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The dynamics of Greek public debt – evidence fromsimultaneous and structural VAR modelsNicholas Apergisa & Arusha Coorayb

a Department of Banking and Financial Management, University of Piraeus, Piraeus, Greeceb School of Economics, University of Wollongong, Wollongong, NSW 2552, AustraliaPublished online: 28 Nov 2014.

To cite this article: Nicholas Apergis & Arusha Cooray (2014): The dynamics of Greek public debt – evidence fromsimultaneous and structural VAR models, Applied Economics, DOI: 10.1080/00036846.2014.985372

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The dynamics of Greek public

debt – evidence from simultaneous

and structural VAR models

Nicholas Apergisa and Arusha Coorayb,*aDepartment of Banking and Financial Management, University ofPiraeus, Piraeus, GreecebSchool of Economics, University of Wollongong, Wollongong, NSW2552, Australia

The goal of the present article was to investigate not only the dynamics ofthe Greek public debt, but also the appropriate measures required forachieving fiscal consolidation. The empirical estimation is carried outusing a macroeconomic data set spanning the period 1980–2008 andboth the three-stage least squares (3SLS) methodological approach on atheoretical model and the structural VARmethodology to perform forecasttests and to calibrate the future paths of the public debt variable up to 2020.The results suggest that only a restrictive fiscal policy that simultaneouslyincreases government revenues and reduces government expenditurecould permit the country to achieve debt sustainability. The results alsosuggest that debt sustainability can be achieved faster when tax revenuepolicies are intensified. The results are expected to have important impli-cations to policymakers for designing effective macroeconomic policy interms of achieving sustainable levels of public debt.

Keywords: primary balance; public debt; structural modelling; Greece

JEL Classification: E62; C51; C30; E27

I. Introduction

Moderate levels of public debt can be good in helpingan economy to smooth consumption through the life-time of individuals and across generations and easecredit constraints faced by firms and individuals

(Cecchetti et al., 2011). High levels of public debt,however, can be damaging for an economy.1 Privateinvestment could be crowded out due to increasedinterest rate payments; private saving may increase inorder to accommodate public dissaving leading tolower aggregate demand; and higher public debt

*Corresponding author. E-mail: Arusha.Cooray@nottingham.edu.myPresent address of Nicholas Apergis is Curtin Business School, Curtin University, Perth, Australia.Present address of Arusha Cooray: NottinghamUniversity Business School, Jalan Broga 43500, Seminyih, Selangor, DarulEhsan, Malaysia.1Cecchetti et al. (2011), investigating the impact of debt levels on economic growth in 18 OECD countries from 1980 to2010, argue that debt levels beyond 85% of GDP are harmful for economic growth.

Applied Economics, 2014http://dx.doi.org/10.1080/00036846.2014.985372

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may come at the cost of higher future taxes. Highlevels of public debt also increase the sensitivity of aneconomy to changes in global market conditions andthe likelihood of defaulting (Cecchetti et al., 2011). Itadditionally places a strain on fiscal authorities inimplementing countercyclical fiscal policy. Debt sus-tainability can be achieved in a number of ways,including higher future taxes (Barro, 1979) and cur-tailing government expenditure, both of which arecontractionary. Another option is unanticipated highinflation, which could reduce the real cost of debtservicing (Reinhart and Rogoff, 2010). These mea-sures, however, are accompanied by costs. Increasingtaxes and cutting down on government spending canlead to a loss of welfare undermining growth.Similarly, reducing the cost of debt through inflationresults in higher interest rate payments (Reinhart andRogoff, 2010).The recent financial crisis saw an escalation in

public debt levels in Greece. The increase in publicdebt to a level of 171% of GDP in 2011 (Eurostat,2012) led to concerns regarding Greece’s fiscal sus-tainability. In an attempt to achieve debt sustainability,the government adopted a number of austerity mea-sures, including public expenditure cuts and highertaxes. The country is currently caught up in a viciouscycle of austerity measures making recovery moredifficult. Critics argue that these measures are counter-productive pushing the country further into recession.Achieving fiscal sustainability within the Eurozone isa complex task due to a commonmonetary policy, butthe absence of a common fiscal policy among mem-bers (Corsetti, 2012). Consequently, a common fiscalconsolidation package that does not take into accountcountry heterogeneity will not have the same outcomefor all Eurozone members. The focus of policymakershereto has been onmanaging systemic risk. An impor-tant implication stemming from these events is that thedynamics of public debt should be analysed on a case-by-case basis. Greece provides for an interesting casein terms of public debt as successive Greek govern-ments embarked on programmes of deficit financingsince 1974 in response to increasing aggregatedemand, the consequences of which have been feltonly now. Public debt has varied around 100% ofGDP since 1993 (Featherstone, 2011). This has beenprimarily due to the high wage expenditures of thepublic sector due to union resistance and high levels oftax evasion, making the country susceptible to crisisevents (Featherstone, 2011). According to the recent

quarterly review report for the Greek economy pub-lished by the Foundation for Economic and IndustrialResearch (IOBE, 2013), the fiscal position of thecountry is on a positive course (i.e. in a sense thatthe primary fiscal deficit is expected to be on thesurplus side by the end of 2013), although the con-tinued tight fiscal policy implementation will putfurther recessionary pressure on the Greek economy.Against this backdrop, the goal of the present

article was to examine the dynamics of the Greekpublic debt and investigate measures required forachieving fiscal consolidation. A macroeconomicmodel based on the work of Favero andMarcellino (2005), Hasko (2007) and Casadioet al. (2012) is employed for this purpose. Theempirical estimation is carried out using first thethree-stage least squares (3SLS) estimation meth-odological approach and, next, for robustness pur-poses, the structural VAR (SVAR) methodology isused (Bernanke, 1986; Blanchard and Watson,1986; Sims, 1986). Although a number of papershave dealt explicitly with the Greek sovereigndebt problem, no study, to the best of our knowl-edge, has provided an empirical presentation ofthis problem since the crisis and documentedempirically prescriptions for reducing the exces-sive Greek primary public deficits and the publicdebt-to-GDP ratio, which is the novelty and thecontribution to the relevant literature of our study.To this end, we apply the SVAR methodology toperform forecast tests and to calibrate the futurepaths of the public debt and primary balancevariables up to 2020. The results suggest that arestrictive fiscal policy that increases governmentrevenues and reduces government expenditurewill permit the country to achieve debt sustain-ability. The results also suggest that debt sustain-ability can be achieved faster when tax revenuepolicies are intensified. The results of this studywill have important implications for designingeffective macroeconomic policy for achievingsustainable levels of debt in Greece.The rest of this article is structured as follows:

Section II discusses the literature, while Section IIIhighlights the main points surrounding the Greeksovereign debt crisis. Section IV presents the model,while Section V describes the data. Section VIpresents the methodology, evaluates the empiricalresults and presents forecasting results. Finally,Section VII concludes.

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II. The Literature

Studies on fiscal adjustment include those by Favero(2002) for the euro area, by Giannitsarou and Scott(2006) for the OECD and by Blanchard and Perotti(2002) for the US, among others. A study related toGreece is that by Featherstone (2011).Blanchard and Perotti (2002) examine the

dynamic effects of shocks in government expendi-tures and taxes on economic activity in the US duringthe post-war period, by using a mixed SVAR meth-odology. The results indicate that positive govern-ment spending shocks have a positive effect onoutput, while positive tax shocks have a negativeeffect on output. Both, increases in taxes and govern-ment spending are found to have a negative effect oninvestment spending. Favero (2002), jointly model-ling the behaviour of monetary and fiscal authoritiesin the euro area, concludes that fiscal stabilizationwas achieved independently of monetary policy.Despite interactions between the two authorities, sta-bilization depends to a great extent on the response offiscal policy to interest rate payments on public debt.Similar conclusions are reached by Giannitsarou andScott (2006) for a group of OECD economies. Theyconclude that the primary surplus has significantexplanatory power for achieving fiscal balance.Evidence for the fiscal balance in predicting inflationis found to be very weak. By investigating the role ofmonetary and fiscal policy in public debt dynamics ina group of OECD nations, Hasko (2007), on thecontrary, finds that these shocks together accountfor approximately half of the forecast error variationin the debt-to-GDP ratio, about 30% is explained byshocks to GDP growth, while inflation shocks playan important role in initiating a public debt problem.By examining fiscal episodes in Denmark, Ireland,Finland and Sweden, Perotti (2011) finds that in allfour countries, the interest rate declined, and wagereductions played an important role in fiscaladjustment.By examining fiscal changes in a group of OECD

countries, Alesina and Ardagna (2010) argue thatlarge fiscal increases are usually accompanied byincreases in expenditure, while large fiscal adjust-ments are accompanied by tax increases. However,they observe a difference between fiscal adjustmentsthat lead to permanent improvements in the fiscalbalance and those that are temporary. By examiningthe role of fiscal policy in severely depressed

economies, DeLong and Summers (2012) arguethat government spending can be self-financingunder these conditions. That is, an increase in taxrevenues will finance the increase in debt servicegiven certain assumptions hold with regard to gov-ernment spending multipliers and hysteresis effects.Moreover, by investigating the role of macroeco-nomic variables including US GDP growth, theprice of oil, EUR/USD exchange rate, EuropeanCentral Bank monetary policy stance and domesticpolicy instruments on the Italian debt-to-GDP ratio,Casadio et al. (2012) argue that external conditionsplay an important role in Italian fiscal consolidation.In contrast to the VAR methodology employed bymost studies, they employ the seemingly unrelatedregression (SUR) estimation method.Given the inconclusive results with regard to the

influence of monetary and fiscal authorities onmacroeconomic variables, a re-examination of thisissue is particularly relevant in the context of Greece,which was on the verge of economic collapse follow-ing the financial crisis. Featherstone (2011) providesa number of reasons for the Greek crisis issue,emphasizing issues of low competitiveness, tradeand investment imbalances as well as fiscal misman-agement eventually resulted in the debt crisis event.Our study deviates from the limited literature on

public debt issues in Greece in a way that we not onlyestimate, but also carry out forecasting tests for thefuture path of public debt and the primary balance inGreece.

III. The Greek Sovereign Debt Crisis

The sovereign debt crisis in Greece has been anissue in world financial markets since the end of2009. In May 2010, Greece became the first euroarea country requesting a financial rescue from theEuropean Union (EU) and International MonetaryFund (IMF). Most importantly, the Greece crisis hasspread to other peripheral countries, namely Irelandand Portugal, which have also requested financialassistance from the EU and IMF in November 2010and April 2011, respectively (while Ireland exitedthe rescue programme in December 2013). Thissovereign debt problem has become a significantrisk to the world financial market and economicrecovery.

