (e.g., Augmented Dickey-Fuller, ADF) suffer from important problems of power and thereremains a high uncertainty about the true degree of integration of the RIDs for the countries beingstudied. For the pre-2004 EU (or European Monetary System, EMS) countries, a number of papersstudies have applied time series and panel data that accommodate breaks to study the RIP (Holmes,2002; Fountas and Wu, 1999; Maveyraud-Tricoire and Rous, 2009; Arghyrou et al., 2009; Camareroet al., 2010; Su et al., 2012). These authors have emphasized the importance of structural breaks ininfluencing the outcome of the RIP among the EMU countries. Based on cointegration method thatallows for breaks to be determined endogenously, Fountas and Wu (1999) have provided evidenceof the RIP, particularly for long-term interest rates. Meanwhile, Maveyraud-Tricoire and Rous (2009)applied a panel test for stationarity proposed by Carrion-i-Silvestre et al. (2005) to demonstrate thatthe financial (RIP) and real integration (PPP) have been modified by the launching of the euro. Theyconclude that the risk premiums have disappeared between the real interest rate for the EU membercountries over the period 1994–2005.

To date, the empirical studies that sought to investigate the international parity condition for theCEE countries is scarce, with the exception of Su et al. (2012), Holmes and Wang (2008), Arghyrouet al. (2009), Cuestas and Harrison (2010), and Kutan and Yigit (2005). The first four articles dealwith real interest rate convergence and the last one is concerned with nominal interest rate con-vergence. The article by Arghyrou et al. (2009) is closely connected to our work as they considerstructural breaks when testing for RIP fulfillment to address the significant monetary and real shocksduring the transition process. They test the RIDs in EU23 (including eight new EU countries) againstthe EMU average over the period 1996–2005. They show, among others that structural shifts (e.g.,launch of the euro) in real interest rate dynamics are responsible for the unit root behavior in theselected countries. Using Lee and Strazicich’s (2003) two-break test for the period 1996: M1-2005:M12, Arghyrou et al. (2009) found 21 of 24 in the RIDs series are stationary with breaks. For the newEU countries, RIP holds for the Czech Republic, Estonia, Latvia, Lithuania, Slovakia and Slovenia, butnot for Hungary and Poland even after allowing for two breaks in the series. They emphasized the

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former new EU members achieved convergence to the EMU average real interest rate by the end of2005. Another interesting aspect of their results is they found a break date closed to the launching ofthe euro in 1999 for the EMU countries. For the new EU members, breaks were widely dispersed linkedto country-specific events. Ignoring breaks in a changing economic environment may lead to spuri-ous unfavorable results on the international Fisher effect (IFE).2 In that paper, the authors argued thatadopting the euro may be more costly for Hungary and Poland, where the real convergence has not beenattained.

Holmes and Wang (2008) examine the same issue using the Seemingly Unrelated AugmentedDickey-Fuller (SURADF) unit root test to exploit the power of a panel unit root test for the period1993–2005. The SURADF procedure allows for cross-sectional dependence.3 The panel membersinclude 10 new EU countries and five peripheral euro area states. They construct three panels forthe US, UK and German rates and their findings appear to support RIP, but not for all the countries.Indeed, they found RIP invariant (variant) against the three numeraire for six (three) cases. Apartfrom showing the importance of addressing the issue of cross-sectional dependence in panel basedmethods, their results show that while there is increasing influence from the Germany; the US stilltends to drive the euro rates.4 To address a major drawback of the unit root test, Holmes and Wang(2008) also reported the speed of reversion of RID to parity. Their calculations based on the traditionautoregressive (AR) method of computing half-lives, confirmed that RIDs are short-lived and meanreverting. Accordingly, the half-lives for the Germany-based model are shorter than the US-based.Finally, Cuestas and Harrison (2010) and Su et al. (2012) apply a nonlinear unit root test for CEEcountries. Cuestas and Harrison (2010) found RIP hold in 10 of the 12 CEE countries using EU and USas the reference country for ex-post rate over the period 1994–2006. As mentioned earlier, this find-ing also is supported by Su et al. (2012). The authors found nine out of 12 cases are stationary againstUS after the account for the unknown nature of the breaks. It should be noted that the outcome ofthese nonlinear findings might due to influential observations (see Koop and Potter, 2001 and Zhouand Kutan, 2011).5 In summary, the evidence from these papers appears to provide support the RIPhypothesis, but not for all of the CEE countries. The results appear to suggest the base country mattersfor testing RIP. Thus, there seems to be room for more research on RIP in CEE countries using morerecent data and advanced methods.

As mentioned earlier, the EU and the US are used as the numeraire country. Intuitively, one canexpect the parity to hold for countries in euro areas since the adoption of a common currency shoulddeepen the integration between the countries of the monetary union. Our choice of the base countryis determined by the growing importance of external trade and foreign direct investment (FDI) withthe CEE countries. The last decade has seen remarkable growth outward direct investments by Europeand the US in these countries.6 The level of trade integration between the CEE countries with the EUmarket is also high, and in the majority cases, even higher than the EU-15.7 We note here that whileCEE countries continued to liberalize their markets, the degree of market liberalization and reformsvaries widely from one country to another.

2 For the emerging market economies, Singh and Banerjee (2006), Ferreira and León-Ledesma (2007) and Holmes et al. (2011)reported stationary RIDs when structural breaks are taken into account.

3 The few panels-based studies that account for cross-sectional dependence are Baharumshah et al. (2011) for East Asianeconomies, Camarero et al. (2009) on selected Organization for Economic Co-operation and Development (OECD) countries,Singh and Banerjee (2006) for the emerging market economies, and Kutan and Yigit (2005) and Holmes and Wang (2008) forthe CEE countries.

4 This latter finding was also supported by Camarero et al. (2010) for 17 OECD countries by applying panel unit root testswith structural breaks.

5 In a recent paper, Zhou and Kutan (2011) tested the PPP hypothesis and argued that the finding based on nonlinear methodscould be the outcome of over-rejecting the unit root hypothesis in presence of outliers (or influential observations).

6 The major contributing factors to the influx of FDI in these countries are: 1) the acceleration of the transition process, and2) the process of integration of CEE countries into the EU and the associated removal of tariffs.

