Panel analysis of the monetary approach to exchange rates: Evidence from ten new EU members and Turkey

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Emerging Markets Review 9 (2008) 57 – 69 www.elsevier.com/locate/emr

Panel analysis of the monetary approach to exchange rates: Evidence from ten new EU members and Turkey Idil Uz 1 , Natalya Ketenci ⁎ Department of Economics, Yeditepe University, Kayisdagi, 34755, Istanbul, Turkey Received 6 February 2007; received in revised form 12 October 2007; accepted 2 December 2007 Available online 8 December 2007

Abstract This paper presents empirical evidence which links the exchange rates to monetary variables in the newly entered ten EU members and Turkey. Using the panel version of various cointegration tests, we find a long-run relationship between nominal exchange rate and monetary variables such as monetary differential, output differential, interest rate differential and price differential. In addition, empirical evidence shows that our error-correction framework of the out-of-sample predictability outperforms random walk after two years. © 2007 Elsevier B.V. All rights reserved. JEL classification: F31; E4 Keywords: Exchange rate; Monetary fundamental; Panel cointegration; Out-of-sample forecasting

1. Introduction Exchange rate determination has been a focus of interest for decades. In particular, explaining exchange rate movements through reference to macroeconomic fundamentals has attracted considerable attention in the literature. Recently, exchange rate economics has seen a number of important developments. These developments have been based on the contributions to both the theory and the empirical understanding of the exchange rate determination through improved econometric techniques and the availability of high quality data. Yet in spite of the great amount of research, there remain unanswered questions in literature such as why the monetary fundamentals can not provide promising results in forecasting much of the variations in exchange rates. ⁎ Corresponding author. Tel.: +90 212 5780581; fax: +90 212 578079. E-mail addresses: [email protected] (I. Uz), [email protected] (N. Ketenci). 1 Tel.: +90 212 5780722; fax: +90 212 5780797. 1566-0141/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ememar.2007.12.001

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The roots of the monetary approach in determining the exchange rate movements as a dominant model go back to the early 1970s, remaining as an important exchange rate paradigm (Frenkel, 1976; Bilson, 1978). In their classical paper, Meese and Rogoff (1983) demonstrated that especially at short horizons, a simple no-change, so-called random walk, of the exchange rate generally outperforms alternative models drawn from economic theory, including purchasing power parity (PPP), uncovered interest rate parity (UIP), and simple versions of the monetary and portfolio balance models of exchange rates. However, there are also some authors that show that out-of-sample forecasting performance improves upon a random walk (MacDonald and Taylor, 1993; Chinn and Meese, 1995; Mark, 1995; MacDonald and Marsh, 1997). The results and robustness of these results have been called into question (Kilian, 1999; Berkowitz and Giorgianni, 2001; Berben and van Dijk, 1998) and recent studies have shown that it is possible, yet difficult, to beat a random walk forecast (Mark and Sul, 2001; Rapach and Wohar, 2002; Faust, Rogers and Wright, 2002). Nevertheless, other studies, especially those based on large industrial countries, have tried to explain at least the majority of the variance in real exchange rates by real shocks and suggesting that exchange rates in those countries serve to a varying degree as a shock observer. Clarida and Gali (1994), for example, found this for Japan, Germany, the United Kingdom and Canada. Alternatively, Enders and Lee (1997) found similar results for Japan and Germany, although for Canada they found that nominal shocks explain about half of the variation in the nominal exchange rate. On the other hand, there are limited studies examining the exchange rate behaviours in the emerging markets, mostly focusing on the exchange rate policies (see Honing, 2005 and Bauer and Herz, 2007) and relationship between exchange rate regime and monetary policy (Kadiyala, 2004 and De Haan et al., 2001). Alternatively, a special issue of the EMR in 2003 is on the debt composition and balance sheet effects and exchange rate fluctuations in Latin America. The newly entered EU members are currently considering when to adopt the euro – except the Slovak Republic, which joined the euro area by the 1 January 2007 – these are also called the first wave of transition countries. Recent studies on the newly entered EU members are based on analyses of the responses of exchange rates to fluctuations in output and these analyses include only a few of the new members. For example, Dibooglu and Kutan (2001) found that in Poland nominal shocks have contributed significantly to movements in nominal and real exchange rates while in Hungary the impact of nominal shocks have been more limited. Suppel (2003), on the other hand, concluded that exchange rates have responded to fluctuations in relative output in the Czech Republic, Poland and the Slovak Republic. Furthermore, according to Borghijs and Kuijs (2004), exchange rates have responded little to shocks that have affected output but responded significantly to fluctuations in monetary variables. Groen (1999) emphasized the high importance of cointegration in establishing a long-run link between nominal exchange rates and monetary fundamentals. In the absence of cointegration Mark (1995), Chinn and Mess (1995) and Groen (1999) found a break down of the long horizon predictability. A relatively short time period of data decreases the power of tests on unit roots or cointegration. A popular response to the problem of low power in the literature is the use of panel data, which can significantly increase the power of unit root and cointegration relations. Therefore, in this paper, we consider panel versions of different cointegration tests in order to find cointegration between the exchange rate and the macroeconomic fundamentals of this model. This paper tries to establish a relationship between nominal exchange rate and monetary variables for the newly entered EU members and Turkey. This kind of study would allow the assessment of the responses of exchange rates to changes in monetary and real variables. We follow Rapach and Wohar (2002, 2004), whose study is heavily based on Mark's monetary