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The EU has called on Greece to implement aus-terity measures in order to reduce debt and cutspending. What continues to worry the markets,however, is a fear of a Greek ‘disorderly default’and the domino effect that may occur within theEurozone. The biggest fear of Greece defaultingon its debt is not the default itself, but the implica-tions within the Eurozone and worldwide. OtherEurozone member nations facing severe austeritymeasures and large debt repayments may look atGreece as an example of an alternative to payingoff debt by choosing to stop all payments and gointo default. In this scenario, countries exposed tothe debts of other Eurozone member nations wouldsee their debt ratings sink almost overnight, as thedomino effect of multiple Eurozone member nationdefaults spread throughout the region. The value ofthe euro would plummet against international cur-rencies, and fear of a complete Eurozone collapsewould plague international markets. A Greekdefault on its debt would lead to a complete lock-up of the Greek economy, which could lead to acollapse and freeze of the Greek banking system,which holds billions of euros of Greek governmentbond debt. The financial chaos that would ensuewould trigger an estimated $5 billion in creditdefault swaps that investors in Greek bonds tookout to protect themselves from a default.The sovereign debt crisis in Greece is primarily

caused by four factors: the first is the lack of fiscaldiscipline due to populist welfare policies. The sec-ond factor is the mismatch between government rev-enues and expenditures. Greece’s public spendingexcluding interest payments accounted for 32% ofGDP, while public revenues excluding social securitycontributions amounted to only 20%. This discre-pancy is due partly to inefficient tax collection, aswell as tax evasion particularly among high-incomebracket individuals, resulting in ongoing fiscal defi-cits. Third, Greece’s competitiveness is decliningrelative to its peers in the EU. A measure ofGreece’s competitiveness has decreased by morethan 25% since becoming an EU member in 2000.Finally, during the global financial crisis in 2008, theGreek government spent extensively to stimulate theeconomy, contributing to the large fiscal burden andloss of investor confidence in the government’s debtrepayment ability.

IV. A Macroeconomic Model

We follow the approach of Favero and Marcellino(2005), Hasko (2007) and Casadio et al. (2012) inspecifying a small macroeconomicmodel for Greece.We start off with the evolution of public debt:

Bt ¼ Bt�1 þ LRt:Bt�1 � PBt (1)

where Bt ¼ nominal general government debt at theend of year t, LR ¼ the long-term nominal interestrate, PB ¼ the primary balance that is equal to taxrevenue less government expenditure (T−G), net ofthe interest paid on debt.2 The budget constraint isusually expressed in terms of the growth of publicdebt-to-GDP ratio (DEBT), as a function of the dif-ference between the real interest rate and outputgrowth rate, and the ratio of the primary balance toGDP (BL) (see Hasko, 2007):

ΔDEBTt ¼ LRt � πt �ΔYtð Þ DEBTt�1�BLt (2)

where π ¼ inflation rate, ΔY ¼ real GDP growth.Equation 2, which is also a debt dynamics equation,suggests that sustained GDP growth and low realinterest rates are important for controlling the growthof public debt. This equation also shows that theprimary balance of the government is an importantdeterminant of government public debt.Equation 2 can be used in empirical estimation as a

single residual equation, by assuming various states forthe primary balance, growth, inflation and interest rate,in determining debt-to-GDP dynamics, or as an equa-tion in a VAR framework taking into account theinterdependence between these variables (Casadioet al., 2012). Here, we follow the approach of Favero(2002), Favero and Marcellino (2005), Hasko (2007)and Casadio et al. (2012) and estimate a simultaneousequations model. Our model comprises five equations:

ΔYt ¼ α1 þ α2ΔYt�1 þ α3Δ LRt�1 � πt�1ð Þþ α4ΔGt�1 þ α5ΔTt�1 þ α6ΔYGERt

þ α7ΔYUSt þ εtΔY (3)

(Output equation)

2Note the same relation would hold if the variables are measured in real terms provided that the rate of inflation is measuredusing the GDP deflator (Casadio et al., 2012).

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Δ T � Gð Þt ¼ α8 þ α9Δ T � Gð Þt�1

þ α10ΔDEBTt�1 þ α11ΔYtþ εt

ðT�GÞ (4)

(Fiscal rule)

ΔDEBTt ¼ α12 þ α13ΔDEBTt�1 þ α14ΔYtþ α15ΔGt�1 þ α16ΔTt�1

þ α17πt�1 þ α18ΔLRt�1

þ εtDEBT (5)

(Public debt equation)

πt ¼ α19 þ α20πt�1 þ α21ΔYt�1

þ α22ΔPOILt þ εtπ (6)

(Inflation equation)

ΔLRt ¼ α23 þ α24ΔLRt�1 þ α25πt�1

þ α26ΔYt�1 þ α27ΔEt�1 þ εtLR (7)

(Interest rate equation)

Equation 3 is an IS curve which also incorporatesthe international business cycle and primary balance(Hasko, 2007). ΔYUS captures US output growth(see Favero and Marcellino, 2005), and ΔYGER,German output growth. US output growth is incor-porated to capture the global economy, whileGerman output growth to capture the Europeangrowth factor (as Germany is Greece’s main tradingpartner, Dees et al., 2010). As growth in the globaleconomy and growth in the German economy wouldlead to growth in Greece, we expect the coefficients,α6 > 0 and α7 > 0. The two components of the primarybalance are also allowed to affect growth. The coeffi-cient on government spending (G) is expected to bepositive, while that of government revenues (T) isexpected to be negative. The lower the real rate ofinterest, the higher would be the borrowing leading tohigher growth. Therefore, we would expect α3 < 0.The primary balance (Equation 4) is a function of

the past periods primary balance to account for delayedeffects of fiscal policy (Favero and Marcellino, 2005).

Equation 4 describes the evolution of a fiscal rule as astock-flow adjustment mechanism following the con-cept of the stability and growth pact (SGP) approach.The SGP is a fiscal rule with a particular focus onbudget deficits. It requires the deficit to stay below aparticular threshold reference value and to have abalanced budget in the medium term. An obviousway to reduce deficits without improving the intertem-poral fiscal position is to shift deficits from the budgetto stock-flow adjustments. This allows us to keep theoverall ‘true’ deficit constant, while window dressingthe reported deficit, to which the SGP applies (Milesi-Ferretti, 2004). Following Bohn (1998), growth inoutput and debt-to-GDP ratio are incorporated asright-hand-side variables. The primary balance is apositive function of output (α11 > 0) and the debt-to-GDP ratio (α10 > 0). We expect the primary balance tobe an increasing function of the debt-to-GDP ratio aswe would expect a government to raise more revenueto support higher levels of public debt. This argumentis maintained by Bohn (1998) who shows that a posi-tive response of the primary budget balance to changesin the debt–income ratio indicates that the governmentis taking remedial action to raise revenue to offset theeffects of rising debt. Similarly, Abbas et al. (2014)argue that high levels of public debt reduce the abilityof a government to implement corrective policies byincreasing the primary surplus to stabilize the debt-to-GDP ratio following a negative shock to growth.The public debt Equation 5 is a function of the past