7 A total of 10 agreements were signed between the EU and CEE countries over the period 1991–96, which led to the abolish-ment of EU trade tariffs in manufacturing products by 2002. These agreements led to a convergence at inter- and intra-sectorallevels toward pre-existing EU members (Crespo and Fontoura, 2007).

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Given the rapid structural changes, these countries have experienced during the early transitionphase, and given the several major economic events that have taken place in the last two decades (notto mention the changes in capital mobility and euro crisis), panel stationarity test with endogenousmultiple breaks by Carrion-i-Silvestre et al. (2005) is utilized to test the validity of RIP. In fact, Garciaand Perron (1996), Bai and Perron (1998), and more recently Holmes et al. (2011) and Arghyrou et al.(2009) have argued that a negative conclusion emerged from the literature on the interest equalizationwas more likely due to the methods used in the analysis. According to these authors, the real interestrate is best characterized as a stationary series with infrequent breaks or a shift in the mean (see alsoArghyrou et al. (2009) for new EU members). The problem of cross-sectional dependence could arisefor a variety of reasons: spatial spillover effects, common unobserved shocks, social interactions, ora combination of these factors (Banerjee et al., 2005; Hadri and Rao, 2008). Failure to consider infor-mation across regions (panel members) may lead to less efficient estimation. Given the importance ofthe issue in panel data studies, several tests have been developed to assess whether individuals in thepanel are cross-sectional independent; see Pesaran (2004). For this purpose, we applied the Pesaran(2004) test to formally confirm the presence of cross-sectional dependence.

The rest of the paper is organized as follows. Section 2 describes the theoretical framework andSection 3 outlines methods used in this study. Section 4 presents the empirical results and Section 5concludes.

2. Theoretical framework

Arbitrage forces are formalized by UIP and relative PPP:

it − i∗t = �set , (1)

�set = �st + εt, (2)

�st = �t − �∗t , (3)

where it is nominal interest rate, st is the exchange rate, �t is the inflation rate, and εt is the usualresidual term with mean equal to zero and constant variance. � denotes the difference operator, and(e) and (*) refer to expected and foreign variables, respectively. Substitute Eqs. (1) and (2) into the Eq.(3) will yield

it − i∗t = �t − �∗t + εt. (4)

Eq. (4) can also be written as:

it − �t = i∗t − �∗t + εt = RIDt . (5)

To test for RIP when real interest rates are I(1), the cointegration relationship can be estimated bythe following regression:

rt = ˛0 + ˛1r∗t + εt, (6)

where rt represents the ex post real interest rate in CEE countries, and r∗t is the ex post real interest

rate in the reference country—either the euro or US. Real interest rates (rt and r∗t ) are computed by

using the ex post form of the Fisher equation. RIP holds when the hypotheses ˛0 = 0 and ˛1 = 1. Byimposing the restriction (˛0 = 0 and ˛1 = 1) on the long-run relationship, the deviation from Eq. (6) canbe denoted as εt where:

rt − r∗t = εt = RIDt . (7)

If domestic and foreign real interest rates are unit root, but the residual term εt is stationary, thenthe strong form RIP is upheld in a long-run equilibrium; that is, the RID is mean reverting. However,if εt followed an I(1) process, then there is no evidence of a long-run relationship between the twoseries. The order of integration for RID is usually verified by performing the standard unit root tests,and depending on the outcome of these tests, the degree of capital market integration is inferred.

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Following Ferreira and León-Ledesma (2007), the benchmark test of real interest rate convergenceaccounts for the adjustment cost and information lags denoted as:

RIDt = + �(RIDt−1) + �t. (8)

The error correction model of Eq. (8) can be written as:

�RIDt = + �RIDt−1 +p∑

i=1

ˇi�RIDt−i + �t, (9)

where � =p∑

i=1

�i − 1 and

p∑

i=1

�i = �. Ferreira and León-Ledesma (2007) contended that if � (< 0) is

significant at its conventional level, and the mean is not significantly different from zero ( = 0), RIDfollowed a stationary process and converged to a zero mean. The constant can be associated with therisk premium. This indicates that risk premium has disappeared between the real interest rate withthe reference countries. If � (< 0) is significant at its conventional level and the mean is significantlydifferent from zero ( /= 0), RID is converged to a mean that is different from zero.

3. Estimation strategy

Since the publication of the work of Perron (1989), it has been widely recognized that ignoringstructural changes may lead to the erroneous acceptance of the unit root null hypothesis. Breaksmay also result nonlinear model been erroneously selected instead of linear to best characterized thedata generating process (DGP) (see Koop and Potter, 2001). To account for the infrequent changesin the integration process, we applied panel stationarity test by Carrion-i-Silvestre et al. (2005)that simultaneous allow for endogenously determined breaks as well as cross-sectional depend-ence among panel units. This test is a generalization for the case of multiple structural breaks ofthe panel stationarity test of Hadri (2000), and depends on the location of breaks (�), which isunknown. It can be defined as the vector �i = (�i,1, . . . , �i,mi

)′ = (Tib,1/T, . . . , Ti

b,mi/T)′, i = 1,. . .,N, at

time, t = 1,. . .,T, which indicates the relative positions of the dates of breaks over the entire timeperiod. It also allows the disturbance to be heteroscedastic across the cross-sectional dimension.Under the null hypothesis of a stationary panel with multiple breaks, the test statistic Z(�) fol-lows the standard Gaussian law: Z(�) = [

√N(LM(�) − )/ς] → N(0, 1), where = N−1

∑Ni=1i and

ς2 = N−1∑N

i=1ς2i

, with i and ς2i

are individual means and the variance of univariate Kwiatkowski

et al. (1992, KPSS) tests (�i(�i) = ω−2i

T−2∑T

t=1S2i,t

), ω2i

is the consistent estimate of the long-run

variance of the εi,t ( ω2i

= limT→∞T−1E(S2i,T

)) and S2i,t

=∑t

j=1εi,j , where εi,t is ordinary least squareresiduals from model accounted for structural breaks. Rejection of the null hypothesis implies diver-gence for at least one country, while non-rejection of the null implies stochastic convergence in allcountries. Homogeneity [LM(�) = N−1

