beyzatlar-Çetin-solowian approach to european convergence with gmm

8/4/2019 Beyzatlar-etin-Solowian Approach to European Convergence with GMM

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Solowian Approach to European Convergence with GMM

Mehmet Aldonat BEYZATLAR ve Mehmet ETN

Dokuz Eyll niversitesi

ABSTRACT

Optimum Currency Areas Theory constructs an important theoretical framework for Europeanmonetary integration. The degree of financial integration is a vital element in this process. In

recent studies, financial integration as a part of the Lisbon Strategy is seemed as a key factorof the EUs economic strategies. One of the most important fundamental features of optimumcurrency area is the convergence of financial indicators to each other. This paper tests andanalyzes the hypothesis of the convergence of interest rates depending on the convergenceframework for the period 1993-2010 using Generalized Method of Moments method for EU-15 countries.

Keywords: Optimum Currency Areas Theory, Interest Rates, Financial Integration, GMM.AMS 1991 Classification: Primary 62M10JEL Classification: C23, F15, F36

1. INTRODUCTION

Theoretical researches within financial integration that concentrates on what determines thevariety of models sorely emphasizing different financial instruments, which might determinehow financial integration affect other economic factors. Interest rates as a Maastricht criteriaand an important financial instrument determines the efforts on effecting vital economicmeasurements such as inflation, credits, etc.

An integrated financial market should meet the same relevant characteristics such as facing

with a single set of rules, having equal access and treating equally when they decide to dealwith financial instruments and/or financial services to be fully integrated. Therefore, financialintegration is independent of the financial structures within regions, concerned with thesymmetric or asymmetric effects of frictions on different regions, separates the supply and thedemand for investment opportunities as the two constituents of a financial market.

Several approaches to measuring how financial integration might have exacerbated the impactof the crisis in emerging Europe lead to more financial integration, even contractions ofnational accounts. Most of the studies among EU member states emphasize the existence ofconvergence among these countries. In this study, the hypothesis of the convergence ofinterest rates is analyzed and evaluated by convergence framework is based on Solows

(1956) model.The analysis about convergence covers economic and econometricperspectives, where Generalized Method of Moments (GMM) method is used to analyze EU-

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27 countries convergence with respect to interest rates for the period 1993-2010. It isargued in this paper that only when all economies are able to access tothe financial integration it may eventually leads to convergence of interestrates in the long run. In the light of the econometric findings, policy implications are set.

This Solowian neo-classical growth model predicts that poor economiestend to grow faster than rich ones. The speed of convergence, initialwealth, steady state circumstances and many other restrictions defineconvergence hypothesis but the assumption of diminishing returns iscrucial to hold. Based on Solows model, inauguration of this literature is oftenattributed to Barro 1991; Barro and Sala-i-Martin (1991 and 1995); Neva and Gouryette(1995) and some other contributing studies in this strand of research are Button and Pentescot(1995), Mauro and Podrecca (1995), Lyberaki (1996), Fagerberg and Verspagen (1999) andetc. There are also a vast amount of studies devoted to convergence withrespect to economic measurements from different perspectives, Baumol1986; Barro and Sala-i-Martin 1992; Mankiw, Romer and Weil 1992; Jones

1997; Pritchett 1997 and De Long 1998; among others.

This study aims to supply more substantial evidence on the convergence of interest rates byemploying a comprehensive data set and GMM approach as an advanced econometrictechnique. GMM method is proposed because this testing procedure allows us to take intoaccount convergence from a robust perspective (Caselli et al, 1996; Bond et al, 2001). Theseare described in detail in the next section. The organization of the article is as follows. SectionII covers literature; Section III develops a GMM approach to reveal the convergence ofinterest rates to Maastricht criteria interest rate as financial integration. Section IV presentsmain conclusions.

