This paper assesses the fiscal sustainability hypothesis for 10 Central and Eastern European countries (CEEC) between 1997 and 2019. The study adopts very recent panel econometric techniques which accounts for issues of structural breaks and crosssectional dependence in the data generating process to examine the cointegration between government revenue and expenditures. Preliminary results show that revenues and expenditures do not have a longrun relationship and hence a rejection of the sustainability hypothesis. As a next step, we discriminate between structural and cyclical components of revenues and expenditures in order to place emphasis on the structural component. We argue that the structural component of fiscal variables represents the actual long term behaviour of the policymaker. Further results indicate that structural revenues and expenditures have a longrun relationship however with a slope coefficient less than unity which implies sustainability in the weaker sense. At that point, expenditures exceed revenues and if this continues for a long time the government may find it difficult to market its debts in the long run. This result suggests that the fiscal authorities in CEEC must therefore do more by taking long term actions to counteract the rising fiscal deficit problems.
Keywords
 Fiscal Sustainability
 Cointegration
 Government Revenue
 Government Expenditure
JEL Classification
 H0
 H6
 E6
The recent financial crises and global economic downturn prompted governments’ Cinterventions across the world by way of fiscal expansions in attempts to stimulate aggregate demand
This principle is based on the Keynesian concept which has dominated the politicaleconomic principles lately. The Keynesian school of thoughts advocates for government intervention to stabilize market economies.
. This has implications on fiscal policy since spending must be financed by public deficits (Greiner & Fincke, 2015). Rising public deficits and debts to unsustainable levels may have longrun implications for the government since holders of government debts (usually the private sector) could lose confidence in government bonds. Secondly, the government could also default on its debts if it reaches unsustainable levels. The need to finance public deficit imposes a constraint on fiscal policy since governments in dynamically efficient economies have borrowing limits and face a present value borrowing constraint.See Abel et al. (1989) or a detailed discussion regarding dynamic efficiency of an economy.
Prior to the accession of the European Union (EU), governments in Central and Eastern European Countries (CEEC) had to institute extensive fiscal policy actions to adjust their budgets and transform structures of revenues and expenditures whilst implementing institutional frameworks for fiscal policy reforms (Gleich, 2003). The objective was to ensure that they meet the necessary fiscal criterion in terms of size of debts, deficits and other obligations as stipulated in the Maastricht Treaty (MT) and Stability and Growth Pact (SGP). Eight CEEC out of the ten countries that joined the EU from the socalled eastern enlargement scheme had lower debt to GDP (Gross Domestic Product) ratios below the 60% threshold required by the MT and SGP; hence, Hallett and Lewis (2007) speculated that these CEEC could follow an explosive debt path for years without necessarily violating the fiscal sustainability requirements. Sixteen years after joining the EU, it remains to be seen if indeed these countries have pursued sustainable fiscal policies.
Most pioneered literature on fiscal sustainability started by empirically testing the stationarity of government debt and deficits (Westerlund and Prohl, 2010) as a way of fulfilling the government budget constraint. Notable among them are Hamilton and Flavin (1986), Trehan and Walsh (1988), Kremers (1988), Wilcox and Walsh (1989) and Baglioni and Cherubini (1993). Later authors such as Hakkio and Rush (1991), Lui and Tanner (1995), Quintos (1995), Ahmed and Rogers (1995) and more recently Afonso (2005) and Westerlund and Prohl (2010), have all zoomed in to specifically consider a more flexible approach of the cointegration between government revenue and expenditures. This is to ascertain if indeed revenues and expenditures have a longrun relationship with a positive cointegration vector, which is a confirmation of the sustainability hypothesis.
Even though a vast stream of empirical studies on fiscal sustainability on the European continent has been undertaken, there exists only a limited number of studies in the context of CEEC (Boekemeier & Stoian, 2018). For instance, Krajewski, Mackiewicz and Szymańska (1993–2016) examined the public debt sustainability for 10 selected CEEC countries using panel stationarity, a cointegration technique and a fiscal reaction function for the period 1990–2012. Their results indicated that the fiscal stance of selected CEEC countries is jointly sustainable. Similarly, Llorca and Redzepagic (2008) assessed the sustainability of fiscal policy for eight CEEC countries using panel cointegration analysis and found out these countries pursued sustainable fiscal policies for period 1999–2006 using quarterly data. Boekemeier and Stoian (2018) also investigated debt sustainability in 10 CEEC countries using estimates of a fiscal reaction function in its cubic form over the period 1998–2015. Their results revealed that government debts were at sustainable levels and that governments had not reached fiscal fatigue thresholds. Even though the studies above employed panel sustainability test for CEEC, none incorporated the possibility of structural breaks and crosssectional dependence in the panel data generating process
The accession of some countries to the EU or Eurozone could represent a structural change in policy due to requirements that must be met and maintained by members of the union, notably requirements enshrined in the socalled MT and SGP.
.The aim of this paper is to ascertain the fiscal sustainability of 10 CEEC for the period 1997–2019 by investigating the longrun relationship between revenues and expenditure using panel cointegration.
These countries are Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia, Bulgaria and Romania.
The study makes use of a panel data analysis in order to benefit from the rich dynamism of panels. The availability of large macroeconomic datasets, over a long period of time and for different economies, is a recipe for a shift in the mean or trend of the individual time series. This increases the probability of break occurrence in the data (CarrioniSilvestre, BarrioCastro & LopezBazo, 2005). In cointegration analysis, structural changes have the tendency of affecting the cointegration vector, which is in contrast to conventional wisdom considering the fact that cointegration is a longrun stable relationship (Westerlund & Edgerton, 2008). This also leads to wrong inferences and hence proves how important it is to account for structural changes in the data generating process (Bai & Perron, 1998; CarrioniSilvestre et al., 2005).Furthermore, cross country macroeconomic and financial datasets are associated with crosssectional dependence because of intercountry links and dependencies, which is more of the rule now than an exception (Westerlund & Edgerton, 2008). Crosssectional dependence affects the size properties of the unit root test and hence renders inferences incredible.
Banerjee et al (2004) argued that unit root test which assumes crosssectional independence suffers from size distortions as the actual size of the test is lower than the empirical size.
Hence, this study adopts the socalled ‘second generational’ econometric procedure which accounts for both crosssectional dependence and structural breaks simultaneously in the data generating process, unlike other previous panel studies such as Beqiraj, Fedeli and Forte (2018), Claeys (2007) and Llorca and Redzepagic (2008).Preliminary results show that revenues and expenditures do not have a longterm relationship and hence indicate a rejection of the sustainability hypothesis. Further, we discriminate between structural and cyclical components of revenues and expenditures in order to place emphasis on the structural component. This is the novelty of this paper when contrasted with previous panel cointegration sustainability studies between revenues and expenditures, such as Westerlund and Prohl (2010), Afonso (2005), Quintos (1995), Prohl and Schneider (2006), Claeys (2007) and Llorca and Redzepagic (2008). With motivations from Galí et al. (2003) who posited that the component of fiscal variables whose variations do not emanate from the influence of cycles represents discretionary fiscal policy, we use fiscal variables adjusted for cyclicallity.. As opined by Blanchard (2006), structural fiscal variables provide a benchmark by which fiscal policy can be judged. We argue that this structural component of fiscal variables (cyclically adjusted variables) represents the actual longterm behaviour of the policymaker and should be examined when conducting sustainability analysis. Further results indicate that the cyclically adjusted revenue and expenditure have a long run relationship. However, the slope coefficient of the cointegration relationship is less than unity and not strong enough to infer sustainability in the strong sense for cyclically adjusted variables
To infer strong sustainability in the sense of Quintos (1995), the cointegration slope must be equal to or greater than unity.
