INTEGRATION OF ECONOMIC ACTIVITY BETWEEN THE AREA AND

Tanja Broz The institute of Economics Zagreb Department for macroeconomic analysis and policy Trg J. F. Kennedya 7 10000 Zagreb Croatia [email protected]

Abstract This paper analyses the integration of Croatian and the euro area’s industrial production indices in order to see whether Croatia and the euro area form an optimum currency area. More precisely, it is of interest to determine would future Croatian adoption of euro be of benefit to Croatia (of course, once Croatia joins the EU). First, unit root tests are employed, then cointegration analysis and finally Granger causality test. Results indicate that there is no long run relationship between Croatian and the euro area’s industrial production indices. However, Granger causality test shows that in the short run the euro area’s industrial production causes Croatian. But, this is not enough to conclude that Croatia and the euro area form the optimum currency area.

Key words : optimum currency areas, integration of economic activity, Croatia JEL classification : E42, F15, F33

157 1. Introduction

In a situation when Croatia started with the negotiation process for joining the EU and when it is very likely that Croatia will join the EU in the next decade at latest, it is justifiable to expect that Croatia will introduce euro, since new members do not have the possibility of the “opt out” clause. For that reason it is necessary to examine whether the introduction of euro will be beneficial for Croatia or will it impose unwanted costs.

Discussion about common currency areas usually rests on the theory of optimum currency areas, which was created by Mundell (1961) and supplemented by many other authors such as McKinnon (1963), Kenen (1969), Tower and Willett (1976), Tavlas (1993), Bayumi and Eichengreen (1996) and Frankel and Rose (1998). An optimum currency area is an area for which the costs of abandoning the exchange rate are outweighed by the benefits of adopting a single currency. In addition, the OCA theory can be viewed as a tool for finding an answer to the question on how to choose the optimum exchange rate regime. It should be mentioned, however, that there is no widely accepted algorithm or index to indicate unambiguously should a country join a currency area or not. In fact, there is no standard theory of optimum currency areas, but rather several approaches that have been inspired by Mundell’s (1961) seminal paper.

The main goal of this paper is to determine would the common monetary policy with the euro area suit to Croatia. More precisely, it will be determined whether Croatia and the euro area are integrated using industrial production indices. The results should help the economic policy makes with the decisions about changing the exchange rate regime.

The rest of the paper is organised as follows. In Chapter 2 some descriptive properties of the data are explored. In Chapter 3 stationarity of time series involved in the analysis is examined. Chapter 4 determines whether those series are cointegrated. If there is a cointegrating relationship between the series, which means that variables have a long run relationship, then the error correction term is used in Granger causality test, which is analysed in Chapter 5. If there is not cointegrating relationship between variables, then Granger causality test is conducted without error correction term. Granger causality test indicates short term adjustment of the selected variables. Last Chapter concludes.

158 2. Data

The degree of economic integration is assessed by the similarities of monthly indices for industrial production for the euro area and Croatia. Series go from January 1994 until May 2005. Even though there are available data for the earlier period, it is not desirable to use those data for Croatia, because of recession period associated with the change of the economic system. Stabilisation program was introduced in the beginning of October 1993, so results conducted with sample from 1994 should be more reliable. Industrial production indices represent total industry defined by NACE.

Sources for the data on industrial production indices are Central Bureau of Statistics for Croatian data and for the euro area’s data. Both time series are seasonally adjusted.

Even though industrial production does not account for the total economic activity, there are some advantages in using industrial production instead of time series data for GDP. Since Croatia exists since the beginning of the ‘90s (and GDP started being calculated officially only in 1997), there is not enough data for conducting econometric analysis using quarterly GDP data. Industrial production is reported monthly, which gives more data and more degrees of freedom in conducting the analysis. Also, in transition economies industrial production is probably more accurately measured than many other indicators of economic activity (Korhonen, 2001).