2. OPTIMUM CURRENCY AREAS THEORY

The essence of modern Monetary Union Theory is based on Mundell (1961)s OptimumCurrency Area (OCA) Theory. Mundell defines Optimum Currency Area as the size of theregion that maximizes the difference of positive and negative effects of monetary union.McKinnon (1963) makes his definition on OCA as the region that the objectives of balance

payments and price stability provided with lowest cost through economic policies of membercountries. OCA theory focuses on conditions that OCA would be successful in a region inwhich fixed exchange rates policy is carried out (Patterson and Amati, 1998). Eventually notevery monetary area is an OCA; potential members of OCA have some obvious economic

characters. Analyzing these characters it can be tested if a monetary are is an OCA. Mundell(1961), McKinnon (1963) and Kenen (1969), the pioneers of the theory, discussed thesecharacters for the first time. To create an OCA and to function successfully a monetary unionrequires some circumstances. According to the theory of OCA, the minimum conditions ofunion success can be listed as: Not to come across frequent and large-scaled asymmetricmacroeconomic shocks, price stability and closeness of inflation rates, mobility of productionfactors among member countries and flexibility of price and real wage and thus to have abilityto adapt to shocks, monetary union member countries should not have different inflation-unemployment trade of preferences and a high financial integration level between membercountries. In Mundell (1961), the most important missing point about OCA was financialintegration and stability. The concept of financial integration is a sub-objective of economic

integration and the determinant of monetary and fiscal policies in order to provide economicintegration.

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The degree of financial integration is a vital element in this process. In recent studies,financial integration as a part of the Lisbon Strategy is seemed as a key factor of the EuropeanUnions growth and strategies in the competitive world. Financial integration is considered toimprove the allocation of efficient capital and the diversification of risks. One of the most

important fundamental features of optimum currency area is the convergence of financial andmonetary indicators to each other in where countries using the same currency as commoncurrency area. According to the theory, borrowing interest rates and market interest rates ofthese countries is expected to converge to financial instruments in Euro as common currencyand interest rates set out in the Treaty of Maastricht. Furthermore, the convergence ofdomestic borrowing interest rates of these countries to interest rates as an indicator of Euro asa common currency provide information about the existence of monetary convergence andthus financial integration. This could imply that the degree of convergence in political andeconomic performance and/or structures is important for the benefits of the reduction betweencountries economies. Moreover, perfect convergence is not necessary for the constitution ofan optimum currency area, not even in its Pareto form (Collignon, 1997).

3. LITERATURE

The first study examining convergence in the Union was carried out by Barro and Sala-i-Martin. Barro and Sala-i-Martin (1991) examined 73 European Region for the period 1950-1985. Barro and Sala-i-Martin (1995), as a result of their convergence analysis found theexistence of and convergence among 90 regions of eight European countries betweenyears 1950 and 1990. Sala-i-Martin (1996) included 90 regions in Europe to his analysis andas a result he estimated approximately 2% speed of convergence.

Neva and Gouryette (1995), following the method of Sala-i-Martin, examined the 73 EU

region taking into account the time interval 1950 1985 and showed that also it is not strongin the Southern Europe there is a overall convergence in the early 1980s. Button andPentescot (1995), taking into account the history of European Union, examined the WesternZones of Union. However, also including 1980s, they did not come across any convergencetendency. Mauro and Podrecca (1995), has taken into consideration the Italian regions overthe period 1970-1991 using time series method they especially investigated the existence of -convergence. Despite the occurrence of weak convergence until the beginning of 1980s aftertaking into account the entire period they noted that there is not a continuous process ofconvergence. Lyberaki (1996), in his study on Greek economy, he stated that the hypothesisof convergence is not valid contrary there is a divergence from the European Union.