∑Ni=1(ω−2T−2

∑Tt=1S2

i,t) with ω2 = N−1

∑Ni=1ω2

i] and hetero-

geneity [LM(�) = N−1∑N

i=1(ω−2i

T−2∑T

t=1S2i,t

)] are imposed for robustness in the estimation of the

long-run variance across the individuals.8

Basher and Carrion-i-Silvestre (2007) contend that the setup in Carrion-i-Silvestre et al. (2005)does not accommodate for common factors to model cross-sectional dependence. They emphasizedthat the results may be biased if a high degree of cross-sectional dependence exists on the panel.The non-rejection of the stationarity null—either the stationarity tests accounting for multiple struc-tural breaks or stationarity tests handle cross-sectional dependence—is not necessarily in favor ofRIP. The degree of persistence in a local-persistent model can be regarded as in between the unitroot and the traditional stationary process. This feature may be present in our model no matter

8 For details of the test, see Hadri (2000) and Carrion-i-Silvestre et al. (2005). The authors are grateful to Professor Carrion-i-Silvestre for kindly making available the Gauss computer codes.

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whether the series is stationary with multiple breaks or stationary once accounted for cross-sectionaldependence.

The above analysis does not reveal speed of adjustment of RIDs to parity. As noted by previousscholars, unit root test tends to be uninformative as to the speed of parity reversion. What policy-makers and investors may be more interested are the degree of persistency (or mean reversion); seeArghyrou et al. (2009) and Arghyrou and Chortareas (2008). For countries that have achieved conver-gence, the steady state-cost of loss of monetary independence should not be high. They went on tosay the “[W]elfare implications of ultimately transitory yet persistent real interest rate differentialsare unknown and potentially significant” (Arghyrou et al., 2009, p. 459). Related to this issue is theargument made by Arghyrou and Chortareas (2008) that the faster the convergence process with euroarea, the faster is the existing current account imbalances corrected.

One measure of persistence (or the degree of mean reversion) that is widely used is the half-life,that is, the number of months it takes for deviations to subside permanently below 50% follow a unitshock in the level of RID. The literature on the speed of convergence toward RIP based on conventionalmethods has produced wide CIs, with infinite upper bound. This may lead to misleading conclusionabout the aped of convergence (Cheung and Lai, 2000). To address the issue, we base our estimationon a method that is more robust to the presence of highly persistent variables, in our case RIDs.Following Kim and Lima (2010), the half-life property of local persistence as ln(0.5b(1))/(−1/nd),

where d = − ln(1 − )/ ln(n), b(1) = 1 −∑k

j=1 ∗j−1 is the correction factor, and n is the number of

observations. The delta method is used to compute the two-sided 95% CIs. Accordingly, the CIs can

be obtained by h0.50 ± 1.96se( )([− ln 0.5/ ][ln( )]−2

), where se( ) =√

2/(n12 + d

2 ) and the is fromthe ADF model.9 If the local persistence parameter (d) lies between 0 and 1, the series is considered tobe the standardized locally persistent process. The series is a special case of local-to-unity process asproposed by Rossi (2005) when d = 1, if d = 0, then the time series process has a short-memory dynamic.Sekioua (2008) argues that the unit root null can seldom be rejected for highly persistent variables andhence the misleading conclusion of market integration can be drawn if the possibility of persistenceof deviations from the parity is ignored.

4. Empirical results and discussions

This paper investigates the RIP in 13 CEE countries over the period 1996: M1 to 2011: M11 (191observations) with the US and euroland as the reference countries. The countries include Albania (AL),Bulgaria (BG), Croatia (CR), the Czech Republic (CZ), Estonia (EE), Hungary (HU), Latvia (LV), Lithuania(LT), Macedonia (MK), Poland (PL), Romania (RO), Slovakia (SK), and Slovenia (SL). The early years oftransition were eliminated because prices and exchange rates were driven by transition reforms. Oursample period also is dictated by the unavailability of the harmonized consumer price index (HCPI,2005 = 100) data series for the pre-1996 period. The money market rate, three-month interest rate, andHCPI are drawn from Eurostat databank.10 The consumer price index (CPI, 2005 = 100) is extracted fromthe IFS, IMF. As mentioned earlier, eight of the 13 countries joined the EU in 2004, and two others—BGand RO—join the EU in 2007. BG is a former Communist country that entered the EU on 1 January 2007.The literature of the CEE (or EU) countries has usually benchmarked each real interest rate against theeuro rate. Are the findings reported in the earlier studies specific to a particular reference country?To provide a more comprehensive investigation on CEE countries’ RIP, we consider each real interestrate with respect to the euro and the US’s rate.

In this article, we employ monthly frequency data and the real interest rate is defined as the nominalinterest rate less the expected inflation rate. Some studies utilized long-run real interest rate to testfor RIP (Singh and Banerjee, 2006; Camarero et al., 2009). Since the long-term domestic rates are

9 We have omitted the details on the method from this paper to conserve space. The readers may refer to the original papersby Lima and Xiao (2007) and Kim and Lima (2010) for exposition of the procedures.

10 Because of missing values for certain data series, nominal interest rates for AL (Treasury bill), CR (money market rate), SL(money market rate), SK (money market rate), and MK (deposit rate) are extracted from the International Financial Statistics(IFS), International Monetary Fund (IMF).

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unavailable for most of the CEE countries for the sampling period, we like Camarero et al. (2010)and Baharumshah et al. (2005) used the short-term real interest rate instead. The inflation rates arecomputed from HCPI for the euro-based and CPI for the US-based variables. It should be mentionedthat there are several ways to generate a measure of expected inflation. In this study, the AR modelis used to approximate inflationary expectations and this is in accordance with the majority of theauthors in testing RIP.11

Real interest rates in the 13 countries along with the US and euroland real interest rates aredepicted in Fig. 1. A glance at the figure reveals several interesting features. First, the real interestrates in the transition economies substantially converged to the euro areas prior to the 2008 crisis(un-shaded area). We observed that the volatility of the short-run real interest rate has declined overthe period, indicating that inflation and risk premia have declined in recent years. In all, they appearto be more volatile than the interest rates of the reference countries. Second, a further look at the datarevealed a significant spread with respect to the euro and US interest rates after 2008, and in somecountries the spread is even larger than the one observed during the late 1990s. The interest ratespread steadily decreased in the mid-1990s, but the trend started to break down in 2008 due to highercurrency-depreciation associated with the recent economic crisis. The graphs, however, do not paintany clear-cut conclusions on whether real interest rates are equalizing across the countries underinvestigation.