value of the debt-to-GDP ratio, growth, inflation,government spending, government revenues andthe interest rate. Lagged levels of public debt areincluded to account for delayed impacts of debt oncurrent levels (Favero and Marcellino, 2005). Ascyclical variations in output influence the debt-to-GDP ratio, we include output (Bohn, 1998; Hasko,2007; Casadio et al., 2012). The inclusion of inflationand the interest rate in the debt equation is supportedby Faini (2006) and Hasko (2007). We expect:α14 < 0, α15 > 0 and α16 < 0. As higher inflationcould lead to lower debt servicing costs (Reinhartand Rogoff, 2010), we expect α17 < 0. Higher interestrates lead to a higher debt burden (Hnatkovska et al.,2008); therefore, α18 > 0.Equation 6 is a Phillips curve equation, and infla-

tion is a positive function of output, α21 > 0, and theoil price, α22 > 0 (see, Blanchard and Gali, 2005;Casadio et al., 2012).

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Finally, Equation 7 is a backward-looking Taylorrule (see Hasko, 2007) where the interest rateresponds to inflation (α25 > 0) and output (α26 > 0).As changes in the exchange rate (E) also influencethe interest rate, we include the exchange rate (seeCasadio et al., 2012). E is defined as the euro to USdollar exchange rate. We expect this coefficient, α27,to be positive.

V. Data

Quarterly data on real (at constant 2000 prices) GDP(Y); gross public debt as a percentage of GDP(DEBT); the long-term nominal interest rate, mea-sured as the yield on 10-year government bonds(LR); consumer prices, measured as the CPI (P);world OPs, measured as West Texas Intermediate(WTI) crude oil spot prices in dollars (POIL); thenominal exchange rate between the euro and thedollar (E) (over the pre-euro period, the exchangerate is defined as the ECU–dollar association); realGDP for both the US and Germany (YUS andYGER); government expenses, measured as a per-centage of GDP (G); and government revenues,measured as a percentage of GDP (T), are used.All data are obtained from the InternationalFinancial Statistics database (1980–1998) andfrom the Eurostat database (1999–2012) spanningthe period 1980–2012. For the empirical purposesof the study, we also built the long-run real interestrate (LRR) as the difference between nominal inter-est rates and inflation, while inflation (π) was mea-sured as logarithmic difference of the CPI.

VI. Methodology and Empirical Results

Methodology

We use both the 3SLS and SVAR estimation meth-ods. The advantages of using 3SLS over more con-ventional maximum likelihood (ML) methodsinclude: (1) It does not require any distributionalassumptions for independent variables, that is, theycan be nonnormal, binary, etc.; (2) in the context of amultiequation nonrecursive system of equations, itisolates specification errors to single equations; (3) itis computationally simple and does not require the

use of numerical optimization algorithms; (4) iteasily caters for interactions effects; (5) it permitsthe routine use of often-ignored diagnostic testingprocedures for problems such as heteroscedasticityand specification errors; and (6) it performs better insmall samples than ML approaches.In terms of the SVAR modelling approach, in

addition to robustness ends, this methodology canremedy a number of deficiencies related to 3SLS,such as it generates efficient estimates, especially,in large samples, while 3SLS estimates dependupon the choice of reference variable, implying thatdifferent 3SLS estimates are obtained given differentscaling variables. Therefore, the methodology of theSVAR approach offers an attractive approach to esti-mation. It does not only take explicitly into consid-eration the theoretical constraints imposed on theeconometric models, but also it promises to coaxinteresting patterns from the data that will prevailacross a set of incompletely specified dynamic eco-nomic models with a minimum of identifyingassumptions. Moreover, SVARs are easy to estimateand contribute to the understanding of aggregatefluctuations. By having clarified the importance ofdifferent economic shocks, they have managed togenerate fruitful debates across different views inthe macroeconomic thought.

Integration analysis

We test for unit root nonstationarity by using the testsproposed by Dickey and Fuller (1981). In particular,the analysis is based on the augmented Dickey–Fuller (ADF) unit root tests, the results of which arepresented in Table 2. Using a 5% significance level,those data clearly cannot reject the hypothesis of aunit root for all series in levels. When first differenceswere used, unit root nonstationarity was rejected.However, the power of the statistical unit root test is

of critical importance. Therefore, two modified DFtests with good power are also applied. They are theDF-weighted symmetric (WS) test, proposed by Parkand Fuller (1995), which makes use of the WSLSestimator, which is more efficient than the OLS esti-mator in estimating autoregressive parameters and theDF-generalized least squares (GLS) test, proposed byElliot et al. (1996), which analyses the sequence ofNeyman–Pearson tests of the null hypothesis of thepresence of a unit root. The results are also reported inTable 2. They indicate that all the variables are inte-grated of order one.