Fagerberg and Verspagen (1999) analyzed the regions of EU9 and EU12 countries over the period 1960-1995, considering the convergence in terms of per capita income. Betweenperiod 1960 and 1980, on both regional and national level there was a significant decrease inthe distribution in EU9. Thus, a path towards European convergence emerged. After 1980, thetrend continued at the national level with a lower rate. According to the European EconomicResearch Report (2000), there is no tendency (-convergence) of fast growing up in the poorcountries than rich countries however, when various structural variables has been added to themodel, it is concluded that convergence (conditional convergence). Martin and Sanz (2001)examined Greece, Ireland, Portugal and Spain between the period from 1960 to 2000 in termsof per capita income convergence. According to the results both four countries converge to

the EU average. However Ireland is the most successful and Greece is the last in this process.Brasili and Gutierrez (2004) analyzed the per-capita incomes convergence process across 140

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NUTS2 European regions during the period 1980-1999 using panel unit root tests. They findthe evidence of convergence among the EU regions. The distribution of per capita incomeconverges toward the average pole and the tests strongly reject the null of divergence.Saraolu and Doan (2005) uses five different panel unit root tests to investigate incomeconvergence for the European Union and candidate countries using quarterly data of these

countries for the period between 1990 and 2004 on per capita GDP. The evidence suggeststhat the null hypothesis that subgroups of EU economies do not converge to the average GDPof the first 15 EU countries cannot be rejected. Sassi (2010) analyzes the role and sources ofwithin and between sectors convergence in the context of the economic catching-up processacross the European Union regions. The growth-accounting approach used in the studyunderlines the role of structural composition and change on the process of economic and

between-sectors catching-up.

The basic literature on convergence mainly focuses on growth rates. Nevertheless, relateddirectly with this study, in some studies interest rates were also taken into account. One of therecent studies is Frmmel and Kruse (2009); in the study they analyze the convergence of

interest rates in the EMS in a framework of changing persistence. Their empirical resultssuggest different convergence dates for analyzed member countries. The main factors drivinginterest rate convergence between analyzed countries were the coordination of budgetary andmonetary policy leading to stable exchange rates in the run up-to EMU. Weber (2006)analyzes British state of convergence towards Euro area compared to USA. Although UK has

been perceived as not aiming at strengthening of the European Integration the results aregenerally in favor of a growing British integration into the European Currency Union.

In particular, overall literature has shown that a significant percentage of variation inconvergence (across countries and in time) could be explained by variations in structuralissues. This is often dubbed a direct causation between beginning situation and target level(Maastricht criteria in this study). It also seems to be logical to expect that the difference

between countries economies also has some explanatory power on the future differences atmany economic levels and even on its growth rate, which is often dubbed by many studies.There could be many mechanisms that may be backing this end result. It is natural to expectthat kind of interaction, which complements and enhances disparateness. This linkage wasfirst pointed out by Barro and Sala-i-Martin (1991). It has been studied elaborately fromdifferent angles theoretically and empirically since then. For empirical studies, see Caselli etal, 1996; Sala-i-Martin, 1996; Lee et al, 1997; Dowrick and Rogers, 2002; Badinger et al2004; Weeks and Yao, 2003; Borys et al, 2006; Lau, 2009.

4. METHODOLOGYIn this study, convergence equation is derived from The Solow growth model with Cobb-Douglas production function under external technology. GMM approach provides significantoutcomes in the context of convergence. In the following part, the general theoreticalframework of convergence and GMM approach are given with their main dimensions briefly.

4.1 Convergence

There are several approaches and methodologies that can be used in empirical studies ofconvergence, which is an important fundamental application of economic growth. The major

distinction is between approach (economics) and empirical tests (econometrics). In thisarticle, we opted to use long-run interest rates to test the convergence to Maastricht criteria

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interest rates. Convergence equation derived from Solowian growth model is used to testempirically.

Cobb-Douglas production function under external technology, Harrod-neutral andlabor-saving restrictions is assumed as follows;

(1.01)

where under these restrictions fundamental equation of growth shown as follows;

(1.02)

and per efficient labor capita form of equation (1.03) denoted below;

(1.03)

per efficient labor type of fundamental equation of growth could be derived in by taking the

log-differential where .