We first apply the univariate unit root test suggested by Ng and Perron (2001) for real interestrate convergence. The results (see Appendix A Table A1) fail to support RIP for all the interest ratepairs except for LV (euro-based). For completeness, we also report the results from the Said andDickey (1984, ADF) unit root test. The unit root test is rejected in four RID pairs (BG, CR, LT andRO) for the euro-based and only one pair (BG) for the US-based. Our findings also reveal that themean is not significantly different from zero for the RID pairs, except for three of the euro-basedRIDs (BG, HU and PL) and two when the US is used as reference country (RO and BG). Statistically,there is little empirical evidence to support RIP for either the euro-based series or the US-basedRID series. This is hardly surprising as these tests are known to display serious distortion in powerand size. Next, we applied panel data methods on the convergence of the real interest rates. As inpast literature, the main motivation for testing stationarity in a panel of data is that the powerof the test increases with the number of cross sections in the panel. Under a null hypothesis ofstationarity against a nonstationary alternative, the Hadri Z-test with homoscedasticity [US-based:Statistic = 14.74; euro-based: Statistic = 10.06] and heteroscedastic consistent Z-stat with no breaks[US-based: Statistic = 9.98; euro-based: Statistic = 6.94] indicate that we reject the stationarity null atthe 1% level of significance.

To take the analysis further, we conduct the cross-sectional dependence test proposed by Pesaran(2004)12. The null hypothesis of cross-sectional independence against the alternative of cross-sectionaldependence is tested using the procedure as outlined in Pesaran (2004, PCD). The results indicate ahigh significant degree of cross-sectional dependence at the 5% significance level for the model withindividual-specific intercepts [US-based: PCD = 29.68; euro-based: PCD = 33.50] and the model withincidental linear trends [US-based: PCD = 28.95; euro-based: PCD = 33.37]. Pesaran (2007) maintainedthat due to the large and significant degree of cross-sectional dependence, the conclusion based onthe so-called first generation panel unit root tests that assume cross-sectional independence mightnot be reliable.

To allow for cross-sectional dependence and the presence structural breaks that may arise fromthe changing economic environment as discussed in the introductory note, we proceed to panel sta-tionarity test based on the work of Carrion-i-Silvestre et al. (2005). Specifically, the pure structural

11 We used the AR(1) model to measure the expected inflation rate. Authors like Holmes and Maghrebi (2006), andBaharumshah et al. (2005), among others, have used this method to validate RIP. It should be noted that others have usedex-post real interest rate to evade the empirical and theoretical issues associated with approximating inflation expectation.Reader may refer to the article by Camarero et al. (2010) on the issue.

12 The cross-sectional dependency among cross-sectional units may occur due to the presence of unobserved factors andglobal shocks that are common to all panel members. In addition, each sectional unit may have their local shocks, which affectonly these respective units.

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Fig. 1. US versus Euroland real interest rate.Notes: Albania (AL), Bulgaria (BG), Croatia (CR), Czech Republic (CZ), Estonia (EE), Hungary (HU), Latvia (LV), Lithuania (LT), Macedonia (MK), Poland (PL), Romania (RO), Slovakia (SK), andSlovenia (SL). The sample period starts from 1998: M1, to exclude extreme (influential) observations during the early months of our sample. The shaded (gray) area refers to post-2008:M10.

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change model, which allows the entire coefficient to change, is considered. Given the length of ourdata span, a maximum of five breaks (m = 5) should be enough to capture the all-major shocks inthe integration process. According to the Bai and Perron (1998, 2003, BP hereafter), break pointsidentified by the information criteria tend to be biased and they recommended the sequential pro-cedure instead to carry out the estimation. Based on this algorithm that allows for a maximum offive break points, we find that most of the tests (sup FT(0|1), sup FT(0|2), sup FT(0|3), sup FT(0|4),sup FT(0|5), UD max, and WD max) are significant at the 10% level or better except for the BG RID’seuro-based series. The empirical results are summarized in Tables 1 and 2 for the euro- and theUS-based rates, respectively. As can be seen from the last two rows in the tables, the evidencesseem to be strongly in favor of the mean reverting to the euro and US pairs after accounting forthe break(s).

We now turn to the location of the break dates. When considering the euro-based model, we finda break for four countries (AL, CZ, LT and HU), two breaks for five countries (EE, LV, MK, PL and SK),and one country (CR) experiences three breaks and two other series (RO and SL) experience fourbreaks. BG is the only country for which no break is detected in the RID. Moving on to the US-basedmodel, the BP dynamic algorithm reveals one break point in eight of the series, two breaks in threeof the series, and three breaks for HU and LT. Importantly, the results of the estimated and locationof the structural break(s) in the two panel data sets constitute substantial evidence of the need tocontrol for several large infrequent shocks in the integration process of the CEE countries. In somecases, we find more than two breaks in the RIDs series. Thus, indicating that previous analyses thatdo not account for the presence of breaks may lead to erroneous conclusions. It should be noted thatusing an incorrect specification for the number of breaks can be as problematic as ignoring the breaksaltogether.

The majority of the dates detected coincide with the major economic events in the region, but thereis disparity in the location of break dates from country to country due to country specific events (seealso Arghyrou et al., 2009). Our test results reveal most of the break points are clustered around the1998–99, mid-2000s and late-2000s recession (e.g., US financial crisis and 2009 Euro zone recession).Focusing on euro rates, the first break date is falling closely with the launching of the euro in 1999 inmajority cases, including three of new EU member states (EE, SL and SK). The location of the secondbreak around 2005 may be associated with joining the EU (LV, PL, RO and SL). Finally, the third breakin 2008 in most of the countries may be associated with the 2008–2009 US financial crisis. Similarto the euro-based rates, the US-based rates in some of these countries are affected by the episodesmentioned above; see Table 2. Moving on to the 2008–2009 US financial turmoil, we found a majorshift in the RID toward a higher regime for all US-based RIDs (except SK). This interesting feature ofour break date estimate is found for the euro-based rates—nine euro out of 13 based RID (AL, CR, EE,HU, LV, MK, PL, RO and SL) shift to upper regime during the same period. Finally, the BP 95% CIs forthe late 2000s includes the events surrounding the EU sovereign debt crisis. It is tempting to concludethat the events had no permanent effect on the parity condition in the euro area.13 The fear of theeuro sovereign debt in the early 2010 has yet to influence the real interest rates in the CEE countriesand we leave this for future research.