Despite the fact that industrial production indices do not reflect total economic activity dynamics 1, until now it was constantly confirmed that the industrial production cycles concur with GDP cycles in Croatia and euro area. This can be seen from Figures 1 and 2, which show quarterly seasonally adjusted industrial production indices and quarterly seasonally adjusted GDP indices from the first quarter 1997 until the second quarter 2004.

So, industrial production is used as a proxy for real economic activity in the euro area and Croatia.

1 In the last couple of years, industry sector participate in total gross added value of Croatian economy with about 28 percent ( Čeni ć, 2005) and in euro area with about 21 percent (Eurostat).

159 Figure 1 Quarterly seasonally adjusted industrial production and GDP indices for Croatia, 2000 = 100.

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4 4 5 6 7 7 8 9 0 0 1 2 3 3 4 9 9 9 9 9 9 9 9 0 0 0 0 0 0 0 tr tr tr tr tr tr tr tr tr tr tr tr tr 1tr 4 3 2 1tr 4 3 2 1 4 3 2 1 4 3

Source: Croatian Central Bureau of Statistics

Figure 2 Quarterly seasonally adjusted industrial production and GDP indices for the euro area, 2000 = 100.

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4 7 0 3 0 r9 tr9 tr00 tr0 1t 4tr94 3tr95 2tr96 1 4tr97 3tr98 2tr99 1 4tr 3tr01 2tr02 1tr03 4 3tr04

Source: Eurostat

160 3. Unit root tests

Not until a long ago economists implicitly presumed that macroeconomic data were stationary, which for expositional purposes can be thought of as “nicely behaved”, or at least stationary around a deterministic time trend (Kennedy, 1996). But, as econometric research studies showed, most macroeconomic data are, in fact, nonstationary. This means that statistics such as t and Durbin-Watson statistics and measures such as R 2 did not retain their traditional characteristics. 2 If a researcher would run a regression with such a data, she or he would produce spurious results (Kennedy, 1996). 3 So, in order to have valid estimates, it has become very important before starting with the analysis to test for nonstationarity in time series data. 4

Even though industrial production samples have monthly data, this does not help to increase the power of the unit root test, because in unit root tests it is not the frequency of the data that counts but longer time period. Hence, in order to increase the power of the tests, we used several unit root tests. Also, after we made Augmented Dickey-Fuller test (ADF), Phillips-Peron test (PP) and Dickey-Fuller-Generalised Least Squares test (DF-GSL), we compared the results with other studies which tested industrial production indices for unit roots. Unit root test results for Croatian industrial production index we compared with Čeni ć (2005) and unit root test results for euro area’s industrial production index we compared with Korhonen (2001). Even though time span differs, the main results are the same.

Figure 3 shows industrial production indices for Croatia and the euro area in levels. Brief look to the figure indicates that those series are increasing with time, which could mean that they are nonstationary. If they are integrated in order one, this should mean that their first differences should be stationary.

2 If two stationary variables are generated as independent random series, when one of those variables is regressed on the other, the t-ratio on the slope coefficient would be expected not to be significantly different form zero and R2 would be expected to be very low (Brooks, 2002). 3 If two unrelated series contain a trend and are thus nonstationary, as the sample grows in size, the trend will dominate, causing the R 2 between two series to come near unity. This happens because the total sum of squares becomes infinite, causing R 2 (which is calculated with formula 1 – explained sum of squares/total sum of squares) to come close to one. t statistics also blows up, because of high R 2. So, this reflects the problem of spurious regression results – unrelated integrated (nonstatinary) series appear to be related, if one uses conventional methods in analysis. 4 One of the first who pointed out this matter were Granger and Newbold (1974).

161

Figure 3 Indices of industrial production for euro area and Croatia – levels

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70 Croatia 60

7 2 5 0 3 8 1 6 1 4 m11 m04 m09 m02 m0 m1 m0 m1 m0 m0 m0 m0 m1 m0 94m01 94m06 94m11 95m04 95m09 9 0 0 1 1 1 2 2 3 3 4 4 4 5 19 19 19 19 19 1996m021996m071996m121997m051997m101998m031998m081999m011999m06199 200 200 200 200 200 200 200 200 200 200 200 200 200

Source: Croatian Central Bureau of Statistics and Eurostat Series are seasonally adjusted.