(1.04)

The equation (1.04) is not linear and could not be used for an empirical test as amodel. Therefore this equation should be used after linearization. Taylor approximation recipeis exploited for linearization process of that non-linear equation. Taylor approximation is anappropriate solution that produces approximation series in terms of polynomials related onlyto coefficients equal to the derivation of the function at that point. The basic representation ofTaylor approximation where is a constant point

The constant point in model derivation is assumed the steady state point and Taylorapproximation is applied to equation (1.04) the general form of equation denoted as follows

(1.05)

and could be found by taking the derivative of equation with respect

to .

(1.06)

This equation exhibits the velocity of growth, where equation (1.06) is linear when

compared to equation (1.04). difference denotes the distance of the current

situation from its steady state. This is an important and fundamental element of growthliterature in terms of income-growth rate comparison of relevant economies.

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In that equation is assumed and convergence rate equationis derived as follows. This equation gives at which speed the economy approximates its steadystate.

(1.07)

Equation (1.06) is linear hence, it should be solved with respect to where ,

and . Then both sides of the equation is multiplied by and

is obtained. For t=0, and

then equations solved for percapita income. To this end convergence equation is derived as a form used in empiricalstudies.

(1.08)

where this convergence equation regresses the interaction between growth rate with respect tostarting point (left-hand side) and external variables. The sign of is negative, whichcorresponds the effect of initial income that is consistent with convergence theory. The morethe magnitude of initial income contributes less to the speed of convergence to steady state.The less the magnitude of initial income contributes more to the speed of convergence tosteady state.

4.2 GMM Approach

The literature provides diversified methods for panel data estimation. Panel data approachesare distinguished from each other in terms of parameter estimation problems. Standard paneldata approach take cross-section and period elements into account to reach robust multi-dimensional outcomes.

GMM approach testing procedure in panel data models is classified as one of the main type ofpanel data approaches. The first one was pioneered by Lars Peter Hansen in 1982, which hasprovided a discussion of the large sample properties of a class of econometric estimators.These estimators are examined under different conditions such as consistency and asymptotic

normality that are defined in terms of orthogonality conditions. GMM approach provide theexploitation of effective estimators when the number of moments extent the number ofparameters. GMM estimator is directly derived from moment conditions and parameter vectoris derived by showing variance moments as a scale and by minimizing the sum of squares ofthe difference between sample moments and population moments (Bronwyn, 1999).

In many studies, moment conditions are emphasized as . That momentcondition included an unknown parameter vector, which is estimated and tested conveniently

by Hansen (1982). GMM approach requires instrument variables to overcome covariancediscrepancies raised from the interaction between lagged values of dependent variable anderror term. Instead of lagged values of dependent variables, which are relational with error

term, one or more instrument variables could be used under some restrictions.

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The advantages of GMM over other panel data approaches are clear: if heteroscedasticity ispresent, the GMM estimator is more efficient than other tests estimators. Nevertheless, theuse of GMM provides substantial results for convergence (Caselli et al, 1996; Dowrick andRogers, 2002; Weeks and Yao, 2003). In addition, GMM overcomes the exogeneity andexternality problem between variables and takes into account the effect of excluded variables.

5. DATA, MODEL AND EMPIRICAL FINDINGS

The data of our study in an attempt to test the convergence by Maastricht criteria interest ratesbetween EU countries in a panel data setting are derived from OECD statistical database. Wehave included balanced panel data set for long-term interest rate (LIR) and Maastricht criteriainterest rate (MIR) on EU-15 countries between 1993Q1 and 2010Q4.

At first, panel unit root tests are performed for the logarithm value of as

variable. The first evidence in favor of divergence is presented in Table 1.According to all panel unit root tests, both variables are found integrated of order one, whichdenotes the non-existence of convergence.