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Finally, in order to detect any number and the datesof potential structural breaks, we recently employed adeveloped impulse indicator saturation technique(Hendry et al., 2008; Johansen and Nielsen, 2009;Hendry and Santos, 2010). To analyse the propertiesof the econometric model, this method uses zero–oneimpulse indicator dummies. Since there are poten-tially T such dummy variables, inclusion of all ofthem in a model is not feasible. The impulse indicatordummies, however, can be included in a model asseparate blocks. In the simplest case with two blocks,the sample is split into two equal parts (T/2), then theimpulse indicator dummies are included only for thefirst half of the sample, and statistically significantdummies at a chosen significant level are stored.Further, chosen in the previous step, the impulseindicator dummies are dropped and another part ofthe dummies are included in the model. After that,the procedure is repeated for the second part of thesample. Statistically significant impulse indicatordummies from two blocks are combined, and jointlysignificant ones are retained. A computational algo-rithm, utilized in the OxMetrics software, performsoptimal splitting and selection of the final model forany number of blocks.The results recommend the presence of one struc-

tural break (two different regimes) in the dynamics ofthe variables under study. The specific date of thestructural break has been obtained by impulse indi-cator saturation break test and indicates the 2000Q4–2001Q1 date that occurs due to the participation ofthe country to the European Monetary Union (EMU)and the following changes of monetary policy. Sincethe above break points have a clear-cut economicinterpretation, the inclusion of the appropriatedummy, taking into account the impact of such abreak in a unit root test, is not just a ‘fitting’ of theregression; it is based on a solid economic ground. Itis also important that the break point is chosen endo-genously within the impulse indicator saturationbreak test.The step dummy is then included in the univariate

Dickey–Fuller unit root test, it is considered as anadditional variable when determining the appropriatecritical values, and critical values are determined onthe basis of Ericsson and MacKinnon (2002). Theresults are also reported in Table 1 and providefurther support to the presence of a unit root in thelevels of the variables under study and to the absenceof a unit root in their first differences.

Estimating Equations 3–7 – A simultaneoussystem of equations

In the first step of the empirical analysis, the systemof Equations 3–7 is estimated as a system of equationusing 3SLS, which deals with the potential endo-geneity problem; based on our unit root tests, allvariables are in first differences, while the dummyvariable (DUMEMU) has been included to capturethe country’s participation in the EMU. We also per-form the Gregory and Hansen (1996) test that allowsfor an endogenous structural break in the estimatedrelationship. The regime shift (C/S) model ofGregory and Hansen (1996) captures the change inboth the intercept and slope. For instance, in terms ofEquation 6, we get:

πt ¼ α19 þ α20πt�1 þ α21Dt þ α22ΔYt�1

þ α23ΔYt�1Dt þ α24POILtþ α25POILt þ a26POILtDt (8)

where Dt is a dummy variable indicating the time ofthe regime shift, α22 and α24 are the slope coefficientsbefore the regime shift, and α23 and α26 are thechanges in the slope terms due to the shift. Thesame methodology holds for the remaining equationsas well. The null hypothesis of Gregory and Hansentests that the residuals contain a unit root and hencethere is no estimation with a break, whereas thealternative hypothesis is that the residuals are station-ary and hence there is a relationship between thevariables. Gregory and Hansen (1996) proposethree test statistics to examine the null hypothesis.These statistics are the ADF test statistic and exten-sions of the Z-test statistics (Zt and Zα). Table 2reports the results of the estimation of the system ofEquations 3–7. The empirical findings point out thatall coefficients have the expected theoretical sign as itwas explained above in terms of the theoreticalmodel, while they are statistically significant. Tocheck the statistical validity of the model, we alsoperformed a couple of diagnostic tests. In particular,LM and RESET are tests for serial correlation andmodel functional misspecification, respectively,while figures in brackets denote p-values. Finally,the Gregory and Hansen results point to the rejectionof the null hypothesis (the absence of a break), andthe majority of them confirm the presence of a regimeshift associated with the launching of the Eurozone.

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Table 1. Unit root tests

Levels First differences

Without trend With trend Without trend With trend

ADF testsY −1.29(3) −1.73(3) −6.57(2)* −6.62(1)*DEBT −1.42(3) −1.89(3) −5.23(2)* −5.83(1)*LRR −1.45(3) −1.81(3) −5.51(2)* −6.76(2)*LR −1.32(4) −1.96(3) −5.46(3)* −5.69(2)*P −1.37(3) −1.85(3) −5.73(1)* −5.89(2)*E −1.18(4) −1.74(3) −6.84(2)* −7.64(1)*G −1.21(3) −1.97(3) −5.89(2)* −6.47(2)*T −1.34(3) −1.86(3) −6.18(1)* −6.59(2)*YUS −1.46(4) −1.76(3) −5.52(3)* −5.46(2)*YGER −1.34(3) −1.94(3) −4.96(2)* −5.62(2)*POIL −1.52(3) −2.05(3) −5.38(2)* −5.69(2)*

DF-WS testLevels trend First differences trend

Y −2.26(3) −4.96(1)*DEBT −1.25(3) −4.72(2)*LRR −2.94(3) −4.45(2)*LR −2.13(3) −4.71(1)*P −1.93(4) −4.68(3)*E −2.77(3) −4.83(1)*G −2.68(3) −5.25(2)*T −2.69(3) −5.61(1)*YUS −2.81(3) −4.95(2)*YGER −2.42(3) −5.07(2)*POIL −2.78(3) −6.99(2)*

DF-GLSLevels First differences

Y −2.42(3) −4.96(2)*DEBT −1.64(3) −4.83(2)*LRR −2.48(3) −4.89(2)*P −1.71(4) −4.91(3)*SR −2.37(4) −4.78(2)*G −2.55(3) −5.25(2)*T −2.39(2) −4.90(1)*YUS −2.64(4) −4.85(2)*YGER −2.72(2) −5.32(1)*POIL −2.43(3) −6.41(2)*

ADF test with breakLevels trend First differences trend

Y −2.58(3) −5.16(2)*DEBT −1.63(3) −4.84(2)*LRR −2.82(3) −4.91(1)*LR −2.24(3) −4.90(2)*P −1.81(3) −5.41(1)*E −2.52(3) −5.38(1)*G −2.76(3) −5.69(2)*T −2.31(2) −5.40(1)*YUS −2.62(3) −5.28(2)*YGER −2.25(3) −5.63(2)*POIL −2.39(2) −7.31(1)*

Notes: Numbers in parentheses denote the optimal number of lags used in the augmentation of the test regression and wereobtained through the Akaike information criterion.*indicates that the unit root null hypothesis is rejected at the 5% level.