Unit root tests are in this paper used in order to determine industrial production indices’ order of integration, which is important for cointegration analysis. If series are I(1), than we can proceed with tests for cointegration.

Table 1 shows results for ADF, DF-GLS and PP tests in levels and Table 2 shows results in first differences. The methodology is as follows: if t-statistics is lower than critical values (this means that t-statistics is more negative than critical values), than the unit root hypothesis is rejected at conventional test levels. On the other hand, if t-statistics is greater than critical values, the unit root hypothesis cannot be rejected at conventional test levels. Tables report t-statistics and stars next to it indicate test level at which the unit root hypothesis is being rejected.

162 Table 1 Unit root tests – ADF, DF-GLS and PP – levels TIME PERIOD ADF DF-GLS PP SERIES t-statistics t-statistics t-statistics t-statistics t-statistics t-statistics (with (with (with (with (with (with intercept) trend and intercept) trend and intercept) trend and intercept) intercept) intercept) Industrial 1994:1 0.151 -3.756 ** 1.588 -2.693 -0.169 -6.13 *** production – (0.968) (0.022) [2] [2] (0.938) (0.000) index – 2005:5 [2] [1] [20] [6] Croatia Industrial 1994:1 -1.446 -1.371 1.291 -1.142 -2.041 -1.882 production – (0.558) (0.865) [3] [3] (0.270) (0.658) index – 2005:5 [3] [3] [3] [5] euro area Source: author's calculation Comment: ADF – Augmented Dickey-Fuller test, PP – Phillips-Peron test, DF-GLS – Dickey- Fuller-Generalised Least Squares test. Variables are seasonally adjusted, Schwartz information criterion is used to determine optimum lag length for ADF and DF-GLS (in brackets) and Newey-West bandwidth with Bartlett kernel is used to determine bandwidth for PP (in italics brackets); p-values are in parentheses, *** Null hypothesis – variable has a unit root is rejected on a 1 percent level, ** Null hypothesis – variable has a unit root is rejected on a 5 percent level, * Null hypothesis – variable has a unit root is rejected on a 10 percent level.

163 Table 2 Unit root tests – ADF, DF-GLS and PP – differences TIME PERIOD ADF DF-GLS PP SERIES t-statistics t-statistics t-statistics t-statistics t-statistics t-statistics (with (with (with (with (with (with intercept) trend and intercept) trend and intercept) trend and intercept) intercept) intercept) Industrial 1994:1 -12.485*** -12.503*** -0.619 -16.433*** -29.337*** -35.489*** production – (0.000) (0.000) [9] [0] (0.000) (0.000) index – 2005:5 [1] [1] [28] [31] Croatia Industrial 1994:1 -5.977 *** -6.063 *** -1.482 -2.873 * -16.310 *** -16.569*** production – (0.000) (0.000) [3] [3] (0.000) (0.000) index – 2005:5 [2] [2] [6] [6] euro area Source: author's calculation Comment: ADF – Augmented Dickey-Fuller test, PP – Phillips-Peron test, DF-GLS – Dickey- Fuller-Generalised Least Squares test. Variables are seasonally adjusted, Schwartz information criterion is used to determine optimum lag length for ADF and DF-GLS (in brackets) and Newey-West bandwidth with Bartlett kernel is used to determine bandwidth for PP (in italics brackets); p-values are in parentheses, *** Null hypothesis – variable has a unit root is rejected on a 1 percent level, ** Null hypothesis – variable has a unit root is rejected on a 5 percent level, * Null hypothesis – variable has a unit root is rejected on a 10 percent level.