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model. But in our analysis we extended the monetary model by including other monetary variables such as interest rate differentials and price differentials both in the cointegration tests and in the forecasts. This is also similar to the study of Crespo-Cuaresma et al., (2005), but we include more countries and also test the predictability of the exchange rates for out-of-sample data that has not been studied previously. The questions that will be answered are as follows: (1) How well does the monetary model explain fluctuations in nominal exchange rates? (2) Is there any cointegration relationship between exchange rate and monetary variables? (3) Do the monetary model forecasts outperform random walk? The paper is structured as follows. The next section explains the methodology and the monetary model of exchange rate and outlines our testing strategy. Section 3 describes the data we used in the research. Section 4 reports the panel versions of unit root and of the cointegration test results, while Section 5 contains the out-of-sample forecasts. Finally Section 6 give the concluding remarks for this study. 2. Methodology and the model The main features of the monetary approach to exchange rate determination starts with flexible-price formulation (Frenkel, 1976). In contrast to the sticky-price monetary model originally developed by Dornbusch (1976), the flexible model assumes that exchange rate as the relative price of two monies and the relative price in terms of the relative supply of and demand for those monies are the major assumptions used to construct the basic monetary approach. The monetary equilibria in the domestic and foreign country, respectively, are given as follows: mt ¼ pt þ jyt  kit

⁎ ⁎ ⁎ m⁎ t ¼ pt þ jyt  kit

ð1Þ

where mt, pt , yt and it denote the log-levels of the money supply, the price level, the income level and the level of the interest rate, respectively at the time t; K and λ are positive constants; asterisks denote foreign variables and parameters. The assumption of the monetary model based on the assumption that the real interest rate is exogenous in the long run and with the perfect capital mobility assumption it is determined in the world markets. Another cornerstone of the monetary model is absolute purchasing power parity (PPP), which holds that goods-market arbitrage will tend to move the exchange rate until the prices in domestic and foreign countries are equalised. Therefore, the monetary model assumes that PPP holds continuously, so that e t ¼ pt  p⁎ t

ð2Þ

where et is the log-level of the nominal bilateral exchange rate (domestic price of the foreign currency). The domestic money supply determines the domestic price level and hence the exchange rate is determined by relative money supplies. When the monetary equilibria of the foreign country is subtracted from the monetary equilibria of the domestic country in Eq. (1), solved for ( pt − p⁎t ), and the result is inserted into Eq. (2) the nominal exchange rate becomes as follows:


 ⁎ þ k i  i⁎  y et ¼ mt  m⁎ þ j y ð3Þ t t t t t

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which is the fundamental equation of the monetary model. For simplicity, it is assumed that the income elasticities and interest rate semi-elasticities of money demand are the same for domestic and foreign countries. According to the model in Eq. (3), for example, an increase in domestic money supply relative to the foreign money supply induces a depreciation of the domestic currency in terms of the foreign currency or vice versa. The empirical fit and forecasting ability of these models discussed above was carried out by the study of Mease and Rogoff (1983). They regressed the log of exchange rates on various combinations of relative macroeconomic variables included in the exchange rate models and predicted the following equation:



 ⁎ þ b i  i⁎ þ b pe  pe4 þ b tb etþk ¼ b0 þ b1 mt  m⁎ y  y þ b t t 2 3 4 5 t t t t t t þ b6 tb⁎ t þ ut

ð4Þ

where et, mt, yt, it, πt and tbt are the logs at the time t of the exchange rate, domestic (US) money supply, output, interest rates, expected inflation, and the trade balance. Once again, the asterisks denote foreign variables. The main conclusion was that none of the structural exchange rate models was able to forecast out-of-sample better than a naïve no-change forecast. Yet there was “some evidence of predictability at longer horizons but – given the massive failure at short horizons – this did not receive much attention” (Neely and Sarno, 2002, p.55). Other remarkable attempts for the better long-run predictability of exchange rate using monetary models were carried out by Mark (1995) and Chinn and Meese (1995). In particular, Mark (1995) considered an expression relating the change in the exchange rate to the deviation of the exchange rate from a linear combination of relative money and relative output that is called the fundamental value of the exchange rate. Mark (1995) exploited this from Eq. (3) with the assumption that k = 1 and the interest differential is equal to zero. However, in our model we do not accept Mark's assumption that interest differential equals to zero and in addition we include price differential. Therefore, as a result we can write the nominal exchange rate value as follows:



 et ¼ b0 þ b1 mt  m⁎  b2 y t  y ⁎  b3 i t  i ⁎  b4 p t  p ⁎ ð5Þ t t t t Additionally, when the fundamentals term is calculated as follows: h



i ft ¼ mt  m⁎  yt  y⁎  it  i⁎  pt  p⁎ t t t t

ð6Þ

Then, the difference between the current fundamental and the current exchange rate, known as the error correction term zt, where zt = ( ft − et) determines the k period-ahead change in the exchange rate and the model becomes as follows: etþk  et ¼ a0 þ a1 zt þ υtþk;t

ð7Þ

The forecasts in this study are based on the monetary approach used by Mark (1995) in Eq. (7) by using different autoregression moving average (ARMA) models for selected countries that will be discussed in detail in Section 5. 3. Data description The data sample of this research includes ten new members of the European Union (EU): Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, the Slovak Republic,

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Slovenia; and one candidate country, Turkey. The quarterly data used in this research includes the period between 1993Q1 and 2005Q4. The reason for selecting a narrow data set might be a disadvantage. However, the central banks in nearly all the new EU members were established in 1993. The nominal exchange rates are the national currencies per euro obtained from Eurostat. Real gross domestic products (GDP) are used for output variables (1995 as the base year) and are not seasonally adjusted. The data for these selected countries and for the euro area, except Turkey, are obtained from the official site of the EU, Eurostat, where in Turkey they are obtained from the Turkish Statistical Institute (TURKSTAT)2. Monetary aggregates M2 are used for measuring money supply and obtained from the national central banks. For the euro area, the data are obtained from Eurostat. Up to the end of 1998, all monetary variables are in ECU and from 1999 in euro. They are seasonally adjusted by taking the arithmetic average of the previous four quarters3. All variables are measured in log levels. 4. Unit root and cointegration tests 4.1. Panel unit root tests In this study, we tested the cointegration relation for monetary variables in Eq. (5) and estimated the long-run monetary model forecasts in Eq. (7). The cointegration relationship is tested for nonstationary variables. Therefore, first we apply the unit root test in order to find the stationarity of our variables. Standard country-by-country unit root test showed that majority of individual time series are non-stationary I(1)4. However, the addition of a cross-sectional dimension to unit root tests is expected to improve the quality of the unit root test significantly by increasing its power5, as initially shown by Levin and Lin (2002). At the same time, another important contribution of the panel unit root tests is that the resulting test output can be normalized to statistics that have limiting standard normal distributions. As Baltagi and Kao (2000) argues, this is due to the fact that individual data units along the cross-sectional dimension can act as repeated draws from the same distribution. In our paper we used three different tests for the panel unit root. The first one was the Levin, Lin and Chu (LLC) test6, which is based on orthogonalized residuals and the correction by the ratio of the long-run to the short-run variance of each variable. Although the LLC test has become a widely accepted panel unit root test, it has homogeneity restriction, allowing for heterogeneity only in the constant term of the ADF regression. The second applied test was the Im, Pesaran and Shin (IPS) test, which is a heterogeneous panel unit root test based on individual ADF tests and was proposed by Im et al. (2003) as a solution to the homogeneity issue. This test allows for heterogeneity in both constant and slope terms of the ADF regression. Finally, the third test used in our paper was again the heterogenous panel unit root test, the PKPSS. This test was presented by Hadri (2000) as an extension of the test of Kwiatkowski et al. (1992), the KPSS (Kwiatkowski–Phillips–Schmidt–Shin) to a panel with individual and time effects and deterministic trends, which has as its null the stationarity of the series. 2 There were some missing GDP data (especially 1993Q1–1994Q4) in countries other than Slovakia and Estonia. We have used the annual data of percentage change in GDP in order to calculate the missing data. 3 Euro area is the foreign country as specified in equation 1. It involves the 11 countries that participated in the euro area up to 31.12.2000 and 12 countries from 1.1.2001. 4 Augmented Dickey–Fuller (1979) (ADF) test is applied for the country-by-country unit root test. 5 See Baltagi and Kao (2000) and Banerjee (1999) for the detailed surveys on panel unit root tests. 6 Levin et al. (2002).