Table 1: Panel Unit Root Tests for Convergence in Interest Rates to Maastricht Criteria

Tests Stationarity

Null Hypothesis: Common Unit Root

Levin-Lin-Chu 1.18829 Has Unit Root

Breitung 5,14576 Has Unit Root

Null Hypothesis: Individual Unit Root

Im-Pesaran-Shin 0,33059 Has Unit Root

Fisher-ADF 28,5134 Has Unit Root

Fisher-PP 28,0261 Has Unit Root

Null Hypothesis: No Unit Root

Hadri 12,7112 Has Unit Root

Therefore, results obtained from panel unit root tests are favorable for GMM panel data

approach. These variables are used to construct our convergence model is as follows:

(1.09)

First order difference of and are taken for GMM testing procedureand the final model comprised as follows:

(1.10)

In the case of outputs, the period of analysis is from 1993 to 2010. The second evidence in

favor of divergence is presented in Table 2-a and 2-b, which shows the positive relationship between (from 1993 to 2010) and at the initial level of long term

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interest rates. This positive interaction holds from 1993 to 2001 before transition to singlecurrency. The estimated coefficient in equation (1.10) is positive for 1993-2010 and 1993-2010, as expected and statistically significant. However, after transition to Euro, the firstevidence in favor of convergence is presented in Table 2-c. The convergence rate is 1.15%(see Table 2-c). To get an idea of the speed (in quarters) with which this convergence should

take place, we calculated the half-life for closing the gap between the long run interest rates ofthe relatively lower countries and the relatively higher ones. In this case, the convergence rateimplies that half the gap should be closed in 13 quarters (see Table 5).

Table 2: Panel GMM Test for Beta Convergence in Interest Rates to Maastricht Criteria

Coefficient Convergence1993Q1 2010Q4

a)- Constant Term -0,001248 (-0,727732)

No Convergence- Convergence 0,228500** (2,238138)

1993Q1 2001Q4

b) - Constant Term 0,025083** (2,567999) No Convergence- Convergence 3,192732*** (3,324163)

2002Q1 2010Q4

c)- Constant Term -0,002253 (-0,628633)

Convergence- Convergence -0,549417** (-2,079399)

Note: ***, ** and * denotes the significance level at 1%, 5% and 10% respectively and numbers in parenthesesare t-statistics. The number of observations, cross-sections and periods included are 1050, 15 and 70respectively.

The empirical evidence in Table 2-c tends to confirm the absolute convergence of interestrates to Maastricht criteria across EU-15 countries. The examination of panel unit root tests

could be the evidence of divergence as an alternative test for beta convergence. Nevertheless,the findings confirm hypothesis that the interest rates across countries follow theoretical andempirical patterns drawn.

Table 3: Country-Level Representation of Convergence 1993Q1 - 2010Q4

D = 0,025083 + 3,192732 D + [CX=F]

CountryCross-Section

EffectsCountry BasedConvergence

Germany -0,0015110 3,1912210Denmark -0,0024010 3,1903310

Netherlands 0,0000708 3,1928028Finland -0,0047840 3,1879480Sweden -0,0041600 3,1885720

Luxembourg 0,0000156 3,1927476France -0,0000312 3,1927008

United Kingdom -0,0016930 3,1910390Austria 0,0004990 3,1932310

Belgium 0,0017660 3,1944980Italy -0,0033510 3,1893810Spain -0,0001030 3,1926290

Portugal 0,0026710 3,1954030Ireland 0,0118850 3,2046170

Greece 0,0011260 3,1938580

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In Table 3, for the whole period, cross section effects of Germany, Denmark, Finland,Sweden, France, United Kingdom, Italy and Spain have negative coefficients that indicate

positive contribution to interest rate convergence. The convergence equation is regressed forthe periods 1993 2001 and 2001 2010 before and after transition to single currencyrespectively.