Unfortunately, results are not always unambiguous. Some unit root tests indicate that indices are nonstationary in levels and stationary in first differences, whereas other indicate that they are stationary in levels. This implies that we cannot unambiguously argue whether series is I(0) or I(1). Also, DF-GLS test (with intercept) indicates that Croatian and euro area’s industrial production indices are nonstationary in levels and in first differences, which means that there is still a doubt whether series is I(1) or I(2). But, since more test results indicate that industrial production indices are I(1) and since similar results can be found in Čenić (2005) and Korhonen (2001), we can conclude that both series are nonstationary in levels and stationary in first differences.

164 4. Cointegration analysis

Engle and Granger (1987), who wrote a seminal paper on cointegration, pointed out that a linear combination of two or more nonstationary series may be stationary. If such a stationary linear combination exists, the nonstationary series are said to be cointegrated. The stationary linear combination is called the cointegrating equation and can be interpreted as a long run equilibrium relationship among the variables.

The aim behind cointegration is the detection and analysis of long run relationships amongst economic time series variables. Given that most economic time series appear to be nonstationary, as was emphasised in the previous chapter, they require differencing or detrending in order to become stationary. A problem with differencing or detrending is that we may remove relevant long run information. The cointegration analysis provides a way of retaining both short-run and long-run information. This is the reason why cointegration analysis is used in this research, more precisely Johansen cointegration methodology.

In the previous section it was determined that industrial production indices for Croatia and the euro area are nonstationary in levels and stationary in first differences. Now, we are going to determine whether those time series are cointegrated. This is also important for Granger causality test since, if industrial production indices are cointegrated, estimates of Granger causality in first differences will be augmented with error correction term. If there is not cointegration between the variables, then there is no error correction term and we proceed only with Granger causality test in first differences.

4.1 Estimating lag length

As it was stated in the previous chapter, cointegrating relationship will be tested with Johansen procedure. The first step in Johansen methodology is pretesting the data to determine order of integration, which was done in the unit root chapter. Next, it is necessary to determine lag length which is going to be included in test for cointegration between the series.

165

The most common procedure in determining lag length is to estimate a vector autoregression using undifferenced data. The procedure begins with the longest lag length considered reasonable and test whether the lag length can be shortened. Likelihood ratio test is one of the most often used in determining whether lag length should be shortened. Besides likelihood test, there are also other methods for determining the lag length – Akaike information criterion, Schwartz information criterion, final prediction error and Hannan – Quinn information criterion, and they will be used here.

We believe that it is reasonable to set maximum lag to begin with to 14, since we have monthly data, because it is plausible that industrial production from t-14 (or more precisely more than a year) can influence today’s industrial production.

VAR for Croatian and euro area’s industrial production is as follows:

= β + β + β + β + + β + yt 0 1 yt−1 2 yt−2 3 yt−3 ... 14 yt−14 ut (1) where

yt = (2 x 1) vector of variables

β0 = (2 x 1) matrix of intercept terms

βi = (2 x 1) matrix of intercept terms ut = (2 x 1) vector of error terms

166 Table 3 Estimating lag length for Croatian and euro area’s industrial production using VAR model Lag Likelihood Final Akaike Schwarz Hannan- ratio test prediction information information Quinn statistics error criterion criterion information criterion 0 NA 1181.822 12.75057 12.79629 12.76914 1 657.8821 5.246289 7.333256 7.470435 7.388978 2 38.24683 4.049147 7.074171 7.302804* 7.167041 3 12.07407 3.894700 7.035124 7.355210 7.165143 4 12.08860 3.739286 6.994125 7.405664 7.161291* 5 9.911747 3.654180 6.970668 7.473660 7.174982 6 1.476366 3.850171 7.022287 7.616732 7.263749 7 4.961701 3.927770 7.041386 7.727284 7.319996 8 1.484495 4.138067 7.092422 7.869774 7.408180 9 9.390750 4.040623 7.067167 7.935972 7.420073 10 6.744960 4.043378 7.066080 8.026338 7.456135 11 2.006181 4.238504 7.111059 8.162770 7.538262 12 6.971928 4.223588 7.104958 8.248122 7.569308 13 28.56252* 3.357657* 6.872472* 8.107090 7.373971 14 5.494920 3.391879 6.879056 8.205127 7.417703 Source: author's calculation Comment: variables are seasonally adjusted, * indicates lag order selected by the criterion.