. These results suggest that even though cointegration exists for cyclically adjusted variables, the magnitude of the cointegration slope implies that expenditures are rising faster than revenue, which indicates fiscal deficits. Hence debt to GDP ratio is not bounded and if this continues for a long time, the debt stock will no longer be finite or sustainable, implying a weaker form of sustainability. There is therefore the need for the fiscal authorities in the selected countries to pursue long term actions that counteract rising fiscal deficits by way of fiscal consolidation to ensure the satisfaction of the government IBC. The contribution of the paper is in three folds. Firstly, it employs recent advances in panel econometrics that models structural breaks and crosssectional dependence simultaneously in the data generating process for the sustainability hypothesis test in CEEC. Secondly, the study makes a case for the use of structural fiscal variables which is devoid of automatic response variables in the cointegration analysis for the sustainability hypothesis test. Finally, the study adds to the growing literature on fiscal sustainability in CEEC region.The rest of the study is structured as follows. Section 2 will discuss the methodology used for the paper by laying the theoretical foundations for the sustainability test. The section will further discuss the various econometric procedures used for the test. Section 3 will provide the empirical estimation and discussion of the results. Section 4 concludes the paper.
We begin with the government budget constraint which is assumed to hold at all times. A period government budget constraint in nominal terms is written as
Taking the state of the economy into consideration and assuming
Since further modification is required for empirical estimation, let
Assuming that Eq. (5) holds continuously, then by forward substitution the present value budget constraint can be written as
Sustainability implies that the second term on the RHS of Eq. (6) converges to zero as time approaches infinity. This is also known as the transversality condition, which constraints the debt ratio not to grow at a faster rate than the interest rate
Also known as the no Ponzi scheme, we rule out the possibility of the government issuing new debts in order to fund principal repayment and interest on existing debts.
. If this is the case, then the current stock of debt should be equal to a total of both current and future discounted primary surpluses. As pointed out by Afonso (2005), the absence of no Ponzi condition can be tested empirically by testing the stock of debt for stationarity. Earlier studies that focused on testing the stationarity of public debt include Kremers (1988), Wilcox and Walsh (1989), Trehan and Walsh (1988) and Greiner and Semmler (1999).Additionally, sustainability can be examined by testing the cointegration between revenues and expenditures, an idea initially pioneered by Hakkio and Rush (1991) and later Quintos (1995). Mathematically, this can be shown from Eq. (5), by making use of the auxiliary definition
Testing for the sustainability hypothesis can be done in two ways. First one could test for the absence of the no Ponzi scheme which implies that the second term of Eq. (7) approaches zero as time approaches infinity. Alternatively, we could assume the absence of no Ponzi scheme and test Eq. (7) directly. In this paper, we proceed to test the absence of no Ponzi scheme.
For Eq. (8) to hold, oneperiod government debt (Δ
Furthermore, γ = 1 implies sustainability since from Eq. (10) we infer that debt to GDP ratio is bounded and will grow at a constant rate. However, this condition was relaxed by Hakkio and Rush (1991), who demonstrated that the condition 0 < γ ≤ 1 guarantees sustainability if variables are cointegrated. Quintos (1995) argued further that 0 < γ ≤ 1 is both a necessary and a sufficient condition. She stressed that cointegration is only a sufficient condition for the sustainability hypothesis to hold.
At this point, it is important to make special remarks about the condition 0 < γ < 1. Even though this is enough for sustainability, at this point the government expenditure exceeds its revenue and therefore the probability of default is high. It will be difficult to market its bonds and the government may have to pay high interest rates to issue new debt or attract new investors. Scenario γ > 0 guarantees sustainability since at this point, revenues are growing at a faster pace as compared to expenditures. Conversely at γ < 0, expenditures and revenues are moving in opposite directions and hence sustainability hypothesis is rejected. As shown by Quintos (1995), γ = 1 implies strong sustainability whereas γ < 1 implies some weaker form of sustainability. Therefore, the magnitude and sign of γ plays a major role in determining if, indeed, the sustainability hypothesis holds and the strength of the hypothesis.
Empirical cointegration test for Eq. (9) can be conducted conventionally by regressing
Firstly, we present a review of past empirical papers on fiscal sustainability with focus specifically on panel datasets. Subsequently, we will discuss our datasets and some characteristics of the data, after which we shall proceed with the empirical test of the fiscal sustainability hypothesis. Table 1 shows previous papers on panel data fiscal sustainability for mostly CEEC, Organisation for Economic Corporation and Development (OECD), and EU countries. Regarding CEEC, previous studies, notably by Llorca and Redzepagic (2008), Krajewski et al. (1993–2016) and Boekemeier and Stoian (2018), all point in the direction of a sustainable fiscal policy. It will therefore be interesting to compare our results directly with these studies.
Summary of existing empirical panel fiscal sustainability test
Afonso and Rault (2010)  Stationarity of debt and cointegration between revenue and expenditure  15 Selected EU countries (1970–2006)  Fiscal stance sustainability confirmed 
Baldi and Staehr (2015)  Estimated fiscal reaction function of primary balance, debt and business cycle viables  Different groups of EU countries (2001–2004)  Sustainable fiscal stance for all groups post financial crises 
Beqiraj et al. (2018)  Panel cointegration test between primary balance and public debt  21 OECD countries (1991–2015)  Fiscal stance judged to be unsustainable 
Boekemeier and Stoian (2018)  Fiscal reaction function of primary balance and debt  CEEC (1997–2013)  Fiscal stance sustainable for selected countries 
Brady and Magazzino (2018)  Stationarity of public debt  19 European countries (1970–2016)  Fiscal stance sustainability confirmed 
ChecheritaWesphal and Žďárek (2017)  Fiscal reaction function of primary balance response to debt  18 Euro Area countries (1970–2013)  Sustainable fiscal stance 
Claeys (2007)  Cointegration between revenue, spending and net interest payment  Selected European countries (1970–2001)  Sustainable fiscal policy 
Krajewski et al. (2016)  Cointegration between revenue and expenditure and a fiscal reaction function  CEEC (1990–2012)  Sustainable fiscal stance 
Lee et al. (2018)  Fiscal reaction function of primary balance response to debt  EU regional groups (1950–2014)  Varied results depending on the region 
Llorca and Redzepagic (2008)  Cointegration between revenue and expenditure  CEEC (1999:1–2006:1)  Fiscal stance sustainable in selected countries 
Prohl and Schneider (2006)  Cointegration between budget deficit and public debt  15 EU countries (1970–2004)  Fiscal stance sustainability confirmed 
Westerlund and Prohl (2010)  Cointegration between revenue and expenditure  8 rich OECD countries (1977:1–2006:4)  Sustainability hypothesis confirmed for selected countries 
CEEC, Central and Eastern European Countries; EU, European Union; OECD, Organisation for Economic Corporation and Development.