Table 4:Country-Level Representation of Convergence 1993Q1 - 2001Q4

CountryCross-Section

EffectsCountry BasedConvergence

Germany 0,0069020 0,7355870Denmark 0,0052860 0,7346970

Netherlands 0,0085670 0,7371688Finland -0,0014100 0,7323140Sweden 0,0013850 0,7329380

Luxembourg 0,0045280 0,7371136France 0,0063010 0,7370668United Kingdom 0,0031100 0,7354050

Austria 0,0073960 0,7375970Belgium 0,0060820 0,7388640

Italy -0,0088240 0,7337470Spain -0,0058760 0,7369950

Portugal -0,0095800 0,7397690Ireland 0,0036780 0,7489830Greece -0,0275440 0,7382240

In Table 4 the results of country based cross section effects display another scenario that thesigns of the coefficients of countries except Finland, Italy and Spain has changed. Parallelwith the cross-section effects, convergence rates decreased, which implies a divergence acrosscountries. This situation shows that, before the transition to common currency, the level offinancial integration of the union seems to be lower with respect to European Monetary Union(EMU). The results after transition to Euro are included in Table 5. Convergence rates andspeed consistency increased after 2002.

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Table 5: Country-Level Representation of Convergence 2002Q1 - 2010Q4

D( ) = - 0,002253 - 0,549417 D( ) + [CX=F]

CountryCross

Section

Effects

CountryBased

Convergence

ConvergenceConsistency

Speed

ConvergencePeriod

Germany -0,0094560 -0,5509280 0,0062693 0,1366592Denmark -0,0096600 -0,5518180 0,0062775 0,1366944

Netherlands -0,0079530 -0,5493462 0,0062548 0,1365965Finland -0,0079700 -0,5542010 0,0062995 0,1367885Sweden -0,0093980 -0,5535770 0,0062937 0,1367639

Luxembourg -0,0042460 -0,5494014 0,0062553 0,1365987France -0,0060120 -0,5494482 0,0062557 0,1366006

United Kingdom -0,0062280 -0,5511100 0,0062710 0,1366664Austria -0,0060150 -0,5489180 0,0062508 0,1365795

Belgium -0,0023110 -0,5476510 0,0062391 0,1365292

Italy 0,0018190 -0,5527680 0,0062863 0,1367320Spain 0,0053490 -0,5495200 0,0062564 0,1366034Portugal 0,0142420 -0,5467460 0,0062308 0,1364931Ireland 0,0196360 -0,5375320 0,0061454 0,1361234Greece 0,0282040 -0,5482910 0,0062450 0,1365546

The asymmetric effects of shocks on countries raised from crisis could be monitored withrespect to the difference between borrowing interest rates while convergence rates areidentical. That situation shows European Monetary Union is confronted with anotherhandicap on the road to Optimum Currency Area. Moreover, the reversion or the discrepancy

between interest rates, borrowing interest rates and amounts against convergence speed

indicates the formation of financial repression, which is also reputed financial pressure. In thatcontext, 1993 to 2010 period is examined separately; 1993-2001 as pre-Monetary Union and2002-2010 as post-Monetary Union. That kind of distinction exposes the interaction betweenconvergence and monetary union beneath monetary policy. Tables 4 and 5 demonstrate that,the acceleration of financial integration level convergence of countries after the transition tosingle currency could be indicated from the differentiation of convergence speed. Although,transition to Euro on the way to full economic integration is seemed an accurate policy movecoordinated by EU, the existence of monetary union could not interact the general trend toconvergence side. Despite the convergence of borrowing interest rates in the union,differentiation in the speed of convergence in time damaged the homogeneity of financialstructure and possible symmetric prevention and reactions to financial crisis. As seen in

Figure 1, countries have different borrowing rates and amounts. Greece, Italy, Belgium,Portugal and Ireland have greatest government debt to GDP ratio EU-wide. In the near future,Greece, Portugal and Ireland from these countries have debt crisis. Because of lower financialintegration and common fiscal policy, economic coordination policies of EU have failed.