How sensitive are the unit root tests for the extension of the sampling period of recent years?A recent paper by Zhou and Kutan (2011) has reminded us that the test statistics tend to fluctuatewith the use of slightly different sample period and in the presence of outliers. For this purpose, were-estimate the model using data that ended in 2005: M12. This also allows us to compare the resultsof earlier studies (e.g., Holmes and Wang, 2008 and Arghyrou et al., 2009). The results for US-based(homogeneous statistic = −1.399, p-value = 0.919; heterogeneous statistic = 0.945, p-value = 0.172) andeuro-based (homogenous statistic = −2.6867, p-value = 0.9964; heterogeneous statistic = 0.9682, p-value = 0.1665) suggest that the validity of RIP is upheld. This also means that the new EU members

13 Structural break tests generally used most, but not all, of the data points in determining the break dates. We cannot considerbreaks too close to the beginning or end of the sample due to the trimming factor. It should also be noted that we subject the datato a robust 3-sigma edit rule test. The results (not reported) show that outliers are present in many of the euro- and US-basedrates, especially during the recent euro crisis. Removing these observations does not seem to affect the results qualitatively ina significant way.

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Table 1Panel stationarity test with structural breaks (Euro numeraire).

Test statistics AL BG CR CZ EE HU LV LT MK PL RO SK SL

SupFT(0|1) 11.903b 2.670 5.235 20.196a 4.285 51.316a 1.924 138.575a 30.997a 38.550a 3.736 183.414a 8.137c

SupFT(0|2) 7.364b 2.053 15.731a 18.516a 6.046 28.127a 9.105b 79.237a 28.113a 76.161a 32.067a 99.631a 17.984a

SupFT(0|3) 7.104b 2.443 27.938a 15.019a 5.087 29.067a 7.610a 64.970a 20.688a 61.157a 26.626a 57.625a 17.668a

SupFT(0|4) 6.042b 1.890 23.152a 11.406a 6.293a 22.315a 5.944b 56.677a 16.297a 47.597a 23.752a 51.492a 18.327a

SupFT(0|5) 5.261a 1.501 13.703a 8.152a 5.396a 18.308a 4.685b 44.929a 13.775a 38.848a 18.591a 57.676a 13.753a

UD max 11.903b 2.670 27.938a 20.196a 7.493c 51.316a 9.105b 138.575a 30.997a 76.161a 32.067a 183.414a 18.327a

WD max 13.168a 3.951 45.968a 24.312a 11.841b 51.316a 10.955b 138.575a 36.913a 100.002a 47.159a 183.414a 36.388a

SupFT(2|1) 3.801 1.457 28.240a 6.779 7.131c 4.264 16.477a 3.059 14.999a 56.406a 60.262a 9.613b 19.070a

SupFT(3|2) 5.967 2.856 15.628a 8.490 8.404 11.248b 3.222 21.730a 2.599 5.094 13.852b 2.888 11.826b

SupFT(4|3) 1.564 0.844 7.651 0.522 8.949 2.726 2.176 25.374a 1.598 6.063 12.842b 0.652 11.826b

SupFT(5|4) 1.576 0.235 – – 0.718 1.169 – – – – – 0.370 –Sequential 1 0 0 1 0 1 0 1 2 2 0 2 4

Estimates with breaksRegime 1 0.795 0.901 0.773 0.408 0.716 0.066 −1.935 0.999 0.956 −2.709 −1.813 0.362

(0.181) (0.119) (0.126) (0.149) (0.079) (0.059) (0.151) (0.115) (0.064) (1.900) (0.116) (0.085)BD1 2009:3 2000:5 1999:11 2000:5 2009:1 2006:8 1998:12 2006:9 2006:3 1998:5 2001:2 2000:6CI* 07:10–10:9 00:3–00:9 98:4–00:10 00:2–05:4 08:8–09:6 05:8–07:4 98:7–99:1 06:3–09:5 06:2–06:11 98:5–07:6 00:12–01:4 99:11–02:4Regime 2 2.299 −0.508 0.078 −0.009 2.056 −0.571 0.580 0.325 0.216 0.122 0.519 −0.024

(0.394) (0.107) (0.088) (0.044) (0.168) (0.144) (0.150) (0.130) (0.051) (0.183) (0.148) (0.072)BD2 2003:7 2008:10 2009:1 2009:1 2009:3 2002:1 2004:12 2003:1CI* 01:8–03:12 05:2–08:11 04:6–09:2 08:9–09:2 08:12–09:4 01:9–02:11 04:3–07:5 02:9–03:4Regime 3 0.236 1.547 0.712 2.281 1.661 1.273 −0.025 0.652

(0.153) (0.675) (0.545) (0.228) (0.129) (0.143) (0.091) (0.070)BD3 2008:9 2005:3 2006:2CI* 04:8–09:7 04:6–06:7 05:12–06:5Regime 4 1.123 0.627 −0.101

(0.345) (0.106) (0.067)BD4 2009:1 2008:7CI* 08:9–09:3 07:8–09:3Regime 5 2.268 0.259

(0.171) (0.064)

Panel stationarity test with structural breaks [m = 5]Homogeneous −1.7990 [0.9640]Heterogeneous 1.0440 [0.1480]

Notes: (a), (b) and (c) denote significant at 1, 5 and 10% significance level, respectively. The values in the () and [] refer to corrected standard error and p-value, respectively. CI* denotes 95% confidenceinterval for the break dates (BD). The trimming is taken on the interval [0.1 T, 0.9 T]. Newey-West bandwidth selection using Bartlett kernel. m is the maximum number of breaks allowed in theanalysis.

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Table 2Panel stationarity test with structural breaks (US numeraire).