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In general, our panel unit root estimations confirm that our variables are non-stationary or contain unit root I(1) (Table 1). A panel version of KPSS almost in all cases rejected the hypothesis of the stationarity of variables — exchange rate, money output, interest rate and price differentials except the exchange rate in first difference, where a time dummy was included. The IPS test gave similar results to the previously considered test except for the exchange rate in first difference, money and price differentials. Here the test produced rejection of non-stationarity hypothesis, and in all these cases the time dummy was not included. The LLC test mainly in the case of time dummy inclusion confirmed that our variables contain a unit root; however, in cases where trend dummies were not included only output differential was found to be non-stationary, while for other variables the test rejected the hypothesis of non-stationarity. We may conclude that the inclusion of time dummies is important for all considered variables because the time period that we consider is full of continuous changes in taken economies and specified by monetary crises in several cases. At the same time we have to take into account that LLC (Levin–Lin) test has an important homogeneity restriction which can significantly affect results of the test. Therefore, based on the results of alternative panel unit root tests we have evidence to assume that all our considered variables contain a unit root. Now we need to test the cointegration relationship of non-stationary variables. 4.2. Cointegrating coefficient estimates In testing panel cointegration we examine the stationarity of the residuals from a levels regression. At the same time panel cointegration tests allow us to check cointegrating relationship due to cross sectional data inclusion. The number of observations available in the panel tests several times exceeds the number of observations used in the country-by-country cointegration tests. For estimating a long-run (cointegrating) relationship between the variables in a panel framework, we consider five different methods. These are the panel ordinary least squares (OLS), the multi-country panel version of multivariate maximum likelihood procedure of Johansen (1991) VEC framework (JOH-ML), panel version of autoregressive-distributed lag (ARDL), the fully-modified OLS (FMOLS) estimator by Pedroni (2000), and the dynamic OLS (DOLS) estimator by Mark and Sul (2001) and Kao and Chiang (2000). Each method has different advantages and disadvantages. Kao and Chen (1995) demonstrated in their work that OLS estimator is asymptotically normal but at the same time it is asymptotically biased. They proposed to correct bias in OLS; however, Table 1 Panel unit root tests Test a

LLC IPSa

PKPSSβ

ct c ct c ct c

e

Δe

m − m⁎

y − y⁎

i − i⁎

p − p⁎

− 0.117 − 5.022⁎⁎⁎ 1.575 − 2.195 12.767⁎⁎⁎ 16.893⁎⁎⁎

− 8.498⁎⁎⁎ − 8.832⁎⁎⁎ − 10.107⁎⁎⁎ − 10.202⁎⁎⁎ 2.437 11.291⁎⁎⁎

− 1.170 − 9.299⁎⁎⁎ 2.723 − 6.022⁎⁎⁎ 10.793⁎⁎⁎ 17.144⁎⁎⁎

− 2.158 0.575 − 2.873 − 0.306 11.260⁎⁎⁎ 13.117⁎⁎⁎

− 3.057 − 3.794⁎⁎⁎ − 2.330 − 2.199 9.797⁎⁎⁎ 11.280⁎⁎⁎

13.501⁎⁎⁎ −5.175⁎⁎⁎ −0.289 −9.575⁎⁎⁎ 11.525⁎⁎⁎ 16.423⁎⁎⁎

⁎, ⁎⁎, ⁎⁎⁎ indicate significance at 10%, 5%, and 1% levels, respectively. Null of non-stationarity (unit root). β Null of stationarity. c and ct denote, respectively, that there is a constant and a constant with time dummy in the regression. a

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Table 2 Panel estimates of the cointegration coefficients Coefficient

OLSFE

β1 β2 β3 β4

0.527⁎⁎⁎ (0.034) − 0.848⁎⁎⁎ (0.100) 0.005⁎ (0.001) 0.363⁎⁎⁎ (0.032)

FM-OLS

DOLSFE-T

ARDL

JOH-ML

0.115 (0.040) − 0.044 (0.082) 0.006⁎⁎⁎ (0.001) 0.916⁎⁎⁎ (0.041)

0.105⁎ (0.030) − 0.277 (0.094) 0.001 (0.001) 0.933⁎⁎⁎ (0.031)

0.057 (0.028) − 0.228 (0.073) 0.002⁎⁎⁎ (0.001) 0.824⁎⁎⁎ (0.031)

0.986 (−0.892) − 3.079 (−4.366) 0.356⁎⁎⁎ (−0.041) − 3.087 (−0.985)

⁎, ⁎⁎, ⁎⁎⁎ indicate significance at 10%, 5% and 1% levels, respectively; standard errors for the coefficient estimate are given in parenthesis. For OLS and ARDL estimation procedures, White correction and for DOLS estimation procedure, Newey–West correction is employed to remove any remaining autocorrelation in the residuals. FE and FE-T denote, respectively, that there is a fixed effect and fixed effect together with a time dummy in the regression.