Regarding our dataset, revenue, expenditure, and debt variables, these were all obtained from the OECD website for 10 CEEC
These countries were chosen based on the availability of quality datasets and length of time series.
. All data exists in annual frequency. The sample period is from 1995 to 2019 and chosen based on the availability of data. A total of 250 observations are generated from a combination of 10 countries over a 25year period. It is important to mention that we consider total expenditures, total revenues, and total debts as ratios of GDP.Figures 1 and 2 provide a graphical overview of revenue and expenditures as well as government debt for each of the countries in the panel. We noticed that in almost all of the cases, revenues and expenditure move in the same direction even though expenditures seem to be higher than revenue for most of the time periods. Poland, Hungary and Romania displayed high variability in the revenueexpenditure relationship. Moreover, the debt to GDP ratios of Hungary and Poland for most of the years exceed revenue and expenditures. For almost all the countries, we notice a rising public debt after 2008 which can be attributed to the activeness of fiscal policy within and after the financial crises. One can infer that since spending exceeded revenue, governments borrowed more to fund their increased spending. Figure 3 shows a scatter plot that reveals the relationship between revenues and expenditure with a smooth trend line. A positive upwardsloping relationship can be observed between the two variables, which provide some hints as to the nature of the relationship between the two fiscal variables.
Figures 1 and 2 also provide some hints about the possibilities of structural breaks in the individual time series. Hence, it is feasible to test for the presence of structural breaks in the data. The presence of structural breaks could render statistical inferences erroneous if not accounted for in the data generating process. For instance, standard unit root tests are likely to exhibit biases towards nonrejection of the null hypothesis, hence leading to a wrong conclusion about results of the test (CarrioniSilvestre, et al., 2005). The issue of structural break has therefore received considerable attention in both theoretical and empirical econometric literature; notable among them includes Andrews, Lee and Ploberger (1996), Andrews (1993) and Bai and Perron (1998) among others. Structural breaks in the mean of data and the changes in the coefficient of a linear regression coincide with political, historical, and economic events (Zeiles et al., 2003) and are therefore not usually a random phenomenon.
To test the availability of structural breaks in the individual series, this study adopts the approach by Zeileis et al (2003). There they combined the Fstatistics test by Andrews (1993) and Andrews and Ploberger (1994) to test the possibility of structural breaks in regression and the technique by Bai and Perron (2003) to locate the break dates and optimal breaks in the individual series of the data
Procedure is implemented in R studios with the package” ‘strucchange’.”. We select the optimal number of breaks by choosing the number of breaks with the least sum of square residuals.
. Table 2 provides results of the break dates for both revenue and expenditures. Regarding revenues, the number of breaks ranges between 1 and 4. We noticed that majority of the breaks were recorded before the early 2000s, which could possibly represent a policy shift as most of the CEEC were preparing to join the EU and therefore had to adjust their fiscal policies in order to meet the demands of the SPG and MT. Secondly, another break can be observed between 2007 and 2011 for most of the countries, which could also be attributed to the exogenous shock and the consequences from the global financial crises. This provides justification for the presence of the shocks and the fact that it must be accounted for in the data generating process.Dates of structural breaks for individual series
Czechia  2002, 2010  2003 
Estonia  1998, 2008, 2011  1999, 2007, 2010 
Hungary  1997, 2006, 2011, 2015  1998, 2001, 2015 
Latvia  1999, 2009  1997, 2000, 2008, 2011 
Lithuania  2000  2000, 2008, 2011 
Poland  1997, 2008, 2015  1997, 2011 
Slovakia  1997, 2000, 2003, 2012  2002, 2008 
Slovenia  2011, 2015  2008, 2015 
Bulgaria  1997, 2008, 2012  1998, 2002 
Romania  1997  1998, 2001, 2006, 2012 
CEEC, Central and Eastern European Countries.
Table 3 below provides a summary statistic of the panel dataset. We notice that there is more variability in expenditures as compared to the revenue components (from the standard deviation) over the sample period. Secondly, on the average, we observe that expenditures are higher than revenues, which is not so surprising since the role of government (spending) has become important especially in the 21st century either to stimulate economic growth or in direct response to macroeconomic shocks.
Panel summary Statistics
Mean  0.391  0.419  0.340 
Standard Deviation  0.039  0.505  0.178 
Maximum  0.482  0.541  0.711 
Minimum  0.308  0.321  0.038 
Observations  250  250  250 
As per the SGP requirements, member states of the EU are required to maintain a strict upper limit of 3% deficit to GDP ratio (Wickens, 2008); hence, we investigate if member countries have followed this rule. Table 3 provides an overview of the deficit to GDP ratio of the CEEC during the sample period. We noticed that, with the exception of Estonia that violated the SGP only once (1999), all other countries violated this rule a couple of times. Firstly, it is observed that this occurred mostly between 1995 and 1998, which is prior to their accession to the EU. Secondly, during the financial crises area between 2008 up till 2012, we also notice another round of SGP violation by all countries with the exception of Estonia. CEEC have therefore run fiscal deficits over the years and have not followed the 3% deficit limit rule strictly (Table 4).
Deficit to GDP ratio
1995  1.05  
1996  
1997  2.15  1.42  0.76  
1998  0.03  1.08  
1999  0.09  
2000  
2001  0.20  1.05  
2002  0.42  
2003  1.82  
2004  2.34  1.80  
2005  1.08  1.00  
2006  2.87  1.81  
2007  2.73  1.10  
2008  1.59  
2009  
2010  0.19  
2011  1.06  
2012  
2013  0.18  
2014  0.70  
2015  0.14  
2016  0.72  0.06  0.23  0.09  
2017  1.56  0.45  1.10  
2018  1.09  0.60  0.77  1.75  
2019  0.75  0.51  0.13  0.72 
Highlights in bold indicates violation of the EU SGP.
EU, European Union; SGP, Stability and Growth Pact.
Next, we test for evidence of crosssectional dependence of the individual units in the panel. We deem it feasible to test for crosssectional dependence, which is peculiar with macro panel data because countries in the same region respond to shocks in the similar ways, thereby generating serially correlated errors which affect inferences from the econometric test. Previous first generational econometric unit root test, such as Levin, Lin and Chu (2002), Im, Pesaran and Shin test (2003) and Maddala and Wu (1999); and cointegration test, notably Pedroni (2000, 2004) and Kao (1999), assume that the crosssection in the panel data is independent. Such The socalled first generational econometric test test usually suffers from size distortions, which affects the inferences (Banerjee, Marcellino & Osbat, 2004). Properly accounting for crosssectional dependence in panels improves the efficiency of parameter estimates and simplifies statistical inferences (Hsiao, 2014). Two main crosssectional dependence tests, namely Breusch–Pagan test and Pesearn test, are carried out in this paper. Proposed by Breusch and Pagan (1980), the test is based on a Lagrangian multiplier (LM), which is applicable to heterogeneous models and other variant panel models. Breusch–Pagan test is very convenient for datasets with short
Table 5 presents results of the Breusch–Pagen and Pesaran crosssectional dependence test. The null hypothesis for both tests indicates cross sectional independence in the panel datasets. In the case of expenditures to GDP ratio, there is a strong rejection of the null hypothesis for both tests. Hence, we accept the alternative hypothesis of crosssectional dependence. For revenue to GDP ratio, there is a strong rejection (1%) for the Breusch–Pagan test and rejection at 10% significance level for the Pesaran test. Result provides evidence of crosscountry dependence in the panel data and hence justifies the need to choose an econometric procedure that accounts for crosssectional dependence.