Test statistics AL BG CR CZ EE HU LV LT MK PL RO SK SL

SupFT(0|1) 54.643a 3.071 30.475a 57.938a 25.542a 120.759a 43.769a 45.192a 143.598a 22.667a 8.028c 131.780a 14.519a

SupFT(0|2) 30.139a 7.466c 17.590a 61.277a 13.978a 87.495a 27.156a 114.152a 73.458a 12.672a 45.484a 123.285a 11.484a

SupFT(0|3) 22.124a 6.564b 12.060a 46.972a 24.448a 62.435a 37.047a 111.849a 56.123a 18.999a 46.900a 81.934a 28.743a

SupFT(0|4) 18.163a 6.845a 12.802a 36.876a 22.809a 47.545a 27.276a 85.567a 46.045a 14.674a 37.350a 76.215a 23.453a

SupFT(0|5) 13.063a 4.799b 9.148a 18.595a 20.625a 35.606a 18.081a 68.178a 35.038a 13.911a 28.380a 51.406a 9.534a

UD max 54.643a 7.466c 30.475a 61.277a 25.542a 120.759a 43.769a 114.152a 143.598a 22.667a 46.900a 131.780a 28.743a

WD max 54.643a 11.770b 30.475a 80.459a 51.626a 120.759a 59.909a 180.872a 143.598a 34.820a 75.842a 161.877a 46.564a

SupFT(2|1) 6.120 12.069b 4.083 8.474c 3.344 8.833b 8.008c 133.045a 3.634 3.720 56.155a 12.968a 6.344SupFT(3|2) 5.181 4.066 1.451 5.937 23.646 11.952b 38.251a 12.535b 20.486a 23.646a 13.083b 1.931 13.864b

SupFT(4|3) 5.743 5.965 12.585b 9.470 4.226 1.329 1.128 3.817 2.275 1.355 3.769 1.116 1.621SupFT(5|4) – – – – 0.489 – – – – – – – –Sequential 1 0 1 1 1 3 1 3 1 1 2 2 1

Estimates with breaksRegime 1 0.895 −10.071 0.363 0.259 0.220 0.438 0.029 −1.994 0.977 0.894 −2.846 −2.029 0.361

(0.206) (5.734) (0.144) (0.121) (0.120) (0.129) (0.119) (0.144) (0.141) (0.156) (1.914) (0.129) (0.115)BD1 2008:11 1998:5 2008:9 2008:11 2008:10 2002:1 2008:10 1998:12 2008:11 2008:11 1998:5 2001:2 2008:10CI* 08:7–09:5 98:4–05:7 07:12–09:1 08:7–09:3 08:1–08:12 01:10–02:7 08:3–09:1 98:11–99:1 08:9–09:2 07:10–09:3 98:4–03:12 01:1–01:2 07:1–09:2Regime 2 3.988 −0.468 2.899 2.412 3.082 1.782 2.553 0.943 4.070 3.468 0.795 1.119 2.150

(0.362) (0.194) (0.434) (0.254) (0.550) (0.129) (0.360) (0.101) (0.214) (0.515) (0.234) (0.155) (0.453)BD2 2008:2 2005:2 2004:10 2008:11 2004:11CI* 05:11–08:11 03:7–05:4 02:5–05:1 08:9–09:5 02:12–05:5Regime 3 1.358 0.608 −0.072 4.137 0.052

(0.485) (0.259) (0.266) (0.275) (0.248)BD3 2008:11 2008:11CI* 08:9–09:2 08:6–09:1Regime 4 3.966 3.359

(0.236) (0.416)

Panel stationarity test with structural breaks [m = 5]Homogeneous −0.8040 [0.7890]Heterogeneous −0.1020 [0.5410]

Notes: (a), (b) and (c) denote significant at 1, 5 and 10% significance level, respectively. The values in the () and [] refer to corrected standard error and p-value, respectively. CI* denotes 95% confidenceinterval for the break dates (BD). The trimming is taken on the interval [0.1 T, 0.9 T]. Newey-West bandwidth selection using Bartlett kernel. m is the maximum number of breaks allowed in theanalysis.

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Table 3The degree of local persistence, half-lives (in months) and confidence interval.

Panel A: euro numeraire Panel B: US numeraire

k d HL(M) se( ) 95% CI k d HL(M) se( ) 95% CI

AL 11 0.2535 2.62 0.0529 [1.58,3.65] 11 0.4206 6.28 0.0342 [2.46,10.10]BG 5 0.1117 1.25 0.0763 [0.89,1.60] 7 0.1210 1.31 0.0745 [0.93,1.69]CR 12 0.2969 3.29 0.0471 [1.84,4.74] 3 0.4129 6.05 0.0347 [2.45,9.65]CZ 11 0.3117 3.56 0.0451 [1.94,5.19] 8 0.5215 10.73 0.0260 [2.26,19.19]EE 5 0.3821 5.04 0.0391 [2.23,7.85] 2 0.4751 8.17 0.0307 [2.37,13.97]HU 12 0.3384 4.10 0.0421 [2.09,6.11] 7 0.5339 11.45 0.0252 [2.11,20.78]LV 11 0.3018 3.38 0.0463 [1.88,4.89] 5 0.4239 6.42 0.0336 [2.50,10.35]LT 11 0.3270 3.86 0.0434 [2.03,5.70] 6 0.4757 8.43 0.0293 [2.53,14.34]MK 11 0.2832 3.06 0.0488 [1.76,4.37] 11 0.4760 8.42 0.0294 [2.51,14.33]PL 12 0.3624 4.65 0.0395 [2.23,7.07] 5 0.5703 13.86 0.0229 [1.43,26.30]RO 11 0.1934 1.91 0.0616 [1.27,2.56] 10 0.2401 2.45 0.0545 [1.52,3.38]SK 12 0.4451 6.56 0.0368 [2.08,11.05] 12 0.4255 5.94 0.0387 [2.08,9.81]SL 12 0.4280 6.56 0.0333 [2.51,10.62] 12 0.4709 8.22 0.0297 [2.54,13.90]

Notes: k is the lag-length. The persistence parameter denoted as d = 1 − = n−d , where 0 < d < 1, and are drawn from theADF model. HL(M) is the half-life for local-persistence model measured in months by ln(0.5b(1))/(−1/nd). The two-sided 95%

confidence intervals (CIs) measured in monthly are constructed according to h0.50 ± 1.96se( )([− ln 0.5/ ][ln( )]−2

) where

se( ) =√

2/(n12 + d

2 ) (see Kim and Lima (2010)).

had achieved convergence by the end of 2005 as echoed by the two authors mentioned earlier. Asshown in Camarero et al. (2010), our results suggest that the outcome of the stationarity test dependson the allowance of both structural breaks and cross dependence when computing the panel teststatistics.