results showed that this correction did not performed well in small samples. The FMOLS estimator takes into account the presence of a constant term and the possible correlation between the error term and the first differences of the regressors. However, it does not produce better results than the OLS test. Kao and Chiang (2000) show that both the OLS and FMOLS estimators exhibit small-sample bias and that the DOLS estimator appears to outperform both estimators. In order to obtain an unbiased estimator of the long-run parameters DOLS estimator uses parametric adjustment to the errors by augmenting the static regression with the leads, lags, and contemporaneous values of the regressors in first differences. Standard cointegration tests such as the Johansen (1991) VEC framework were found to suffer from a ‘time span problem’: the shorter the span of the data, the lower the power of cointegration tests to reject the null of non-cointegration as a result of low adjustment speeds, according to Otero and Smith (2000). One way of circumventing this time span problem was to analyze the monetary exchange rate model within multi-country panel data sets, Groen (2002). Groen found that testing for cointegration based on the monetary exchange rate model in panel data sets has a higher rate of success when the Johansen (1991) framework was used instead of the panel EngleGranger (1987) approach, without taking account of countries' amount. Long-run parameters may be consistently estimated using the traditional autoregressivedistributed lag (ARDL) approach. Moreover, as Pesaran, Shin and Smith (1999) have shown, this approach yields consistent and asymptotically normal estimates of the long-run coefficients irrespective of whether the underlying regressors are stationary or non-stationary. In ARDL, we decided the appropriate autoregressive order by using Akaike Information Criterion (AIC)7. In JOH-ML estimates, one important thing is the selection of lag order. We focus mainly on two selection criteria that are commonly used in the literature: the Sims (1980) sequential modified likelihood ratio (LR) test and the Schwarz criterion (SC). We used a maximum lag order of five, and modified LR. We rely on modified LR for the reason that there is no evidence for heterosckedasticity and no serial autocorrelation8 for these lag orders. From Table 2 we can see that mainly basic features of the monetary model are robust to the estimation model. All variables, except price index in some tests, have the correct sign and highly significant. Estimations for country-by-country analysis showed little support that the estimated cointegrating relationships for these countries are consistent with the simple long-run monetary 7 Also, we followed the traditional method and used AIC for determining the appropriate autoregressive lag order in cointegration tests (to see whether the residuals are stationary). 8 We tested autocorrelation by using the Lagrange Multiplier for lags up to six and tested for White heteroskedasticity.

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model, both in terms of magnitude and sign. Therefore response to the problem of low power, which initially was tried by Levin and Lin (2002) and became popular later, proved its suitability once more. This response implied combining cross-sectional and time-series information in the form of a panel which can significantly increase the power of unit root and cointegration tests. The coefficient on the money supply has correct sign in all estimates, and close to unity in estimates derived by OLSFE and by Johansen tests. The coefficient on output variable has correct sign in all estimates and is highly significant in estimates of OLSFE. While the FM-OLS test notably reduces the magnitude for output coefficient, on the contrary the Johansen test provides significantly higher magnitude for the coefficient, which performs the significant effect of output variable on exchange rate. All five of interest rate estimates had the expected positive sign. However, all estimates, except Johansen, performed a very small effect of interest rate on exchange rate. The price index presented the expected negative sign only in the Johansen estimations. Although all other four tests show opposite sign of this variable, all of them perform the high statistical significance of the price index at the 1% level. In addition to the correct sign estimates of the panel version of the Johansen test, estimated elasticity of price index was found to be much higher than 1. Therefore it can be expected that with increase of price index by 1%, exchange rate can be appreciated by more than 3%. The reason for the controversial results for price index can be explained by the heterogeneity of the panel sample, which includes the new ten members of the EU and Turkey. In all these countries price index has to have a very important effect on the exchange rates; however considering the period which covers before and after entry to the EU, this number can be significantly affected by drastic changes in economic policies. Nevertheless, results prove a high significance level of price index on the exchange rates. Tests for cointegrating coefficients presented better results in the panel version estimates than in country-by-country estimates. Country-by-country estimates had high level of significance; however, in many cases signs did not corresponding to theoretical values. Panel version provided high robustness of the monetary model to the estimation method. Almost all variables demonstrated the correct sign with high levels of significance. 4.3. Cointegration test result In this section we report the cointegration test results for the selected countries in order to find whether the residuals of the tested variables are stationary. Table 3 presents cointegration test results by using the panel ordinary least squares (OLS), the multi-country panel version of the Johansen (1991) VEC framework (JOH), the panel version of ARDL, the FMOLS estimator, and the DOLS estimator, as discussed earlier. We used alternative tests to find cointegrating relationships in order to control their results and to maximize the power Table 3 Panel cointegration test results OLSFE

(a)

− 8.81⁎⁎⁎

b

DOLSFE−T

ARDLb

JOHc

FM-OLS

−4.81⁎⁎

− 7.011⁎⁎⁎

133.116⁎⁎⁎

−8.929⁎⁎⁎

⁎, ⁎⁎, ⁎⁎⁎ indicate significance at 10, 5, and 1% levels, respectively. a OLS test of H0: no cointegration. b DOLS and ARDL tests of H0: no cointegration. c Johansen one-sided upper-tail test of H0: no cointegration; 10, 5, and 1% critical values equal − 27.07, −29.8, and − 35.46, respectively.