In Table 3 there are estimates of lag length for Croatian and euro area’s industrial production using bivariate VAR model. There are five criteria used in the analysis of determining lag length. Asterisk indicates lag order selected by the criterion. As it is noticeable from Table 3, not all results show the same estimated lag length. Likelihood ratio test statistics, final prediction error and Akaike information criterion suggest 13 lags, while Schwarz information criterion and Hannan-Quinn information criterion suggest 2 and 4 lags, respectively. Schwarz information criterion is usually smaller than others, because it imposes larger penalty for additional coefficients. But, since most criteria, including likelihood ratio test statistics and Akaike information criterion, suggest that analysis should continue with 13 lags, we will proceed to the to the next step using 13 lags.

167

4.2 Estimating the number of cointegrating vectors

Long run equilibrium relationship between Croatia and the euro area would imply relatively low costs of giving up monetary policy and joining European monetary union. This would indicate higher degree of integration and mean that monetary policy conducted by would in the long run be aligned with Croatian needs. On the other hand, if correlation of Croatian business cycle with the euro area cycle is low, then too rushed abandonment of monetary policy and accession to the European monetary union in the presence of asymmetric shocks could create welfare losses. If a country losses the option of following an independent monetary policy and gives up the possibility of changing its exchange rate when it consider necessary, this could create a major cost when entering in a monetary union.

So, after lag length was determined in the previous section, the second step in Johansen procedure is to test for existence of the long run relationship between Croatian and euro area’s industrial production. This is done by estimating the number of cointegrating vectors.

Since this analysis includes two variables, maximum number of cointegrating vectors is two. If test indicates no cointegrating vectors, then there is no cointegration between the industrial production indices. If there is one cointegrating vector, then there is a long term relationship between industrial productions. And, on the other hand, if the test indicates two cointegrating vectors, this means that unit root tests wrongly made us to conclude that variables are nonstationary and they are, in fact, stationary, which suggests that the analysis should not have been performed using cointegration analysis, but that standard time series analysis should have been employed.

168 Table 4 Determining the number of cointegrating vector for Croatian and euro area’s industrial production

Hypothesised Eigenvalue λtrace 1% 5% λmax 1% 5% number of statistics critical critical statistics critical critical cointegrating value value value value vectors (maximum rank) 0 0.028 3.508 19.937 15.494 3.500 18.520 14.265 1 6.68E-05 0.008 6.635 3.841 0.008 6.635 3.841 2 ------Source: author's calculation Comment: variables are seasonally adjusted, lag length = 13.

Table 5 Information criteria by models

Hypothesised No trend With trend number of LL LL SIC SIC AIC AIC LL LL SIC SIC AIC AIC cointegrating (at (at (at (at 5%) (at (at (at (at (at (at (at (at 5%) vectors 1%) 5%) 1%) 1%) 5%) 1%) 5%) 1%) 5%) 1%) (maximum rank) 0 -366.8 -366.8 8.077* 8.077* 6.843 6.843 -366.8 -366.8 8.077* 8.077* 6.843 6.843 1 -365.1 -365.1 8.205 8.205 6.879 6.879 -361.2 -361.2 8.182 8.182 6.833* 6.833* 2 -365.1 -365.1 8.362 8.362 6.944 6.944 -359.9 -359.9 8.356 8.356 6.893 6.893 Source: author's calculation Comment: variables are seasonally adjusted, lag length = 13. LL = Log likelihood, SIC = Schwarz information criterion, AIC = Akaike information criterion. Models: data trend – linear, test type – with intercept and with or without trend. * indicates significance at given level.