The testing procedures presented so far consist of classifying variables as either integrated of orderzero or one. Kim and Lima (2010) pointed out that such a polar characterization may not provideadequate information about the persistence of the shocks on the variable of interest. According tothem, it is possible that the evidence is in favor of stationary, but the process is still persistent in thesense that deviations are slow to die out. Another potential problem with unit root tests is that theymay not be free from the so-called near unit root bias (i.e., AR parameters are closed, but less thanunity). In such a process, it is likely to bias the results by making the DGP more stationary than itactually is, and making it difficult to distinguish between a stationary and a random walk process.Additionally, the effect of the shocks may be highly persistent over a certain range of time, but itdisappears outside this range (Phillips et al., 2001). Kim and Lima (2010), for example, illustrated thatthe local-persistent processes best describe the exchange rate behavior of the major industrializedcountries. In our view, the locally persistence processes are also better suited to describe the behaviorof RID in the transition economies than the standard unit root and stationary processes. To complementthe results reported earlier, we present the speed of adjustment back toward parity based on the local-persistent model developed by Phillips et al. (2001) to draw some implications of the RIP relationship.Out setup is identical to that of Kim and Lima (2010) who consider the local-persistent model todescribe the movement of the bilateral real exchange rate of the US vs. France, Germany and theUK.

Table 3 reports the degree of local persistence of the stochastic process, the corresponding half-lifeestimates persistence, and the associated 95% CIs for the RIP. The estimated local-persistent parametervaries from 0.11 (BG) to 0.45 (SK) for euro-based model and 0.12 (BG) to 0.57 (PL) for the US-basedmodel. Turning to the euro-based RIDs, the point estimates of half-lives for the persistence modelrange from 1.25 (BG) to 6.56 months (SK and SL). The half-lives for the US-based model, however, arelonger than the euro-based model and range from 1.31 (BG) to 13.86 months (PL). Interestingly, ourresults also reveal that the half-live for BG and RO, two countries that joined the EU in 2007, havethe shortest among the CEE countries. This may reflect much tougher euro entry criteria for the newmember states of the EU.

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It should be noted here that these estimates are much shorter than the ones based on the traditionalmethod (AR).14 Our results, however, are in line with Holmes (2005) who found the deviations to RIPvary from six to seven months for EU members and by Singh and Banerjee (2006) who found shorterhalf-lives for a group of emerging markets (six months). For 10 EU member states, Dreger (2010)found that half-lives 1.1 years after the launching of the euro. A major finding of this paper is that half-lives decrease because of monetary integration, highlighting the idea that half-lives tend to be lowerunder the fixed exchange rate regime. Our contribution fits well with Holmes and Wang (2008) whoreported that RIDs are short-lived and mean reverting. The half-lives for the US-based model (around14.4 months) are longer than the Germany based (around 2.5 months). The specific contribution ofthis paper shows that the 95% CIs of the half-lives for the euro-based model are generally tighter thanthose of the US-based model.

Finally, we find that the 95% CIs were much narrower than those found in past literature, indicatingthat the local-persistent model produces half-lives that are more precise. This is an important findingas Rossi (2005), among others, reminded us that wide CIs provide little information regarding thespeed of convergence. Tighter CIs translate to more information about the integration process. To sumup, we conclude that shocks to RID in the transition economies display mean-reverting behavior andare best viewed as transitory. In all, there is no evidence a single (foreign) market is driving the region’scapital market as reveal by the half-life estimates. The mean reverting and the speed of convergencealso reflect the progress that the new EU members have achieved toward adopting the euro. Thepersistent of shocks to RID is less than a year after accounting for temporary responses to shocks andpolicy measures. From a macroeconomic perspective, this means that the transition economies havethe ability to smooth consumption shocks by altering domestic savings in response to temporary RIDshocks. The fast speed of convergence indicates the monetary authority has limited control over realinterest rate relative to based countries (Singh and Banerjee, 2006).

5. Concluding remarks

As argued in past literature, the evidence of real interest rate equalization and their convergencedepends on whether one adopts linear or nonlinear methods, the span of the data being examined,whether the methods account for breaks in the DGP adjustments and the choice of the referencecountry. In this study, our primary focus is on the last two issues to provide a quantitative assessmentof the extent to which financial globalization has progressed in the transition economies in Europe.In doing so, we applied a testing procedure recently developed by Carrion-i-Silvestre et al. (2005)that simultaneously addresses the problems associated with cross-sectional dependence among panelmembers as well as structural breaks in the data.

We examine the validity RIP for the CEE countries over the period 1996: M1 to 2011: M11. RIPholds for all 13 CEE countries when multiple breaks that characterize these countries are taken intoaccount in the stationarity tests. The empirical evidence is invariant to the choice of the referencecountry—the US and euroland. Thus, our analysis provides convincing evidence that the RIDs behavelike a stationary process in the post-1996 period. The number of breaks, as well as the timing ofthe breaks, varies across the countries. As noted in Arghyrou et al. (2009), because the RID shifts inthe new EU countries are linked to more than one event, it reflects the country specific episodes,the start and/or the accession of EU negotiations, and changing monetary framework (movingfrom the exchange rate to IT). Evidently, the interest rates in the CEE countries are also affected byinternational events, namely the US interest rates. In all the countries but one (SK-US differential),we observed a break that is closely connected to the recent 2007–2012 global financial crisis. Anotherimportant finding is that the dynamics of the RIDs in most of the countries under review can bedescribed by a transient process that reverts back to a fixed (zero) mean after major economic shocks.We also find that the speed of adjustment of the euroland-related differentials to be lower thanthe one prevailing in the US with the exception of SK-US differential. Our results reveal that the

14 The results based on the traditional method of computing the half-lives are available from the authors upon request.

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local-persistent model provides an accurate point estimate and tight CIs than those reported inprevious studies.