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Table 2. Estimations of Equations 3–7

Variables Equation 3 Equation 4 Equation 5 Equation 6 Equation 7

Constant 1.369 0.254 −0.649 0.573 0.309(0.36) (0.58) (−0.53) (0.72) (0.46)

ΔΥ(−1) 0.614 0.615 0.219(5.15)* (5.83)* (5.72)*

ΔLRR(−1) 0.092(5.68)*

ΔG 0.508 0.449(5.74)* (5.84)*

ΔT −0.623 −0.582(−6.51)* (6.61)*

Δ[T−G](−1) 0.682(5.14)*

ΔYUS 0.274(5.82)*

ΔYGER 0.452(6.89)*

ΔDEBT(−1) 0.396 0.674(6.70)* (5.59)*

ΔΥ 0.614 −0.271(5.89)* (−5.37)*

π(−1) −0.079 0.744 0.236(−4.68)* (6.39)* (6.30)*

ΔLR(−1) 0.691 0.741(5.80)* (5.03)*

ΔPOIL 0.551(7.26)*

ΔE(−1) 0.072(5.69)*

DUMEMU 0.225 0.125 0.231 −0.293 −0.258(4.91)* (5.63)* (4.57)* (−5.38)* (−5.42)*

DiagnosticsNo. of observations 132 132 132 132 132R2-adjusted 0.76 0.68 0.77 0.73 0.76LM [0.24] [0.28] [0.34] [0.35] [0.43]RESET [0.30] [0.49] [0.41] [0.29] [0.27]ADF* −8.58 −8.14 −7.69 −7.16 −7.64

[0.00] [0.00] [0.00] [0.00] [0.00]2000:1 2000:1 2000:2 2000:1 2000:2

Zt −125.39 −129.34 −136.29 −132.61 −124.63[0.00] [0.00] [0.00] [0.00] [0.00]

2000:1 2000:1 2000:2 2000:1 2000:1Zα −16.72 −18.46 −22.37 −26.52 −16.85

[0.00] [0.00] [0.00] [0.00] [0.00]2000:1 2000:1 2000:2 2000:1 2000:1

Notes: Figures in parentheses denote t-statistics, while probability values are in square brackets. LM isa serial correlation test and RESET is a functional misspecification test. ADF*, Zt and Zα are the threeGregory and Hansen (1996) tests. The critical value of Gregory and Hansen at 1% is −5.13. Thefollowing instruments were used: for Equation 3 = lagged values for ΔLLR, ΔG, ΔΤ, ΔYUS, ΔYGERand lags 3 and 4 for ΔY; for Equation 4 = lagged values of ΔG, ΔT and ΔDEBT, 3 lags for ΔY; forEquation 5 = 2 lags for ΔG, ΔΤ, ΔDEBT and ΔLR, 3 lags for ΔY and π; for Equation 6 = 2 lags for ΔYand 2 lags for π and ΔPOIL; and for Equation 7 = 2 lags for ΔY and π, 3 lags for ΔLR and ΔE.*indicates statistical significance at 1% level.

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We focus on the two equations of interest,Equations 4 and 5. The results in Equation 4 showthat the primary balance shows high inertia, forexample 0.682, confirming the presence of delayedeffects in terms of fiscal policy. In addition, anincrease of 1% of the debt-to-GDP ratio and in outputgrowth leads to 0.396% and 0.614%, respectively,increases in the primary balance. In terms ofEquation 5, a 1% increase in government revenues,output growth and inflation leads to 0.582%, 0.271%and 0.079% falls in the public debt-to-GDP ratio,respectively. By contrast, a 1% increase in govern-ment expenses and the long-term interest rate leads toa 0.449% and 0.691%, respectively, increase in thepublic debt-to-GDP ratio. Finally, the public debt-to-GDP ratio also displays high inertia, for example0.674, confirming that higher long-term interestrates tend to worsen the debt burden of the country.

Estimating Equations 3 through 7 – a SVAR model

Alternatively to the methodology of a simultaneoussystem of equations and for robustness ends, theEquations 3 through 7 are estimated using the meth-odological approach of the SVAR model. This parti-cular approach assumes that the structure of ourmodel is described by a structural form equation,ignoring constant terms. There are several ways ofspecifying the restrictions to achieve identification ofthe structural parameters. A general method forimposing restrictions was suggested by Bernanke(1986), Blanchard and Watson (1986) and Sims(1986) that gives restrictions on only contempora-neous (long-run) structural parameters. This methodpermits nonrecursive structures and the specificationof restrictions based on prior theoretical and empiri-cal information about public-sector behaviour andpolicy reaction functions, such as Equation 7 in ourmodel specification. These structural restrictions aresummarized as:

uΔPOIL ¼ vΔPOIL

uΔE ¼ vΔE

uΔLRR ¼ vΔLRR

uΔYUS ¼ vΔYUS

uΔYGER ¼ vΔYGER

Block of structural restrictions

uΔLR ¼ f1uπ þ f2u

ΔY þ f3uΔE þ vΔLR

uπ ¼ d1uΔY þ d2u

ΔPOIL þ vπ

uΔDEBT ¼ c1uΔY þ c2u

ΔG þ c3uΔT þ c4u

π þ c5uΔLR

þ vΔDEBT

uΔðT�GÞ ¼ b1uΔDEBT þ b2u

ΔY þ vΔðT�GÞ

uΔY ¼ a1uΔLRR þ a2u

ΔG þ a3uΔT þ a4u

ΔYUS

þ a5uΔYGER þ vΔY

The ‘exogeneity restrictions’ block indicates that thevariables included in this are determined exogen-ously and affected only by their own exogenousshocks. The restricted model is estimated with theassistance of the Bernanke (1986) restriction matrix,which associates the residuals from the underlyingunrestricted VAR model with the structural shocks.The results are reported in Table 3.The empirical findings contain the Eurozone