Breusch–Pagan and Pesaran crosssectional dependence test
Expenditure to GDP ratio  107.84, 0.000  5.494, 0.000 
Revenue to GDP ratio  113.42, 0.000  1.716, 0.0862 
Pooled group variable by country. Null hypothesis of the test implies crosssectional independence for both Breusch–Pagan and Pesaran test.
CD, crosssectional dependence.
From an econometric point of view, it is important to decide if, indeed, data can be pooled or not. According to Baltagi et al. (2008), imposing the pooling restriction reduces the variance of the pooled estimator. However, this could lead to a bias and hence wrong inferences if the restriction is false. Pooling data assumes that the parameters in the model are the same (homogeneous) across the individual countries. Similarly, we can also verify if the parameters are the same or different across the time periods. The decision of whether to pool or not is a natural question which arises in panel studies (Baltagi, 2005). Once the true nature of the parameters is established, it is then feasible to choose an appropriate econometric estimator. Considering an unrestricted model in the regression of the form
From Table 6, if we consider an unrestricted model of a FEwithin model with a time invariant slope, then clearly, we can reject the null of model stability or poolability at 1% significance level. Similarly, if we consider a pooled OLS regression with constant intercept and slope parameter, we can still reject the null hypothesis at 5% level. Hence, there is evidence to back the claim that we cannot pool the slope coefficient across the individual countries from our data. The slope coefficient is therefore heterogeneous across countries and cannot be considered as homogeneous. This provides useful guidance regarding the selection of an appropriate econometric model and procedure suitable for accounting for heterogeneity in the slope coefficient.
Chow test of poolability of coefficients (5% significance level)
FE (within)  Variable  2.8438  0.004  Not poolable 
Pooled OLS  Fixed  14.892  0.000  Not poolable 
Null hypothesis implies ‘model stability’ or constant coefficient.
FE, fixed effect; OLS, ordinary least square estimator.
As part of the cointegration requirement, the variables must be integrated of order 1. In other words, we test if revenues and expenditures are stationary at their first difference (I(1)). In this study, we adopt the Fourier unit root test by Nazlioglu and Karul (2017), which allows for smooth breaks in the mean of the series and crosssectional dependence at the same time. This test is one of the few second generational unit root tests that accounts for both crosssectional dependence and structural breaks. The test is a combination of an earlier test by Becker, Enders and Lee (2006) who employed a Fourier approximation function to model structural breaks and Hadri and Kurozumi (2011, 2012) who used a common factor structure to account for crosssectional dependence. A Fourier approximation can be used to model structural shifts of any form or nonlinearity in the deterministic term as this was shown by Becker et al (2006). It is important to note that the Fourier approximation is used to model breaks or shifts in a smooth gradual process which is in contrast to sharp breaks
Models with sharp breaks cannot be modelled by Fourier approximation. In such instances, dummy variables can be used to capture the sharp breaks. CarrioniSilvestre et al. (2005) developed a panel unit root test which is capable of accommodating sharp breaks in panels by using dummy variables.
. Another distinctive feature of the test is that the breaks are determined endogenously and do not have to be predetermined.The null hypothesis of the test implies ‘stationarity’ against the alternative of a unit root. The test depends on the Fourier frequency (
I would like to thank Saban Nazlioglu for making the Gauss codes available.
. We observe that the null of stationarity for the panel is strongly rejected at 1% significance level irrespective of whether we consider a model with a ‘constant’ or a model with ‘constant and a trend’. For the individual countries, asPanel stationarity test – Expenditure
Czechia  0.070  0.309  0.302  0.050^{*}  0.059  0.050 
Estonia  0.099  0.410^{*}  0.331  0.052^{*}  0.124^{*}  0.125^{*} 
Hungary.  0.203^{**}  0.298  0.201  0.044  0.101  0.091 
Latvia  0.183^{**}  0.504^{**}  0.414^{*}  0.051^{*}  0.080  0.083 
Lithuania  0.052  0.055  0.116  0.051^{*}  0.043  0.051 
Poland  0.182^{**}  0.496^{*}  0.416^{*}  0.053^{*}  0.056  0.076 
Slovakia  0.048  0.219  0.197  0.040  0.145^{**}  0.139^{*} 
Slovenia  0.059  0.189  0.322  0.054^{*}  0.097  0.085 
Bulgaria  0.148^{**}  0.089  0.095  0.051^{*}  0.088  0.087 
Romania  0.124  0.313  0.181  0.053^{*}  0.136^{**}  0.133^{*} 
Panel statistic  2.995^{***}  3.508^{***}  2.282^{***}  4.938^{***}  3.309^{***}  2.460^{***} 
(0.001)  (0.000)  (0.011)  (0.000)  (0.000)  (0.007) 
Fourier panel stationarity test for 10 CEEC under the Null hypothesis of stationarity.
Critical values (obtained from Becker et al. 2006, p. 289) for individual test statistics are as follows: 0.1318 (10%), 0.1720 (5%), 0.2699 (1%) for
Critical values for constant and trend are as follows: 0.0471 (10%), 0.0546 (5%), 0.0716 (1%) for
Significance at 10%, 5% and 1% are denoted by ^{*}, ^{**} and ^{***} respectively.
CEEC, Central and Eastern European Countries.
After establishing that revenue and expenditure are I(1), we estimate the cointegration relationship between the variables. Regarding testing the cointegration relationship between revenues and expenditure, we resort to the test by Westerlund and Edgerton (2008). This test is very appealing because it serves as a onestopshop by accounting for structural breaks and crosssectional dependence in panel data, making it very desirable. Secondly, the test is robust to serial correlation and heteroscedasticity in the residuals. Westerlund and Edgerton (2008) proposed two tests for the null hypothesis of no cointegration. The proposed test is derived from a LM function in the similitude of Schmidt and Phillips (1992), Ahn (1993), and Amsler and Lee (1995) unit rootbased test.
We test the null of ‘no cointegration’ against an alternative hypothesis of ‘cointegration’ between revenue and expenditures. There are two proposed test statistics of the null hypothesis. The first test statistics
Panel stationarity test – Revenue
Czechia  0.146^{*}  0.314  0.408^{*}  0.047  0.048  0.068 
Estonia  0.157^{*}  0.271  0.108  0.044  0.066  0.091 
Hungary.  0.342^{***}  0.187  0.148  0.040  0.107^{*}  0.103 
Latvia  0.070  0.448^{**}  0.313  0.052^{*}  0.074  0.076 
Lithuania  0.074  0.155  0.299  0.051^{*}  0.076  0.054 
Poland  0.170^{*}  0.409^{*}  0.352^{*}  0.065^{**}  0.101  0.103 
Slovakia  0.262^{**}  0.371^{*}  0.362^{*}  0.057^{**}  0.153^{**}  0.143^{**} 
Slovenia  0.056  0.128  0.239  0.056^{*}  0.079  0.074 
Bulgaria  0.268^{**}  0.115  0.121  0.065^{**}  0.114^{*}  0.102 
Romania  0.074  0.151  0.160  0.049^{*}  0.121^{*}  0.116^{*} 
Panel statistic  5.652^{***}  2.716^{***}  2.136^{***}  5.606^{***}  3.405^{***}  2.523^{***} 
(0.000)  (0.003)  (0.016)  (0.000)  (0.000)  (0.006) 
Fourier panel stationarity test for 10 CEEC under the Null hypothesis of stationarity.