Financial reforms have been a major priority in CEE countries since the early 1990s. The newEU members and the countries that are preparing to participate as members must satisfy certaincriteria before a rush entry to the euro (Bahmani-Oskooee and Kutan, 2009). These factors could havecontributed to the close link between domestic and foreign interest rates. Confirmation of RIP wouldsuggest these countries are suitable candidates for participation in a common EU monetary policy. Ourresults tend to support the view that the enlargement process of the euro area is a smooth one. It isworth mentioning that our results, which are based on a larger set of transition countries, suggest thatCEE countries are more integrated than what is reported in the past literature. The ability of domesticmonetary authority to influence internal real interest rates and other variable that depend upon themare severely limited in CEE countries.

Another important aspect of our results is that it appears to support Dreger (2010), who showedthat the exchange rate regime does not affect the parity condition, but it has an effect on the speed ofadjustment toward RIP. Specifically, the empirical results in Dreger (2010) suggest that half-livestend to be relatively low under the fixed exchange rate regime compared to a flexible exchangerate. From a statistical perspective, we show that exploiting cross-sectional information (and dataspan) may reveal the convergence in real interest rate. The inherent failure of the RIP hypothesisreported in past studies is due to the estimation method, as pointed out by Camarero et al. (2010) andothers.

Acknowledgements

We would like to thank the Editor and an anonymous referee for helpful comments and suggestionson a previous version of the paper. The first author acknowledges financial support from the UniversitiPutra Malaysia [Grant no: 06-02-12-2255RU]. The usual disclaimer applies.

Appendix A.

Table A.1.

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Table A.1Unit root tests.

Panel A: Euro numeraire Panel B: US numeraire

c ADF k MZa MZt MSB MPT k c ADF k MZa MZt MSB MPT k

AL � 0.297 −1.584 11 −0.898 −0.548 0.610 20.552 11 0.172 −0.017 11 0.888 0.533 0.600 29.048 14� 0.158 −1.764 11 −2.221 −1.052 0.474 40.971 11 −0.119 −0.889 11 −1.281 −0.684 0.534 55.779 11

BG � −0.768 −3.975a 5 −0.346 −0.393 1.134 63.795 12 −0.479 −3.828a 5 −0.374 −0.429 1.149 64.795 12� −2.867c −4.243a 5 −13.307 −2.579 0.194 6.848 6 −2.823c −3.653b 7 −13.591 −2.604 0.192 6.720 6

CR � 0.085 −2.855c 12 −2.316 −1.048 0.452 10.388 11 0.083 −1.694 3 −5.539 −1.451 0.262 5.033 10� 0.047 −2.876 12 −4.100 −1.412 0.344 22.012 11 −0.083 −2.425 3 −9.106 −2.009 0.221 10.508 3

CZ � 0.023 −1.789 11 −3.443 −1.203 0.349 7.091 12 0.060 −0.743 7 −2.616 −0.736 0.281 7.958 7� 0.090 −1.902 11 −3.459 −1.309 0.378 26.237 11 −0.095 −1.145 8 −5.499 −1.458 0.265 16.083 7

EE � 0.060 −2.181 5 −1.582 −0.739 0.467 12.852 12 0.057 −1.716 5 −0.507 −0.315 0.622 23.192 5� 0.030 −2.206 5 −8.121 −2.004 0.247 11.256 13 −0.032 −2.055 2 −5.234 −1.599 0.305 17.341 5

HU � 0.137c −1.831 12 −2.930 −1.114 0.380 8.147 13 0.072 −0.401 6 0.699 0.285 0.408 16.725 13� 0.077 −2.163 12 −6.981 −1.856 0.266 13.070 12 −0.099 −1.103 7 −4.439 −1.317 0.297 19.198 7

LV � 0.021 −2.399 11 −11.717c −2.399c 0.205c 2.178c 11 0.059 −1.049 5 −2.175 −0.666 0.306 8.717 5� 0.029 −2.301 11 −11.458 −2.384 0.208 8.005 11 −0.143 −1.916 5 −5.831 −1.558 0.267 15.426 5

LT � 0.067 −3.173b 12 −1.315 −0.670 0.510 14.936 12 0.074 −0.924 6 1.059 0.716 0.676 36.083 6� 0.013 −3.100 11 −12.506 −2.469 0.197 7.465 12 −0.126 −1.897 6 −6.741 −1.805 0.268 13.549 6

MK � 0.229 −1.578 11 −2.244 −1.059 0.472 10.915 11 0.156 −1.090 6 −4.279 −1.224 0.286 6.096 6� 0.137 −1.792 11 −2.447 −1.103 0.451 37.120 11 −0.096 −0.947 11 −1.688 −0.777 0.460 42.744 11

PL � 0.157b −2.084 12 −5.176 −1.580 0.305 4.812 12 0.051 −0.040 14 0.081 0.025 0.307 11.698 14� 0.154 −2.073 12 −7.341 −1.915 0.261 12.415 12 −0.058 −1.083 5 −5.381 −1.388 0.258 16.223 12

RO � 0.173 −2.639c 10 −1.105 −0.729 0.660 21.607 13 0.271c −1.999 11 −3.873 −1.200 0.310 6.489 11� −0.364 −3.169c 11 −11.543 −2.402 0.208 7.896 10 −0.600 −2.892 10 −7.732 −1.915 0.248 11.922 11

SK � 0.002 −1.372 12 −1.084 −0.684 0.631 20.449 12 0.036 −1.278 13 −0.118 −−0.046 0.394 14.294 13� −0.007 −0.886 12 −2.182 −1.012 0.464 40.068 12 −0.192 −1.436 12 −3.388 −1.165 0.344 24.411 12

SL � 0.052 −1.765 11 −2.150 −1.036 0.482 11.387 11 0.059 −0.293 12 −1.293 −0.327 0.253 8.943 12� −0.007 −0.886 12 −2.182 −1.012 0.464 40.068 12 −0.093 −1.189 12 −2.739 −0.825 0.301 23.801 11

Notes: (a), (b) and (c) denote statistically significant at 1, 5 and 10% significance level, respectively. The lag length (k) based on Kmax = 14. The spectral GLS-detrended AR is based on ModifiedAkaike information criterion (MAIC). Here, � and ϕ indicate intercept (c), and intercept (c) and trend as exogenous in the testable model, respectively.

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