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of the found evidence, as this is also a necessary condition for the monetary model. We determined the power of our panel cointegration test to reject hypothesis of no cointegration. Based on the results represented in Table 3, we found strong evidence of the cointegration. All reported statistics show rejection of the null hypothesis of no cointegration in favour of cointegration at the 1% level except for the DOLSFE-T test where we rejected the null hypothesis at a 5% level. Although only selected estimates were presented in this paper9, overall almost all tests rejected the null hypothesis of no cointegration at the high significance levels. Therefore we may conclude that there is strong evidence of cointegration among exchange rate, money demand, output, interest rate and price differentials. As a result it can be concluded that there is substantial support for a simple form of the long-run monetary model in these countries. 5. Out-of-sample forecasting The difficulty in predicting the exchange rates has been a long-standing problem in international economics. The seminal studies by Meese and Rogoff (1983) have shown that several monetary models fail to explain exchange rate movements better than a random walk. However, there also have been studies like those by Mac Donald and Taylor (1993), Chinn and Meese (1995), Mark (1995), Mark and Choi (1997), Wu (1999), Groen (2000), and Mark and Sul (2001) that have found that the exchange rates are predictable at longer horizons. Others, like Berkowitz and Giorianni (2001), Kilian (1999), Rapach and Wohar (2002) and Faust et al., (2003), have showed that exchange rates are not predictable with monetary models such as monetary differential and output differential. Especially Berkowitz and Giorianni (2001) and Kilian (1999) have focused on the disadvantage of bootstrapping procedures employed by Mark (1995). In this section, we provide evidence to show that exchange rates are predictable over long horizons in its out-of-sample forecasts. We adopted the convention in the empirical exchange rate modelling literature of implementing “rolling regression” rather than the bootstrapping procedure. The estimates of the cointegration tests discussed in the previous sections were applied to a given data sample. The same data were used to produce out-of-sample forecasts, then the sample was moved up, or “rolled” forward one observation before the procedure was repeated. We generated out-of-sample forecasts at three different horizons including short-horizon (k = 1), and longhorizon (k = 16). We have demonstrated in the previous section that there exists a long-run relation between nominal exchange rates, monetary differentials, output differentials, interest rate differentials and price differentials. According to Granger’s (1974) representation theorem, if a cointegrating relationship exists among a set of I(1) series, then a dynamic error correction representation of the data also exists. We used Eq. (7) as our structural model and compared the panel regression forecasts against those implied by the random walk model10. We explicitly tested the null hypothesis of no difference in the accuracy of the two competing forecast (i.e., structural model vs. random walk with drift). Therefore, we used different statistics for prediction evaluation for competing models, which are presented in Table 4. First, we measured relative forecast accuracy with Theil's U-statistic — the ratio of the rootmean-square prediction error (RMSPE) from two competing models. A value smaller (bigger) 9

Specifications (time dummy and fixed effects) in reported econometric tests were chosen on the basis of better performance. 10 We follow Kilian (1997) and Mark and Sul (2001), who argue that it is appropriate to employ the random walk with drift.

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Table 4 Monetary model forecasts Country

U

r

GN

DM

− 1.001 − 0.070 − 0.771 −3.460 − 0.062 −4.920 −2.603 − 1.400 −2.238 −5.468 −5.984 −2.543 −2.238

− 0.051 − 0.002 − 0.028 − 0.076 − 0.009 − 0.234 − 0.076 − 0.036 − 0.057 − 0.102 − 0.128 − 0.073 − 0.057

A. One-quarter ahead forecasts CYP CZK EST HUN LAT LIT MAL POL SLK SLN TUR Mean Median

1.008 1.000 0.989 0.985 0.979 1.026 1.010 1.010 1.014 1.026 0.951 1.000 1.008

−0.028 −0.002 −0.021 −0.097 −0.002 −0.150 −0.073 −0.039 −0.063 −0.162 −0.161 −0.073 −0.063

Country

U

r

GN

DM

−4.810 −8.332 − 0.882 −10.568 −3.012 −11.557 − 0.463 −9.638 −4.996 −10.552 −14.715 −7.230 −8.332

− 0.139 − 0.276 − 0.040 − 0.281 − 0.135 − 0.458 − 0.040 − 0.269 − 0.137 − 0.458 − 0.558 − 0.254 − 0.269