Critical values (obtained from Becker et al. 2006, p. 289) for individual test statistics are as follows: 0.1318 (10%), 0.1720 (5%), 0.2699 (1%) for
Critical values for constant and trend are as follows: 0.0471 (10%), 0.0546 (5%), 0.0716 (1%) for
Significance at 10%, 5% and 1% are denoted by ^{*}, ^{**} and ^{***} respectively.
CEEC, Central and Eastern European Countries.
Regarding the output of the test, we consider three scenarios. Firstly, we test the null hypothesis of ‘no cointegration’ under the condition of absence of breaks. That is, we assume there are no breaks in the cointegration relationship. Secondly, we test the null hypothesis by considering breaks in only the intercept (level break). Finally, we consider breaks in both the intercept and the slope (regime shift). From Table 9, we observe that none of the models are cointegrated when we consider significance at a strict 5% level. Considering a more relaxed significance level at 10%, we find evidence of cointegration for the model with no breaks for the
I would like to thank Joakim Westerlund for making the Gauss codes available.
. This implies a rejection of the fiscal sustainability hypothesis for CEEC, which is in contrast to previous studies on CEEC, notably by Krajewski et al (1993–2016) and Llora and Redzepagic (2007). Even though they both employed a panel cointegration procedure, their studies did not to test for structural breaks and crosssectional dependence in the cointegration relationships, which can be considered a major weakness. Hence, accounting for this dynamism (breaks and crosssectional dependence) in a panel data setting reinforces the credibility of the results in this study.Panel cointegration test of revenue and expenditure – Europe
No breaks  0.065  0.168  
Level break  0.879  0.810  0.690  0.755 
Regime shift  0.422  0.663  0.437  0.669 
Number of observations  250  250 
Westerlund and Edgerton (2008) cointegration test with three maximum number of breaks in the cointegration relationship, which are determined by grid search at the minimum of the SSR. Null hypothesis indicates ‘No cointegration’. Displayed
SSR, sum of squared residuals.
Recall from Eq. (9) the cointegration relationship between revenues and expenditure. We decompose these fiscal variables into a trend and cyclical components. Following Galí et al. (2003), we posit that the cyclical component of fiscal variables necessitates automatic responses from government, which represents passive policy. In other words, this aspect does not constitute planned longterm government action and is influenced mainly by business cycles. The trend component on the other hand represents an active discretionary fiscal policy and hence should be only considered when examining the longterm behaviour of government policy. Decomposing Eq. (9), we have
A popular tool for decomposing a series into trend and cyclical component is the Hodrick Prescott (HP) filter (see Hodrick and Prescott (1997)). Consider a time series of the form
Using the HP filter, we denote mathematically by minimising the equation
In a seminal paper, Hamilton (2018) proposed an alternative method for detrending a series and proved that the HP filter is deficient in three respects. Firstly, he argued that HP filter imposes a spurious dynamic relationship which has no basis as far as the data generating process is concerned. Secondly, there are discrepancies between filtered values at the end of the sample and those at the middle of the sample and also spurious values. Finally, the values of HP smoothing parameter are vastly at odds with common practice and hence not reliable. To demonstrate his recommended approach, Hamilton (2018) applied OLS regression of series
In the case of data with annual frequency, Hamilton (2018) recommended that the value of h should be fixed at h = 2
. The residualThe residual is stationary provided the fourth difference of
As a requirement for cointegration, we test all variables at their levels and first difference to ensure they are
Panel Cointegration cointegration test of cyclically adjusted revenue and cyclically adjusted spending
No breaks  0.002  0.000  
Level break  0.001  0.002  
Regime shift  0.009  0.001  
Number of observations  220  220 
Westerlund and Edgerton (2008) cointegration test with three maximum number of breaks in the cointegration relationship which are determined by grid search at the minimum of the SSR. Null hypothesis indicates ‘No cointegration’. Displayed
^{*}, ^{**} and ^{***} denote rejection of the null hypothesis at 10%, 5% and 1% respectively.
Hamiliton Filter is used to obtain cyclically adjusted revenues and expenditures.
SSR, sum of squared residuals.
Once cointegration is established, it is necessary to estimate the equilibrium parameter in the long run dynamic relationship in order to infer the sustainability hypothesis. Recall from Section 2 that to infer strong sustainability in the sense of Quintos (1995), the size of the slope coefficient must be equal to unity; otherwise a weak form of stationarity is inferred. It is important to note that when variables are expressed as ratios of GDP or in per capita terms, then it is even crucial to get a slope coefficient of 1 in the other to ensure that debt is bounded and does not explode to infinity (Afonso, 2005). We estimate a panel form of Eq. (13) with heterogeneous slope coefficient as below
Even though the OLS has been found to be super consistent, it has been proven to be deficient with finite data sets and complex dynamic relationships (Forest and Turner, 2013) and insufficient for panel data (Baltagi, 2005). Considering the fact that our slope coefficient in Eq. (18) is heterogeneous, it is necessary to use estimates that account for slope heterogeneity. This is quite common with macro datasets with large time series and crosssectional dimensions represented by countries or regions. The asymptotic of macro panels are different from that of traditional micro panels with large number of crosssections and a smaller time period. Such micro datasets are usually estimated with FE, random effects, instrumental variables techniques to account for possible endogeneity such as the generalised method of moments (Blackburne & Frank, 2007). However, these estimators rely on the assumption that the slope coefficient is homogeneous, something which is not applicable to our model.
Likely candidates are the mean group (MG) type of estimates, which includes the MG estimator, common correlated effect mean group (CCMG) and augmented mean group. The MG first developed by Pesaran and Smith (1995) is similar to the FEwithin model; however, it averages the slope for each individual country in the panel. From Eq. (18), one would estimate the Ngroup specific ordinary least squares regression and average the estimated coefficient for the group to account for heterogeneity in the coefficient. From Eq. (18), MG estimator is as follows:
One of the main criticisms of the MG estimator is the fact that it does not account for the issue of crosssectional dependence in panel data. Hence inferences from this estimator should be made with some caution due to potential bias. To circumvent this problem, Pesaran (2006) developed the CCEMG that accounts for crosssectional dependence by allowing for heterogeneous impact across panel members. From Eq. (18), we expand the error term to include an unobserved common factor which is recovered by cross sectional averages of the dependent variable and independent variable:
A further problem arises if the regressor in the model is potentially endogenous. Then most estimators which do not account for endogeneity bias will suffer from depending on nuisance parameter (Westerlund & Prohl, 2010). Authors such as Kao and Chiang (2000) and Chen, McCoskey and Kao (1999) therefore recommended the fully modified OLS (FMOLS) proposed by Phillips and Hansen (1990) and dyanmic OLS (DOLS) introduced by Saikkonen (1991) and later advanced by Stock and Watson (1993) as promising models for estimating the longrun vector in a cointegration regression.