Johansen procedure uses λtrace and λmax statistics to determine the number of cointegrating vectors. The first t t51514t51514t51514t5114t5114t5114t5114t5114t558(r)2.80561(s)-1.2(r)2.80561(s)-1.2(r)2.80561(s)-1.2(r)2.8056185rrnrr0rssssssss01.20r0r01.20r0rJ/R8cointegrating vectors. The first t 12 Tf0561(J/R8 12 Tf0561(J/R8 12 Tf0561(J/R8 12 Tf0561(J54()-2.05734(67 18 6 ref1642)2.80561(J/.05734(67 18 6/.05734(67 18 6/.055585(u)-0.2955863()4.)-0.2955863()4.)-0.2955863()4.)-0.2955863()4.)-0.2955863()4.)-0.2955863()4.)-0.2955863()4.)-0.2955863()4. tczrating vector fTh558(o)-0.171439.171439( )2512.87t f Tpro he 2.87 q0.2955863()4558(o)-0a.36 503.36 Tm[(92.87)-1879ermsr 52073(3(r)8.7J/824154(n)9.71(s)-1.20r)2.807nr0r01.20r076 Td[(c)01.20r0rsr 52073(3(r)8.7J/824154(n)9.7.807)-1879e5(e)3.74024( )-420.395(2.87)-1879er18 6/.055585(u)-0.295587

169 r + 1. The procedure for determining the number of cointegrating vector is similar to λtrace statistics.

It is also possible to use information criteria for determining the best model for estimating the numbers of cointegrating vectors. In this case log likelihood, Schwarz information criterion, Akaike information criterion were used.

Table 4 shows the results of cointegration analysis using λtrace and λmax statistics. The first hypothesis tests whether there is no cointegrating vector. Since λtrace and λmax statistics are smaller than both critical values, it can be concluded that the null hypothesis should not be rejected. This means that there is no cointegration between Croatian and euro area’s industrial production, implying that there is no stable long run relationship between Croatia and the euro area. Table 5 shows similar picture of no cointegration. Schwarz information criterion is significant with the null hypothesis of no cointegrating vectors, while others, even though are not significant, have the smallest value under the same null, which indicates no cointegration. The exception is Akaike information criterion using trend, which is significant under the null hypothesis of one cointegrating vector. But, since all other values in Tables 4 and 5 indicate that there is no cointegration between the variables, we can conclude that Croatian and euro area’s industrial production is not cointegrated and has no stable long run relationship.

This result is also important for the next step in which we are going to determine whether euro area’s industrial production in the short term causes changes in Croatian industrial production. Since there is no cointegration between the variables, Granger causality test will not be augmented with error correction term and instead only Granger causality test in first differences will be conducted.

5. Granger causality test

Test of causality analyses whether the lags of the one variable enter into the equation for another variable. In that respect, Granger (1969) approaches to the question of whether x causes y in order to see how much of the current values of y can be explained by the past values of y and then to see whether adding lagged values of x can improve the explanation. y

170 is said to be Granger caused by x if x helps in the prediction of y, or equivalently if the coefficients on the lagged x’s are statistically significant. Granger causality test is two way test. If x Granger causes y, this does not mean that y does not Granger causes x. It could be that test shows both that x Granger causes y and that y Granger causes x. This is called the endogeniety problem.

When conducting Granger causality test, it is necessary to choose the number of lags for the test regressions. In general, it is better to use more lags rather than fewer, since the theory is formulated in terms of the relevance of all past information. With monthly data, usually 6, 12, 14 or even up to 24 lags are used. Lag length should be picked in a way that it corresponds to reasonable beliefs about the longest time over which one of the variables could help predict the other. Since industrial production more slowly responds to changes than, for example, monetary variables, we believe that it is reasonable to use 14 lags for Granger causality test.

In order to determine potential endogeniety problem, the Granger causality test will be conducted in two ways. First, we will determine whether euro area’s industrial production Granger causes Croatian industrial production and then we will determine whether Croatian industrial production Granger causes euro area’s industrial production. If hypothesis can be rejected in both cases, then there is an endogeniety problem.