B. Sixteen-quarter ahead forecasts CYP CZK EST HUN LAT LIT MAL POL SLK SLN TUR MEAN MEDIAN

0.943 0.909 0.995 0.676 0.985 0.707 0.993 0.750 0.095 0.816 0.778 0.786 0.816

−0.135 −0.223 −0.024 −0.286 −0.083 −0.335 −0.013 −0.263 −0.140 −0.303 −0.374 −0.198 −0.223

Note: t-tests are given for GN and DM. Bold values represent statistically significance at 5% level (the 5% critical value is − 1.65 since our test is one-tail test).

than one indicates a better performance of the structural model (random walk). The second one was the Granger–Newbold test (GN) based on the r, the correlation coefficient between data sets of these two models11. If r is statistically different from zero, structural model (random walk) has a larger mean square prediction error (MSPE) if r is positive (negative). Lastly, we used the Diebold–Mariano (DM) statistic (Diebold and Mariano, 1995), which is defined as the ratio between the sample mean loss differential and an estimate of its standard error; this ratio is asymptotically distributed as a standard normal. We also performed joint tests of the hypothesis of equal forecast accuracy by using joint test statistics formed alternatively by taking the mean value and the median value of each test. Before explaining the forecast results it is important draw attention to a specification of these selected countries where they applied different exchange rate regimes throughout the selected period. This might cause problem when the relationship between fundamentals and exchange rates is examined (see discussions in Neely and Sarno, 2002). However, it is common in the literature to examine exchange rate movements with different regimes (see Mills and Pentecost, 2001; CrespoCuaresma et. al., 2005; and Borghijs and Kuijs, 2004). We employed Mark’s monetary model as our structural model, the results of which are not reported in this paper. We found moderate support for some countries. Therefore, we extended the Mark’s structural model by including interest rate differential and price differential. In the first column, U statistics clearly show that performance of the structural model is improved at sixteenquarter ahead forecast. At one-quarter ahead forecasts, only in Estonia, Hungary, Latvia and Turkey does the structural model outperform random walk. However, in all countries the structural model outperforms random walk at eighteen-quarter ahead forecasts. Another test, the GN test, gives results similar to those of U statistics. GN tests show whether forecasting the performance of the structural model is statistically different from that of the random walk. Test results were statistically improved at longer period horizons. For Czech Republic, the pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi GN test is calculated by r= ð1  r2 Þ=ð H  1Þ where r is the correlation coefficient and (H − 1) is the degree of freedom. 11

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performance of the structural model was not statistically different from that of the random walk at one-ahead forecast. For Estonia, the performance of the structural model was not statistically different from that of the random walk at all horizons. It is no coincidence that these two currencies are strictly controlled. Especially Estonia has a currency board system that makes provision for the pegging of the kroon to the Deutschmark and then the euro. In 1999, the exchange rate never came under serious pressure due to a strong disruption in the financial markets. Thus, Estonia maintained its currency board system without interference from the exchange rate markets. In the second column, r represents the correlation coefficient between two data sets and for countries where it is statistically significant random walk have larger MSPE. Countries such as Cyprus, Hungary, Poland, Slovakia, Slovenia and Turkey have adopted floating regimes, mostly since 2000, where these countries give similar responses in the determination of the exchange rate fluctuations. Only Slovenia, among these countries, entered the eurozone in 2007. However, DM statistics are never statistically significant at the conventional significance level at all horizons. Berben and van Dijk (1998) argue that this is because of the difficulty of accurately estimating long-run variances often results in misleading inference. Our results give promising evidence for exchange rate predictability with the extended structural model in the long run, specifically after two years12. This is consistent with the recent literature; for example, Abhyankar et al. (2005) found similar results for predictability at horizons of three and four years13. We have improved the structural model by including an interest rate differential and a price differential. There is sufficient evidence that establishes a long-term relation between the exchange rates and interest rate differentials (Wu, 1999; Alexius, 2001) and the price differentials (see Groen, 2000; Neely and Sarno, 2002; Taylor and Taylor, 2004). 6. Conclusion This study tested the monetary model using sample of ten new members of the EU and a candidate country Turkey over 1993Q1 and 2005Q4 period. In order to solve the problem of short-term data we exploited the available cross-sectional information in a panel data set. In our research we found that nominal exchange rates are cointegrated with considered monetary fundamentals, and significant support for the monetary model using panel procedures. The enrichment of the Mark's monetary model with the inclusion of interest rate differential and price differential allows a closer look at the relationship between exchange rates and extended monetary variables in the emerging economies. We found strong evidence for cointegration between exchange rates and monetary model using panel data, which is consistent with the existing literature (see, for example, Mark and Sul, 2001; Groen, 2000; Rapach and Wohar, 2002; Crespo-Cuaresma et al., 2005). Therefore, we show that support for the long-run monetary model of exchange rate determination is not elusive as it once appeared, even for the emerging economies. Efforts for the predictability of exchange rates with monetary variables, despite its density, do not provide convincing results and therefore it is still considered as elusive. However, there have been convincing studies for the predictability of exchange rates with interest differentials and promising results for the predictability of exchange rates with price differentials. Our out-of-sample forecasts of exchange rates with extended monetary variables, including interest rate differential and price

12 We also employed in-sample forecasts for the panel and found similar results that error correction model of the monetary variables outperform random walk not in the short run but at longer horizons. 13 Even though they employed an identical work of Mark (1995).