Since our slope coefficient is very heterogeneous, MG estimator of FMOLS and DOLS will be appropriate in this context. Specifically, the MGFMOLS and MGDOLS introduced by Pedroni (2000, 2001) are suitable for estimating the cointegration vector such that it is consistent with crosssectional heterogeneity in panel cointegration studies. Again, consider a panel regression of the form in Eq. (18)
Under the specification above, if
The FMOLS makes correction for the OLS model by accounting for endogeneity and serial correlation in the OLS in Eq. (18) by applying a nonparametric correction. The MGFMOLS (accounting for heterogeneity in slope coefficient) is given by
The MGDOLS regression on the other hand entails augmenting the cointegration model with lags and leads of Δ
From Eq. (22), the panel DOLS is given as
The study makes use of all four models (MG, CCEMG, MGFMOLS and MGDOLS) to estimate the longrun cointegration vectors. Table 11 shows the estimated cointegration slope (γ) using the different estimators. In the case of MGDOLS, we explore lags and leads from 1 to 4 to experiment with the sensitivity of the γ coefficient. For FMOLS, we make use of the Bartlett Kernel for the longrun covariance matrix. The long run coefficient for the MG and CCEMG are 0.499 and 0.364, respectively, and
Long run coefficient for cyclically adjusted fiscal variables
γ  0.499  0.364  0.938  0.935 
Test stat  3.390  3.815  480.74  604.07 
0.000  0.000  0.000  0.000  
Std error  0.147  0.095  0.002  0.02 
Obs  220  220  210  130 
Shapiro–Wilk Normality test  0.970  0.977  0.992  0.986 
(0.000)  (0.001)  (0.260)  (0.220)  
Peseran CD test  3.652  3.328  
(0.000)  (0.016)  (0.001)  (0.795) 
Reported lags and leads of 4 for MGDOLS. The study explored lags and leads from 1 to 4; however, this does not change the estimates of the parameter.
CCEMG, common correlated effect mean group; CD, crosssectional dependence; DOLS, dyanmic OLS; FMOLS, fully modified OLS; MG, mean group; OLS, ordinary least square estimator.
We conduct some residual diagnostic tests to ascertain address the question of which estimator performs better. Recall that the model Eq. (18) relies on the assumption that the residuals are normally distributed with a zeromean and a constant variance. Hence it is feasible to test if, indeed, this is the case. The lower part of Table 11 depicts test statistics and probability values of the Shapiro–Wilk test (see Royston (1982)). From the
Secondly, we conduct crosssectional dependenking use of the Pesaran test (according to Pesaran (2004)). Reported
Considering the size of the slope coefficient for the two efficient estimators (MGDOLS and MGFMOLS), it is important to establish if indeed they are equal to 1. Recall from Section 2 that a slope coefficient of 1 guarantees strong fiscal sustainability since it implies that the debt to GDP ratio is bounded. To ascertain if cointegration slope (γ) is indeed 1, it is plausible to conduct a hypothesis test of the coefficient. We employ the Wald test under the null hypothesis that γ = 1 (
Results of the Wald test shown in Table 12 imply the rejection of the null hypothesis of a unit slope for the two models (MGDOLS and MGFMOLS) indicating that 0 < γ < 1. This is statistically significant if we consider the
Wald test of coefficient and confidence intervals
T stat  −32.018^{***}  −49.89^{***} 
Chisquare (1 df)  1,025.13  1,754.72 
0.000  0.000  
95% confidence intervals  (0.934–0.941)  (0.932–0.938) 
^{*}, ^{**} and ^{***} denotes rejection of the null at 10%, 5% and 1% respectively.
Null hypothesis: γ = 1.
DOLS, dyanmic OLS; FMOLS, fully modified OLS; MG, mean group; OLS, ordinary least square estimator.
This study sought to ascertain if the fiscal sustainability hypothesis holds for 10 CEEC from the period 1995 to 2019. Previous studies have shown that these countries have pursued policies compatible with the government IBC. We tested the hypothesis of sustainability of the fiscal stance by examining the cointegration relationship between revenues and expenditures, both as percentages of GDP. The econometric intuition is that if revenues and expenditure can be expressed as a linear combination and residuals can be proven to be stationary, then debt to GDP ratio is meanreverting, since the difference between revenue and expenditures do not drift wide apart. Hence inferences about long term relationship between revenues and expenditures could be made.
We adopted recent advancements in econometrics to test the fiscal sustainability hypothesis. As a first step, we considered total revenues and total expenditure. Preliminary results indicated that these fiscal variables are not cointegrated and cast doubt on the sustainability hypothesis for the 10 CEEC. The result is also in sharp contrast to earlier panel studies conducted for CEEC, which have all pointed in the direction of cointegrated revenue and expenditures. However, none of the studies considered accounted for structural breaks and crosssectional dependence in the data generating process, something that has become associated with dynamic macro panels. The study therefore tested, found evidence, and accounted for structural breaks for CEEC – most of which occurred as a result of the changes in fiscal policies prior to joining the EU and also shocks due to business cycles, notably the global financial crises in 2008.
As a next step, the study makes a justification for using cyclically adjusted revenues and expenditures and argues that this represents the longterm discretionary action of the fiscal authorities. Hence, the action of fiscal authorities should be judged by variables which are devoid of business cycle fluctuations or shocks. This is plausible because shocks to fiscal variables induce an automatic response by policymakers and do not necessarily characterise discretionary policy. We use the recently formulated Hamilton filter, which addresses the limitations of the popular HP filter to obtain cyclically adjusted fiscal variables. Results indicate that cyclically adjusted revenue and expenditures are cointegrated with a slope less than unity. We employed the Wald test to ascertain if, indeed, the slope coefficient is unity by way of hypothesis testing since the values are close enough to unity. Results provide enough evidence to reject the null hypothesis of a unit slope coefficient, indicating that the coefficient lies between 0 and 1. Considering the fact that these variables are ratios to GDP, a unit slope of the cointegration is necessary to guarantee strong sustainability in the sense of Quintos (1995). But even though there is cointegration between cyclically adjusted revenue and expenditure, a slope coefficient less than unity implies that expenditures to GDP ratio will grow faster than revenues to GDP ratio, implying a weaker form of sustainability. This is because the debt to GDP ratio is not bounded and therefore not finite. If this continues to happen for a long time, it will generate spikes in the debt to GDP ratio and the fiscal stance will no longer be sustainable.
The possible policy implications are as follows. Firstly, holders of government bonds could lose confidence if debt accumulation is persistent, since this casts doubt with regards to the ability of the government to service its payment. Secondly, the government may have difficulties in marketing its debts to new investors and hence would not be able to raise substantial additional revenue by issuing bonds in the future due to unattractiveness of its debts. Otherwise, government would have to pay high interest in order to make its debt attractive to investors. CEEC governments may therefore have to alter their fiscal policy by way of increasing revenue or reducing expenditure or both as a way of counteracting the deficit problem. The study provides fresh evidence using cyclically adjusted revenue and expenditure for panel sustainability analysis in the context of CEEC. The discretionary action of the government is deemed not to be sufficient to infer strong sustainability of the fiscal stance. The government in CEEC must therefore do more to address the fiscal deficit problem by way of fiscal consolidation to avoid future implications of sustainability.