So, there are two null hypotheses:

Euro area’s industrial production does not Granger cause Croatian industrial production. Croatian industrial production does not Granger cause euro area’s industrial production.

Granger causality test will also tell us about the short term responses of Croatian industrial production to shocks to euro area production. So, if euro area’s industrial production does Granger cause Croatian industrial production, this means that euro area’s monetary policy is compatible with Croatian short term needs and that euro area’s monetary policy could be effective for Croatia in the short term.

171 So, the first null hypothesis can be expressed in equation format using VAR for two variables:

=α +α + +α + β + + β + Croatia t 0 1 Croatia t−1 ... 14 Croatia t−14 1 Euro area t−1 ... 14 Euro area t−14 ut (2)

H0: β1 = β2 = … = βl = 0 (3)

For testing the potential endogeniety problem, the VAR model is:

=α +α + +α + β + + β +ε Euro area t 0 1 Euro area t−1 ... 14 Euro area t−14 1 Croatia t−1 ... 14 Croatia t−14 t (4)

H0: β1 = β2 = … = βl = 0 (5) where Croatia = Croatian industrial production Euro area = euro area’s industrial production α, β = parameters

Table 6 Granger causality test in first differences NULL HYPOTHESIS F STATISTICS PROBABILITY EURO AREA does not 2.71925 0.00215 Granger cause CROATIA CROATIA does not Granger 1.31017 0.21650 cause EURO AREA Source: author's calculation Comment: variables are seasonally adjusted, lag length = 14. CROATIA = Croatian industrial production, EURO AREA = euro area’s industrial production.

Table 6 shows Granger causality test in first differences for Croatian and euro area’s industrial production. First hypothesis that euro area’s industrial production does not Granger cause Croatian industrial production can be rejected, which means that changes in industrial production of the euro area causes changes in Croatian industrial production. Second hypothesis investigates potential endogeniety of the model and as table shows it cannot be

172 rejected, which means that there is no endogeniety problem in the model. This also indicates that Croatian industrial production does not Granger cause euro area’s industrial production. Due to the fact that euro area is much larger than Croatia, this is not a surprising result.

6. Implication of results

Results in this paper indicate that:

There is not a long run equilibrium relationship between Croatia and euro area, more precisely between Croatian and euro area’s industrial production indices.

Euro area’s industrial production in the short term causes changes in Croatian industrial production.

As it has already been explained cointegration test is a method for detecting a long run relationship between variables. The result of the test has shown that there is no cointegration between Croatia and the euro area in terms of industrial production and the variables in question can move independently of each other and can drift apart. This result implies that one of the OCA criteria is not satisfied and in this context this means that Croatia and euro area do not form the optimum currency area and that in the long run ECB monetary policy may not suite Croatia. Also, it seems that drifting apart in industrial production indices is actually happening in the last few years, since Croatian industrial production is trending, while the euro area’s industrial production is more or less stagnating, which can be seen from Figure 3. It is interesting to note that stagnating faze of euro area’s industrial production concur with the introduction of euro.

However, there are few things to note. First of all, as all econometric methods, this one also has its limitations. In this case, limitations are most likely to be connected with the short data span. Only 11 years and 5 month of data are used, which is quite small sample, so the results might be unreliable and they might change, when more data will be available. To continue, industrial production is only one indicator, which is used to compare the behaviour

173 of real activity in Croatia and the euro area. There are other indicators that might be used, like GDP or unemployment rate.

On the other hand, the results of the Granger causality test indicate that the euro area’s industrial production has the influence on Croatian industrial production in the short term. Since industrial production in this paper is proxy for GDP and consequently for the real activity, it can be said that the euro area with its short term real activities can induce changes in Croatian real sector, which is also an important factor if a country considers joining the euro area, after entering in the EU. However, unfortunately, this is not enough to conclude that Croatia and euro area form an optimum currency area, because of the potential repercussion in the long run. For example, the ECB does not change interest rates for every short term disturbances. Rather, the goal of ECB is, besides maintaining price stability, to support high employment and sustainable growth, which means that ECB is oriented to minimise medium to long term business cycle deviations, instead of short run fluctuations.