dummy, while the estimated coefficients of the struc-tural identification, that is, the structural equationsthat belong to the block of structural restrictions ofTable 3, are summarized as follows: we focus, again,on the two equations of interest, Equations 4 and 5.The results in Equation 4 – the primary balanceequation – show that an increase of 1% of the debt-to-GDP ratio and in output growth leads to 0.396%and 0.653%, respectively, increases in the primarybalance. In terms of Equation 5 – the debt-to-GDPratio equation – an 1% increase in government rev-enues, the growth rate and inflation leads to 0.267%,0.338% and 0.084%, respectively, declines in thepublic debt-to-GDP ratio. By contrast, an 1%increase in government expenses and the long-terminterest rate leads to 0.475% and 0.653%, respec-tively, increases in the debt-to-GDP ratio. Onceagain, Gregory and Hansen’s results confirm thepresence of a regime shift associated with the launch-ing of the Eurozone.

Forecasting comparisons of the fiscal equationsacross the two models

In this part of the study, we perform forecasting tests tocheck the forecasting capacity of the two alternativemodels. In particular, we perform an out-of-sampleforecasting exercise, using a rolling regression meth-odology. That is, the model is first estimated using

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data up until the first forecasting period. The forecastsare generated at one, two, three and four quarters. Inthe next step, the estimation period is rolled forwardby one quarter, keeping the total length of the estima-tion period fixed. New forecasts are then generated atone, two, three and four quarters. In the end, thesquares of the forecast errors at the different horizonsare averaged using the root mean square error(RMSE), the mean absolute error (MAE) and theTheil inequality coefficient (THEIL).More specifically, the estimation period goes from

1980Q1 to 2008Q4, while the forecast period goesfrom 2009Q1 to 2012Q4. We, then, compare the out-of-sample forecasted values with the actual values. Inorder to assess the forecasting performance, we haveto analyse the forecast accuracy through a set ofstatistical measures, while the forecasting exercisewill take place in terms of Equations 4 and 5, thatis, primary balance and public debt. The empiricalfindings, reported in Table 4, show that the forecast-ing performance in both equations deteriorates, as weextend the forecasting horizon from one to fourquarters ahead. The evidence using all three alterna-tive metrics is reported in Table 4 and suggests thatthe SVAR model performs better than the estimationsthrough the 3SLS model at forecasting both fiscalvariables and at all horizons.

A calibration exercise with the SVAR model

Based on the forecasting superiority of the SVARmodel, in this subsection, we are making use of it tocalibrate the future path (over the period 2013–2020)

of the relevant public debt and primary deficit fiscalvariables, under the implementation of an austerityprogramme imposed by the ‘Troika’ (IMF, theEuropean Central Bank (ECB) and the EuropeanCommission (EC)) which the country strictly follows.To the end of the calibration exercise, we let the modelto evolve over time with the new predictions feedingin the variables under consideration.We are employing two alternative scenarios.

According to the first, taxes are allowed to keepincreasing by 1% throughout the period under con-sideration, while the second assumes that taxes areallowed to keep decreasing by 1% throughout thesame period. In both scenarios, government expenses

Table 4. Forecasting metrics

RMSE MAE THEIL

3SLS SVAR 3SLS SVAR 3SLS SVAR

Equation 41 9.447 6.637 7.652 6.547 0.475 0.3622 9.562 6.825 7.859 6.816 0.497 0.3953 9.835 7.275 8.247 7.236 0.523 0.4284 10.546 7.409 8.840 7.903 0.549 0.452

Equation 51 6.237 4.186 5.227 3.522 0.146 0.1252 6.699 4.437 5.508 3.847 0.162 0.1583 6.892 4.661 5.782 4.136 0.194 0.1844 7.124 4.952 5.958 4.573 0.203 0.226

Note: RMSE, MAE and THEIL denote the root meansquare error, the mean absolute error and the Theilinequality coefficient, respectively.

Table 3. Structural restrictions

uΔLR ¼ 0:273uπ þ 0:152uΔY þ 0:0427uΔE�0:163DUMEMU

uπ ¼ 0:485uΔY þ 0:566uΔPOIL � 0:187DUMEMU

uΔDEBT ¼ � 0:338uΔY þ 0:475uΔG � 0:267uΔT � 0:084uπ þ 0:653uΔLR þ 0:175DUMEMU

uΔðT�GÞ ¼ 0:396uΔDEBT þ 0:653uΔY þ 0:203DUMEMU

uΔY ¼ 0:109uΔLRR þ 0:441uG � 0:575uΔT þ 0:298uΔYUS þ 0:464uΔYGER þ 0:193DUMEMU

ADF* −8.32 −7.69 −7.12 −6.72 −6.62[0.00] [0.00] [0.00] [0.00] [0.00]2000:1 2000:2 2000:2 2000:1 2000:1

Zt −119.46 −119.62 −125.22 −121.59 −118.26[0.00] [0.00] [0.00] [0.00] [0.00]2000:1 2000:1 2000:2 2000:1 2000:2

Zα −14.36 −18.63 −19.48 −23.25 −15.48[0.00] [0.00] [0.00] [0.00] [0.00]2000:1 2000:1 2000:2 2000:1 2000:2

Notes: u denotes residuals from the unrestricted VAR model, while v denotes structural shocks.* indicates statistical significance at 1% level. Probability values are in square brackets.