With the current corona pandemic, fiscal sustainability has become even more challenging as the current recession necessitates further action of the government in terms of stimulating aggregate demand. However, with low revenues due to low productivity and output, government cannot respond adequately to the pandemic without, for instance, borrowing to augment its revenue. Others have also advocated for taxing the superrich in society as a way of increasing revenue. However, the effectiveness of this policy, as demonstrated by Scheuer and Slemrod (2019), depends on the elasticity of the taxpayers. The current recession and the previous (global financial crises) have taught us that the possibility of a looming recession in the future cannot be ruled out; hence, there should be adequate fiscal space for governments to respond appropriately to future shocks. It is therefore important for government with high debt burdens to institute structural changes, especially in normal times, as a way of reducing debt stocks. This will ensure that there is enough fiscal space in the future to combat the consequences of recessions.
Wald test of coefficient and confidence intervals
T stat  −32.018 
−49.89 
Chisquare (1 df)  1,025.13  1,754.72 
0.000  0.000  
95% confidence intervals  (0.934–0.941)  (0.932–0.938) 
Panel summary Statistics
Mean  0.391  0.419  0.340 
Standard Deviation  0.039  0.505  0.178 
Maximum  0.482  0.541  0.711 
Minimum  0.308  0.321  0.038 
Observations  250  250  250 
Deficit to GDP ratio
1995  1.05  
1996  
1997  2.15  1.42  0.76  
1998  0.03  1.08  
1999  0.09  
2000  
2001  0.20  1.05  
2002  0.42  
2003  1.82  
2004  2.34  1.80  
2005  1.08  1.00  
2006  2.87  1.81  
2007  2.73  1.10  
2008  1.59  
2009  
2010  0.19  
2011  1.06  
2012  
2013  0.18  
2014  0.70  
2015  0.14  
2016  0.72  0.06  0.23  0.09  
2017  1.56  0.45  1.10  
2018  1.09  0.60  0.77  1.75  
2019  0.75  0.51  0.13  0.72 
Panel Stationarity stationarity test with sharp breaks  – Robustness robustness check
Break (Homogeneous)  1.762 (0.039) 
0.426 (0.335) 
Breaks (Heterogeneous)  2.975 (0.001) 
0.152 (0.440) 
Break (Homogeneous)  1.687 (0.046) 
0.137 (0.446) 
Breaks (Heterogeneous)  2.031 (0.021) 
0.630 (0.264) 
Panel stationarity test – Expenditure
Czechia  0.070  0.309  0.302  0.050 
0.059  0.050 
Estonia  0.099  0.410 
0.331  0.052 
0.124 
0.125 
Hungary.  0.203 
0.298  0.201  0.044  0.101  0.091 
Latvia  0.183 
0.504 
0.414 
0.051 
0.080  0.083 
Lithuania  0.052  0.055  0.116  0.051 
0.043  0.051 
Poland  0.182 
0.496 
0.416 
0.053 
0.056  0.076 
Slovakia  0.048  0.219  0.197  0.040  0.145 
0.139 
Slovenia  0.059  0.189  0.322  0.054 
0.097  0.085 
Bulgaria  0.148 
0.089  0.095  0.051 
0.088  0.087 
Romania  0.124  0.313  0.181  0.053 
0.136 
0.133 
Panel statistic  2.995 
3.508 
2.282 
4.938 
3.309 
2.460 
(0.001)  (0.000)  (0.011)  (0.000)  (0.000)  (0.007) 
Panel stationarity Test test – First first difference of Revenuerevenue
Czechia  0.127  0.075  0.074  0.044  0.057  0.074 
Estonia  0.093  0.082  0.117  0.051 
0.071  0.111 
Hungary.  0.038  0.151  0.159  0.031  0.148 
0.153 
Latvia  0.063  0.062  0.107  0.047  0.052  0.056 
Lithuania  0.081  0.122  0.079  0.061 
0.082  0.077 
Poland  0.088  0.128  0.157  0.057 
0.056  0.060 
Slovakia  0.108  0.531 
0.423 
0.076 
0.120 
0.099 
Slovenia  0.061  0.108  0.117  0.057 
0.101  0.077 
Bulgaria  0.180 
0.327  0.366 
0.027  0.149 
0.159 
Romania  0.038  0.065  0.083  0.035  0.048  0.041 
Panel statistic  1.282  0.574  0.292  4.723 
2.965 
2.357 
(0.100)  (0.283)  (0.385)  (0.000)  (0.002)  (0.009) 
Chow test of poolability of coefficients (5% significance level)
FE (within)  Variable  2.8438  0.004  Not poolable 
Pooled OLS  Fixed  14.892  0.000  Not poolable 
Panel stationarity test – first difference of expenditure
Czechia  0.195 
0.170  0.132  0.059 
0.094  0.093 
Estonia  0.032  0.033  0.065  0.031  0.035  0.031 
Hungary.  0.078  0.091  0.110  0.048 
0.089  0.107 
Latvia  0.057  0.126  0.070  0.046  0.050  0.046 
Lithuania  0.078  0.148  0.080  0.053 
0.097  0.060 
Poland  0.075  0.044  0.055  0.045  0.038  0.051 
Slovakia  0.122  0.136  0.137  0.047  0.083  0.088 
Slovenia  0.064  0.131  0.097  0.060 
0.111 
0.096 
Bulgaria  0.069  0.114  0.123  0.031  0.091  0.096 
Romania  0.053  0.069  0.181  0.052 
0.040  0.068 
Panel statistic  0.970  4.437 
1.674 
1.034  
(0.166)  (0.796)  (0.909)  (0.000)  (0.047)  (0.151) 
Panel stationarity test – first difference of cyclically adjusted expenditure
Czechia  0.224 
0.255  0.298  0.140 
0.142 
0.137 
Estonia  0.041  0.105  0.061  0.037  0.043  0.045 
Hungary.  0.059  0.141  0.144  0.045  0.141 
0.131 
Latvia  0.076  0.075  0.068  0.056 
0.063  0.068 
Lithuania  0.121  0.211  0.194  0.119 
0.126 
0.118 
Poland  0.193 
0.191  0.198  0.051 
0.120 
0.144 
Slovakia  0.044  0.304  0.204  0.036  0.036  0.063 
Slovenia  0.138 
0.189  0.147  0.108 
0.137 
0.074 
Bulgaria  0.102  0.073  0.142  0.049 
0.051  0.061 
Romania  0.042  0.147  0.055  0.038  0.048  0.044 
Panel statistic  2.237 
0.668  9.258 
3.130 
2.196 

(0.013)  (0.252)  (0.532)  (0.000)  (0.001)  (0.014) 
Panel stationarity test – Revenue
Czechia  0.146 
0.314  0.408 
0.047  0.048  0.068 
Estonia  0.157 
0.271  0.108  0.044  0.066  0.091 
Hungary.  0.342 
0.187  0.148  0.040  0.107 
0.103 
Latvia  0.070  0.448 
0.313  0.052 
0.074  0.076 
Lithuania  0.074  0.155  0.299  0.051 
0.076  0.054 
Poland  0.170 
0.409 
0.352 
0.065 
0.101  0.103 
Slovakia  0.262 
0.371 
0.362 
0.057 
0.153 
0.143 
Slovenia  0.056  0.128  0.239  0.056 
0.079  0.074 
Bulgaria  0.268 
0.115  0.121  0.065 
0.114 
0.102 
Romania  0.074  0.151  0.160  0.049 
0.121 
0.116 