There are some papers that confirm the results of this analysis. Benczur, Koren and Ratfai (2004) were interested to see how impulses from the euro area are transmitted to the eastern European countries. The results show that for the most countries impulses are transmitted through monetary channels, while for Croatia impulses are transmitted through real sector. Analysis conducted in this paper confirms his findings, since the results of the Granger causality tests point that there is a link, although only in the short term, between Croatian and the euro area’s industrial production.

Even though Croatia is reasonably well integrated with the short term business cycle of the euro area, since there is no long run equilibrium relationship between them, too hasty abandonment of monetary independence could create welfare losses. This also confirms Čeni ć (2005), who points out that exchange rate channel as a transmission mechanism is important in Croatia, since monetary policy can influence industrial production by changing the exchange rate.

Korhonen (2001) conducted a similar analysis as in this paper for a sample of eastern European countries, excluding Croatia, and he got mixed results. He finds that and Slovenia are quite integrated with the euro area, while, for example, appears to be very little integrated. Since he finds that, compared with some smaller present euro area

174 members, some eastern European countries are well integrated with the business cycle of the euro area, this indicates that with further economic growth, Croatia may expect to become more integrated with the euro area.

Similar studies were conducted in many transition countries, especially in those that entered the EU in the last round of enlargement. Since some of the countries already entered ERM II, they obviously consider that benefits of joining the monetary union outweigh the costs and that their business cycles are synchronised with euro area’s, so that common monetary policy will not have negative effects. 5

However, there are also some different findings for Croatia. Šonje and Vrbanc (2000) compare Hungarian, and Croatian business cycles with German business cycle and conclude, using Hodrick –Prescott filter to detrend the variables, that there is symmetry between selected transition countries and Germany. Similar study was conducted by Belullo, Šonje and Vrbanc (2000) in which they broaden the sample and conclude that there is a strong linear relationship between German and central European shocks in unemployment and cycles in unemployment. Hence, they concluded that benefits from joining the euro area are high and costs of abandoning exchange rate for transition countries, including Croatia, are low.

But, as we already stated, long run relationship in this paper is what tells us whether countries or regions form an optimum currency area. As it can be seen from the cointegration test, Croatia and euro area are not cointegrated, which means that they do not form an optimum currency area.

The reason why Croatia and the euro area are not cointegrated can be found in convergence theory. If Croatia wants to catch up the euro area in GDP per capita level, than Croatia has to grow much faster and need to have higher growth rates. This means that their industrial production indices will diverge from each other, until Croatia comes to euro area’s level of development. So, if Croatia would join the euro area now, different states of the economy would require different monetary policy.

5 However, one could also claim that joining the euro area is a political decision, so that the above mentioned countries do not think that they satisfy the OCA criteria and in that case satisfying the OCA criteria for those countries is not important.

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This conclusion also does not mean that Croatia should stop thinking about the time when it will enter the EU and consequently later to the euro area, but that Croatia has to be careful in preparation for this process, because not all decisions from the European central bank will affect it in a way Croatia desires.

However, there is one more theory which can be applied here and that is endogeniety of optimum currency areas. Frankel and Rose (1998) find that international trade and international business cycle correlations are endogenous. This means that if a country decides to join a common currency area, this can help it to significantly raise trade linkages, which in turn causes that business cycles become more synchronised. So, predictions based on historical data are invalid, if some policy measure alters the relationship between the relevant variables. This means that when Croatia joins the euro area, due to the increased trade with the other members, which will also increase the level of development, Croatia will experience increased business cycle correlation with them. So, if one conducts this kind of analysis after Croatia joins the euro area, he or she would probably get different results, which would mean that then Croatia and the euro area would have cointegrated industrial production indices and that then will be a long run equilibrium relationship between them.

So, conclusion of this paper is not as black as it may at first glance seem.

References

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