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are assumed to decrease by 3% over the same periodcalibrated. The results for these fiscal projectionsrelative to the debt-to-GDP ratio are shown inFig. 1, while Fig. 2 reports the real GDP trendsfollowed through the two alternative fiscal scenarios.The findings in Fig. 1 indicate that the debt-to-GDPratio follows a consistent downward path, reaching a97.5% in 2020 only under the hypothesis that taxeswill be following an increasing trend. Therefore, ifthe country follows a consistent fiscal policy thatreduces government expenses and increases taxesthroughout the calibration period, its fiscal profilewould improve, implying that the savings of signifi-cant resources can be spent on more productiveactivities, which will enable the substantial reductionin the debt-to-GDP ratio. Moreover, Fig. 2 highlightsthat under the tax increasing scenario, real outputgrowth will reach 3.8% by 2020. Although the goalof the article has not been to provide a detailedanalysis about the portfolio of taxes that can achievethe simultaneous objective of debt reduction and

economic growth, we can safely argue that countryneeds a substantial and fundamental reform in itstaxation system, which should lead to the introduc-tion of green taxes, like taxes on nonsustainabletourism, pollution and kerosene, as well as a raiseof the top tax rates. Greece should consider a levy onwealth in order to insure the contribution of profitsfrom widespread corruption and tax fraud as well.Needless to say, a brave tax cut on R&D and otherrelated domestic and FDI expenses are also expectedto deliver the maximum outcome, in terms of moreinvestments, higher demand and higher future outputgrowth.

VII. Conclusions

This article investigated the dynamics of the Greekpublic debt, while it also indicated what the appro-priate measures were for achieving fiscal consolida-tion. The empirical estimation was carried out using amacroeconomic data set spanning the period 1980–2013 as well as two econometric methodologicalapproaches, that is, the 3SLS technique on a theore-tical model and the SVAR methodology, to performforecast tests. The estimations were used to calibratethe evolution of the primary fiscal variables, that is,the primary balance and public debt, until 2020. Theempirical findings pointed to the fact that a restrictivefiscal policy that simultaneously increases govern-ment revenues and reduces government expensescould provide good policy guidance to the countryto achieve debt sustainability. Moreover, alternativecalibration scenarios indicated that the correction infiscal imbalances is obtained quicker when the fiscalauthorities intensify their tax revenues policies,which not only implies debt sustainability, but alsothat more emphasis should be given on collecting therequired taxes from the underground economy thatgives room for massive tax evasion activities.The empirical findings are highly substantial for

designing effective macroeconomic policies inGreece and in terms of sustainable levels of publicdebt, given the country’s current position in the cen-tre of the recent European sovereign debt crisis aswell as the three austerity-rescue plans imposed bythe IMF, the ECB and the EC (the ‘Troika’).Reforms up until now have been constrained,

however, by high levels of tax evasion, corruption

174.3182.9

188.6194.1

199.5204.7 208.1 211.4

160.4

142.7

128.9122.5 119.6

112.5105.7

97.5

0

50

100

150

200

250

2013 2014 2015 2016 2017 2018 2019 2020

Debt-decreasing taxes

Debt-increasing taxes

Fig. 1. Projections for public debt (%GDP) undertwo alternative fiscal scenarios

–3.6

–4.9

–5.5–5.9

–6.4–6.8 –7

–7.5

–1.8

0.40.9

1.6

2.22.7

3.1

3.8

–10

–8

–6

–4

–2

0

2

4

6

2013 2014 2015 2016 2017 2018 2019 2020Y-decreasing taxes

Y-increasing taxes

Fig. 2. Projections for real GDP under two alterna-tive fiscal scenarios

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and lack of competitiveness (Featherstone, 2011).According to Featherstone (2011), the EC estimatedthat tax revenue that was not collected in Greece hasbeen up to 3.4% of GDP in 2006. Moreover, thecountry has one of the highest levels of corruptionamong theWestern and Southern European countries(standing at 40 on the transparency internationalindex in 2013 on a scale of 0 (highly corrupted) to100 (very clean)). Finally, high levels of governmentexpenses, due to strong union pressures, have led tothe lack of competitiveness. Therefore, establishingcompetitiveness and fighting corruption are thenecessary tools to reduce government expenses andincrease taxes.Fiscal policies, which are not accompanied by

improvements in these institutional characteristics,will not have the desired effect on stabilizing debtlevels. The country must do more to fight tax evasionwith this ‘notorious’ problem being still a majorissue, with the rich and self-employed ‘simply notpaying their fair share’ as austerity unfairly hitsmostly public-sector workers earning a salary or apension. Deficit cuttings have been achieved primar-ily through cutting jobs and salaries bringing‘unequal distribution of the burden of adjustment’.Governments have to strengthen the independence ofthe tax administration to make it easier to reform thesystem.According to Tagkalakis (2014), high levels of tax

evasion are a significant and relevant channelthrough which real GDP growth impacts on the effi-ciency of the tax system. This requires strengtheningthe tax enforcement mechanism to combat tax eva-sion, while our simulation results indicate that furtherincreasing tax obligations will not necessarily trans-late into increased revenues, given that both indivi-duals and firms face binding financing and liquiditypressures. Moreover, the country’s competitivenessmay be increased by tax incentives to encourage newproduct development, while increasing tax relief oninvestment by reducing the interest rate elasticity ofinvestment. Our calibration scenarios just confirmedthat.Overall, Greece needs to become more economic-

ally competitive at the same time that the countryreduces its debts and deficits. The country mustencourage growth by fostering domestic policyreforms to temporarily lower living standards, whileincreasing employment and productivity levels, par-ticularly in the tradable sector so that Greece can

increase its exports. Even if these domestic reformsare successful, though, they will take time to produceresults (Sapir et al., 2011).

Acknowledgements

The authors wish to thank an anonymous referee andthe participants at the Banking, Finance, Money andInstitutions: The Post Crisis Era Conference, Surrey,for their valuable comments and suggestions on ear-lier drafts of the article. Needless to say, the usualdisclaimer applies.

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