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Figure 1: EU-15 Government Debt/GDP Ratios

Source: IMF Statistical Database

6. Concluding Remarks

European Monetary Union seems to be the most successful Union in the history with respectto its economic size. Accessing to a single currency was the most important moves against thesuperiority of US Dollar. Financial integration level is an important and fundamental criterionof OCA on the goal of full economic integration. Interest rate convergence is one of the basicindicators of financial integration. Moreover, after financial liberalization policies thefinancial capital could move across countries without any constraint and this will equalizeinterest rates. EU is in a blurred crisis and member countries do not have chance to use

monetary and exchange rate policy instruments because of monetary union. In thisframework, financial integration is vital in building up and prevailing common policies toneutralize the harmful effects of crisis. Because, EMU countries transfer the power of theirmonetary authorities to a common association.

In this paper we examine the empirical validity of convergence across EU-15 countries usinglong-term interest rates during 1993Q1 to 2010Q4. Using panel unit root tests any strongevidence could not be found in support of beta convergence across EU-15 countries on long-term interest rates. In order to determine the financial integration by convergence of interestrates of EU countries to Maastricht criteria interest rate, GMM approach to a panel data modelwith fixed effects for cross-section. Significant results of testing GMM hypothesis shows the

existence of convergence consistent with Barro and Sala-i-Martin, 1991 and 1992. Although,there is an overall convergence to Maastricht criterion interest rate, the speed of convergencedifferentiates across member economies. Therefore, the combination of this situation withdifferent debt/GDP ratios causes discrete economic paths for economies. Under thesecircumstances, the findings will play a critical role on the destiny of the union. EU cannotachieve being a single currency area. The existence of single currency requires thecommitment of the Law of one price that means the full monetary integration. Therefore, fullmonetary integration means a strong convergence. In that sense, the coefficient ofconvergence parameter should be less than or equal to -1, which reflects a strongconvergence.

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REFERENCES

Agenor, P. R. (2003). Benefits and Costs of International Financial Integration: Theory andFacts,Blackwell Publishing, Malden, MA, USA.

Badinger, H. Mller, W. G. and Tondl, G. (2004). Regional Convergence in the European

Union, 19851999: A Spatial Dynamic Panel Analysis, Regional Studies, 38(3): 241253.Baele, L. et al. (2004). Measuring Financial Integration in the Euro Area, European Central

Bank Occasional Paper Series, No: 14.Barro, R. J. (1990). Government Spending in a Simple Model of Endogenous Growth,

Journal of Political Economy, 98(5): 103-125.Barro, R. J. (1991). Economic Growth in a Cross Section of Countries, Quarterly Journal of

Economics, 106(2): 407-443.Barro, R. J. and Sala-i-Martin, X. (1991). Convergence Across States And Regions,

Brookings Papers Economic Activity, 1:107-182.Barro, R. J. and Sala-i-Martin, X. (1992). Convergence, Journal of Political Economy,

100(2): 223-251.Barro, R. J. and Sala-i-Martin, X. (1995). Economic Growth, New York, McGraw-Hill.Baumol, W. (1986). Productivity Growth, Convergence and Welfare: What the Long-Run

Data Show,American Economic Review, 76: 1072-1085.Bloom, D. E., Canning, D. and Sevilla, J. (2002). Technological Diffusion, Conditional

Convergence, and Economic Growth,NBER Working Paper, No 8713.Borys, M. M., Polgar, E. K. and Zlate, A. (2008). Real convergence and the determinants of

growth in EU candidate and potential candidate countries a panel data approach,European Central Bank Occasional Paper Series, No: 86.

Caselli, F., Esquivel, G. and Lefoirt, F. (1996). Reopening the Convergence Debate: A NewLook at Cross-Country Growth Empirics,Journal of Economic Growth, 1: 363-389.

De Long, J. B. (1998) Productivity Growth, Convergence and Welfare: Comment, AmericanEconomic Review, 78: 1138-1153.

Dowrick, S. and Rogers, M. (2002) Classical and Technological convergence: beyond theSolow-Swan growth model,Journal of Economic Growth, 1:363-389.

Durlauf, S. N., Kourtellos, A. and Tan, C. M. (2005). Empirics of Growth and Development,Discussion Papers Series, Department of Economics, Department of Economics, TuftsUniversity.