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differential, along the other monetary variables are likely to provide evidence for exchange rate predictability at longer horizons. Our results suggest directions for future research. Obviously, we need a larger time span of currently available data for a critical analysis of exchange rate determination and the relationship with monetary variables. Additionally, the data might be extended by including other emerging countries. Furthermore, the analysis might compare monetary approach to other models in determination of the exchange rates such as portfolio balance model. Alternatively, there is a tendency to study exchange rates that might be nonlinearly mean reverting to fundamentals and therefore, monetary model predictions might be more informative, especially in the long-run. Therefore, it would be interesting to study the behaviours of exchange rates in emerging markets with non linear models and with the regime switching approach. References Abhyankar, A., Sarno, L., Valente, G., 2005. Exchange rates and fundamentals: evidence on the economic value of predictability. Journal of International Economics 66 (2), 325–348. Alexius, A., 2001. Uncovered interest parity revisited. Review of International Economics 9 (3), 505–517. Baltagi, B.H., Chihwa, Kao, 2000. Nonstationary panels. Cointegration in Panels and Dynamic Panels: A Survey, Advances in Econometrics 15, 7–51. Banerjee, A., 1999. Panel data unit roots and cointegraiton: an overview. Oxford Bulletin of Economics and Statistics 61, 607–629 (Special Issue: November). Bauer, C., Herz, B., 2007. Credibility of CIS exchange rate policies — a technical traders's view. Emerging Markets Review 8 (1), 50–66. Berben, R.B., van Dijk, D.J., 1998. Does the absence of cointegration explain the typical findings in ling horizon regressions? Econometrics Institute, Erasmus University Rotterdam, Paper, 9814. Berkowitz, J., Giorgianni, L., 2001. Long horizon exchange rate predictability. Review of Economics and Statistics 83 (1), 81–91. Bilson, J.F.O., 1978. Rational expectations and the exchange rate. In: Frenkel, J.A., Henry, G.J. (Eds.), The economics of the exchange rates: Selected studies. Addison Wesley Press, MA. Reading. Borghijs, A., Kuijs, L., 2004. Exchange rates in central Europe: A blessing or a curse? IMF Working Paper WP/04/2. Chinn, M.D., Meese, R.A., 1995. Banking on currency forecast: how predictable is change in money. Journal of International Economics 38, 161–178. Clarida, R., Gali, J., 1994. Sources of real exchange rate fluctuations: how important are nominal shocks? CarneigeRochester Conference Series on Public Policy 41, 1–56. Crespo-Cuaresma, J., Fidrmuc, J., MacDonald, R., 2005. The monetary approach to exchange rates in the CEECs, the economics of transition. The European Bank for Reconstruction and Development 13 (2), 395–416 04. De Haan, J., Berger, H., van Fraassen, E., 2001. How to reduce inflation: an independent central bank or a currency boar? The experience of Baltic countries. Emerging Markets Review 218–243. Dibooglu, S., Kutan, A., 2001. Sources of real and nominal exchange rate fluctuations in transition economies: the case of Poland and Hungary. Journal of Comparative Economics 29, 257–275. Dickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431. Diebold, F., Mariano, R., 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13, 253–265. Dornbusch, R., 1976. Expectations and exchange rate dynamics. Journal of Political Economy 84 (6), 1161–1176. Enders, W., Lee, B., 1997. Accounting for real and nominal exchange rate movements in the post-Bretton Woods period. Journal of International Money and Finance 16 (2), 223–254. Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: representation. estimation and testing, Econometrica 55, 251–276. Faust, J., Rogers, H., Wright, J.H., 2002. Exchange rate forecasting: the errors we've really made. Journal of International Economics 60, 35–59. Frenkel, J.A., 1976. A monetary approach to exchange rate: doctrinal aspects and empirical evidence. Scandinavian Journal of Economics 78 (2), 200–224. Granger, C., Newbold, P., 1974. Spurious regressions in econometrics. Journal of Econometrics 2, 111–120. Groen, J.J.J., 1999. Long horizon predictabilty of exchange rates: is it for real? Empirical Economics 24, 451–469.

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