Panel statistic  5.652 
2.716 
2.136 
5.606 
3.405 
2.523 
(0.000)  (0.003)  (0.016)  (0.000)  (0.000)  (0.006) 
Panel cointegration test of revenue and expenditure – Europe
No breaks  0.065  0.168  
Level break  0.879  0.810  0.690  0.755 
Regime shift  0.422  0.663  0.437  0.669 
Number of observations  250  250 
Panel cointegration test of cyclically adjusted revenue and cyclically adjusted spending (HP Filter used for detrending series)
No breaks  1.731  0.958  −2.409 
0.008 
Level break  −1.984 
0.024  −2.490 
0.006 
Regime shift  −6.050 
0.000  −7.182 
0.000 
Number of observations  250  250 
Panel stationarity Test test – First first difference of cyclically adjusted revenue
Czechia  0.060  0.092  0.168  0.058 
0.068  0.067 
Estonia  0.043  0.073  0.097  0.039  0.067  0.086 
Hungary.  0.042  0.071  0.121  0.040  0.071  0.122 
Latvia  0.093  0.096  0.092  0.093 
0.096  0.088 
Lithuania  0.166 
0.496 
0.303  0.103 
0.130 
0.104 
Poland  0.217 
0.187  0.286  0.033  0.071  0.089 
Slovakia  0.102  0.244  0.214  0.102 
0.104 
0.095 
Slovenia  0.131  0.172  0.185  0.114 
0.197 
0.088 
Bulgaria  0.1068  0.085  0.117  0.063 
0.077  0.116 
Romania  0.120  0.071  0.052  0.039  0.047  0.042 
Panel statistic  2.244 
0.421  0.190  9.430 
3.306 
2.275 
(0.012)  (0.337)  (0.425)  (0.000)  (0.000)  (0.011) 
Summary of existing empirical panel fiscal sustainability test
Stationarity of debt and cointegration between revenue and expenditure  15 Selected EU countries (1970–2006)  Fiscal stance sustainability confirmed  
Baldi and Staehr (2015)  Estimated fiscal reaction function of primary balance, debt and business cycle viables  Different groups of EU countries (2001–2004)  Sustainable fiscal stance for all groups post financial crises 
Panel cointegration test between primary balance and public debt  21 OECD countries (1991–2015)  Fiscal stance judged to be unsustainable  
Fiscal reaction function of primary balance and debt  CEEC (1997–2013)  Fiscal stance sustainable for selected countries  
Stationarity of public debt  19 European countries (1970–2016)  Fiscal stance sustainability confirmed  
Fiscal reaction function of primary balance response to debt  18 Euro Area countries (1970–2013)  Sustainable fiscal stance  
Cointegration between revenue, spending and net interest payment  Selected European countries (1970–2001)  Sustainable fiscal policy  
Cointegration between revenue and expenditure and a fiscal reaction function  CEEC (1990–2012)  Sustainable fiscal stance  
Fiscal reaction function of primary balance response to debt  EU regional groups (1950–2014)  Varied results depending on the region  
Cointegration between revenue and expenditure  CEEC (1999:1–2006:1)  Fiscal stance sustainable in selected countries  
Cointegration between budget deficit and public debt  15 EU countries (1970–2004)  Fiscal stance sustainability confirmed  
Cointegration between revenue and expenditure  8 rich OECD countries (1977:1–2006:4)  Sustainability hypothesis confirmed for selected countries 
Breusch–Pagan and Pesaran crosssectional dependence test
Expenditure to GDP ratio  107.84, 0.000  5.494, 0.000 
Revenue to GDP ratio  113.42, 0.000  1.716, 0.0862 
Long run coefficient for cyclically adjusted fiscal variables
γ  0.499  0.364  0.938  0.935 
Test stat  3.390  3.815  480.74  604.07 
0.000  0.000  0.000  0.000  
Std error  0.147  0.095  0.002  0.02 
Obs  220  220  210  130 
Shapiro–Wilk Normality test  0.970  0.977  0.992  0.986 
(0.000)  (0.001)  (0.260)  (0.220)  
Peseran CD test  3.652  3.328  
(0.000)  (0.016)  (0.001)  (0.795) 
Panel stationarity test – cyclically adjusted expenditure
Czechia  0.144 
0.165  0.132  0.065 
0.134 
0.126 
Estonia  0.080  0.401  0.385 
0.046  0.105 
0.092 
Hungary.  0.157 
0.096  0.068  0.059 
0.100  0.126 
Latvia  0.106  0.202  0.155  0.045  0.080  0.073 
Lithuania  0.089  0.172  0.146  0.072 
0.135 
0.126 
Poland  0.154 
0.466 
0.577 
0.047  0.074  0.073 
Slovakia  0.095  0.258  0.206  0.063 
0.146 
0.126 
Slovenia  0.091  0.488 
0.332  0.070 
0.140 
0.093 
Bulgaria  0.102  0.072  0.097  0.068 
0.072  0.065 
Romania  0.064  0.087  0.110  0.049 
0.089  0.110 
Panel statistic  2.491 
2.376 
1.461 
7.017 
4.511 
2.885 
(0.006)  (0.009)  (0.072)  (0.000)  (0.000)  (0.002) 
Dates of structural breaks for individual series
Czechia  2002, 2010  2003 
Estonia  1998, 2008, 2011  1999, 2007, 2010 
Hungary  1997, 2006, 2011, 2015  1998, 2001, 2015 
Latvia  1999, 2009  1997, 2000, 2008, 2011 
Lithuania  2000  2000, 2008, 2011 
Poland  1997, 2008, 2015  1997, 2011 
Slovakia  1997, 2000, 2003, 2012  2002, 2008 
Slovenia  2011, 2015  2008, 2015 
Bulgaria  1997, 2008, 2012  1998, 2002 
Romania  1997  1998, 2001, 2006, 2012 
Panel Cointegration cointegration test of cyclically adjusted revenue and cyclically adjusted spending
No breaks  0.002  0.000  
Level break  0.001  0.002  
Regime shift  0.009  0.001  
Number of observations  220  220 
Panel stationarity Test test – Cyclically cyclically adjusted revenue
Czechia  0.160 
0.416 
0.519 
0.048 
0.070  0.060 
Estonia  0.079  0.414 
0.249  0.044  0.042  0.089 
Hungary  0.040  0.398 
0.348 
0.039  0.058  0.103 
Latvia  0.037  0.324 
0.356 
0.037  0.036  0.051 
Lithuania  0.061  0.242  0.165  0.059 
0.145 
0.111 
Poland  0.241 
0.434 
0.435 
0.078 
0.056  0.072 
Slovakia  0.080  0.262  0.230  0.056 
0.149 
0.123 
Slovenia  0.097  0.237  0.261  0.075 
0.132 
0.066 
Bulgaria  0.040  0.281  0.343 
0.037  0.035  0.071 
Romania  0.083  0.246  0.338  0.054 
0.044  0.073 
Panel statistic  1.524 
4.395 
3.768 
5.575 
1.985 
1.682 
(0.064)  (0.000)  (0.000)  (0.000)  (0.023)  (0.046) 
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