Eicher, T. S. and Turnovsky, S. J. (1999). Convergence in a Two-Sector Non-Scale GrowthModel,Journal of Economic Growth, 4: 413428.

European Commission (2000). Catching up and Falling Behind: Economic Convergence in

Europe,Economic Survey of Europe, 1: 155-187.Frmmel, M. and Kruse, R. (2009). Interest rate convergence in the EMS prior to EuropeanMonetary Union, CREATES Research Paper, No.23.

Hansen, L.P. (1982). Large Sample Properties of Generalized Method of MomentsEstimators,Econometrica, 50(4): 1029-1054.

Jones, C. I. (1997). Convergence Revisited,Journal of Economic Growth, 2(2): 131-153.Kenen, P. (1969). The Theory of Optimum Currency Areas: An Eclectic Review, in R.

Mundell and A. Swoboda eds. Monetary Problems of the International Economy, TheUniversity of Chicago Press, Chicago, Pp. 41-60.

Lau, C. K. M. (2009). New Evidences about Convergence Across States, InternationalResearch Journal of Finance and Economics, 27: 7-18.

Lee, K., Pesaran, M. H. and Smith, R. (1997). Growth And Convergence In A Multi-CountryEmpirical Stochastic Solow Model,Journal Of Applied Econometrics, 12: 357-392.

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13/13

Mankiw, N. G., Romer, D. and Weil, D. N. (1992). A Contribution to the Empirics ofEconomic Growth, Quarterly Journal of Economics, 107: 407-437.

McKinnon, R. I. (1963). Optimum Currency Areas, American Economic Review, 53: 717-724.

McQuinn K. and Whelan, K. (2007). Conditional convergence and the dynamics of the

capital-output ratio,Journal of Economic Growth, 12: 159184.Mundell, R. (1961). A theory of optimum currency areas,American Economic Review, 51(4):509-517.

Patterson, B. and Amati, S. (1998). Adjustment to Asymmetric Shocks, Directorate-Generalfor Research Working Paper, Economic Affairs Series no Econ-104, EuropeanParliament, September.

Petrakos, G., Arvanitidis, P. and Pavleas, S. (2007). Determinants of Economic Growth: TheExperts View. Dynamic Regions in a Knowledge Driven Global Economy Lessonsand Policy Implications for the EU (DYNREG) Working Paper, No: 20.

Pritchett, L. (1997). Divergence, Big Time.Journal of Economic Perspectives, 11(3): 317.Sala-i-Martin, X. (1996). The Classical Approach to Convergence Analysis, The Economic

Journal, 106(437): 1019-1036.Sala-i-Martin, X. (1997). I Just Ran Two Million Regressions, American Economic Review,

87(2): 178-183.Saraolu, B. and Doan, N. (2005). European Union Member and Candidate Country

Convergence Analysis, Paper presented at 7th National Econometrics and StatisticsConference, 26-27 May, Istanbul: Istanbul University.

Sassi, M. (2010). OLS and GWR Approaches to Agricultural Convergence in EU-15,International Advances in Economic Research, 16: 96-108.

Solow, R. (1956). A Contribution to the Theory of Economic Growth, Quarterly Journal ofEconomics, 70: 65-94.

Solow, R. M. (1957). Technical Change and the Aggregate Production Function, Review ofEconomics and Statistics, 39: 312320.

Steger, T. M. (2007). On the Mechanics of Economic Convergence, German EconomicReview, 7(3): 317337.

Weber, E. (2006). British Interest Rate Convergence between the US and Europe: ARecursive Cointegration Analysis, SFB 649 Discussion Paper, No.005.

Weeks, M. and Yao, J. Y. (2003). Provincial Conditional Income Convergence in China,19531997: A Panel Data Approach,Econometric Reviews, 22(1): 5977.

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beyzatlar-Çetin-solowian approach to european convergence with gmm

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