Fiscal Sustainability Hypothesis Test in Central and Eastern Europe: A Panel Data Perspective

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 political-economic principles lately. The Keynesian school of thoughts advocates for government intervention to stabilize market economies.

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 so-called 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 long-run 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 cross-sectional 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 so-called 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 long-run relationship between revenues and expenditure using panel cointegration.

These countries are Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia, Bulgaria and Romania.

Furthermore, cross country macroeconomic and financial datasets are associated with cross-sectional dependence because of inter-country links and dependencies, which is more of the rule now than an exception (Westerlund & Edgerton, 2008). Cross-sectional 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 cross-sectional independence suffers from size distortions as the actual size of the test is lower than the empirical size.

Preliminary results show that revenues and expenditures do not have a long-term 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 long-term 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.

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 (1) gt+itbt−1=rt+bt−bt−1 {g_t} + {i_t}{b_t} - 1 = {r_t} + {b_t} - {b_t} - 1 where g_t represents government spending, b_t is the government bond, i_t is the interest rates on bonds and r_t represents the government revenue. The above equation shows that the government expenditure (LHS) must be equal to total government receipts (RHS) at all times in order for the budget constraint to hold intertemporarily. Here we rule out the possibility of monetising of spending or the activities of monetary authorities. That is, we do not consider government printing money (also known as seignorage) to fund its expenditure as this is known to cause inflation. This assumption is plausible because the characteristic of modern economies is such that central banks independently control monetary policy (Greiner & Fincke, 2015) with little or no influence from fiscal authorities.

Taking the state of the economy into consideration and assuming i_t to be stationary around its mean i, Eq. (1) can be re-written as (2) gtyt+(1+i)bt−1yt=ryt+btyt {{{g_t}} \over {{y_t}}} + {{\left( {1 + i} \right){b_{t - 1}}} \over {{y_t}}} = {r \over {{y_t}}} + {{{b_t}} \over {{y_t}}} where y_t represents national income or nominal GDP. Simplifying further leads to (3) gtyt+(1+i)bt−1yt.yt−1yt−1=rtyt+btyt {{{g_t}} \over {{y_t}}} + {{\left( {1 + i} \right){b_{t - 1}}} \over {{y_t}}}.{{{y_{t - 1}}} \over {{y_{t - 1}}}} = {{{r_t}} \over {{y_t}}} + {{{b_t}} \over {{y_t}}} (4) gtyt+(1+i)(1+f).bt−1yt−1=rtyt+btyt {{{g_t}} \over {{y_t}}} + {{\left( {1 + i} \right)} \over {\left( {1 + f} \right)}}.{{{b_{t - 1}}} \over {{y_{t - 1}}}} = {{{r_t}} \over {{y_t}}} + {{{b_t}} \over {{y_t}}} where f is the nominal growth rate of the economy (GDP). Using capital notations, Eq. (4) can be rewritten as (5) Gt+(1+ρ)Bt−1=Rt+Bt {G_t} + \left( {1 + \rho } \right){B_{t - 1}} = {R_t} + {B_t} where gtyt=Gt {{{g_t}} \over {{y_t}}} = {G_t} , bt−1yt−1=Bt−1 {{{b_{t - 1}}} \over {{y_{t - 1}}}} = {B_{t - 1}} , rtyt=Rt {{{r_t}} \over {{y_t}}} = {R_t} , btyt=Bt {{{b_t}} \over {{y_t}}} = {B_t} . ρ=i−f1+f \rho = {{i - f} \over {1 + f}} is the growth adjusted interest rate, which is assumed to be stationary for sake of simplicity.

Since further modification is required for empirical estimation, let Gtr=Gt+(ρt−ρ)Bt−1 G_t^r = {G_t} + \left( {{\rho _t} - \rho } \right){B_{t - 1}} , where ρ is the mean real interest rate and stationary.

Assuming that Eq. (5) holds continuously, then by forward substitution the present value budget constraint can be written as (6) Bt−1=∑s=t+1∞(11+ρ)s−t(Rs−1−Gs−1r)+lims→∞(11+ρ)s−tBs−1 {B_{t - 1}} = \sum\limits_{s = t + 1}^\infty {{{\left( {{1 \over {1 + \rho }}} \right)}^{s - t}}\left( {{R_{s - 1}} - G_{s - 1}^r} \right)} + li{m_{s \to \infty }}{\left( {{1 \over {1 + \rho }}} \right)^{s - t}}{B_{s - 1}}

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.

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 GG_t−1 = G_t + ρB_t−1. Assuming stationary real interest rate and applying the difference operator, the present value budget constraint can be re-written as (7) GGt−1−Rt−1=∑s=t+1∞(11+ρ)s−t(ΔRs−1−ΔGs−1r)+lims→∞(11+ρ)s−tΔBs−1 G{G_{t - 1}} - {R_{t - 1}} = \sum\limits_{s = t + 1}^\infty {{{\left( {{1 \over {1 + \rho }}} \right)}^{s - t}}\left( {\Delta {R_{s - 1}} - \Delta G_{s - 1}^r} \right)} + li{m_{s \to \infty }}{\left( {{1 \over {1 + \rho }}} \right)^{s - t}}\Delta {B_{s - 1}}

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, one-period government debt (ΔB_s−1) must not grow faster than the interest rate on debts. In order words, it is easier to see that Eq. (8) holds if ΔB_s−1 is stationary as compared to a situation where it is not stationary. Considering that one period debt is given by the relationship ΔB_t = GG_t − R_t, testing for stationarity of a one-period government debt implies testing for the difference stationarity for GG_t and R_t. This can be problematic if government spending and revenue are not stationary at their levels. However, if one can prove that they are stationary at their first difference, then the concept of cointegration can be applied. The intuition is that, if one variable can be written as a linear combination of the other with a slope coefficient such that the residual is proved to be stationary, then their relationship is stable and mean-reverting. In order words, the difference of these variables does not drift wide apart. Hence, we say they are cointegrated because they have a long-run stable relationship. From Eq. (8), this implies testing if GG_t−l and R_t−1 are integrated of order 1 (I(1)) with an imposition of cointegration vector (1, −1) as argued by Quintos (1995). One can test for cointegration equivalently as below: (9) Rt=α+γGGt+μt {R_t} = \alpha + \gamma G{G_t} + {\mu _t} Alternatively, making use of the expression ΔB_t = GG_t − R_t, then from Eq. (9) we have (10) ΔBt=(1−γ)GGt−α−μt \Delta {B_t} = \left( {1 - \gamma } \right)G{G_t} - \alpha - {\mu _t}

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 R_t on GG_t simply by ordinary least square estimator (OLS) and testing the residuals for stationarity to confirm if cointegration holds. Westerlund and Prohl (2010) argued that such conventional test fails to reject the null hypothesis of no cointegration very often, which implies a rejection of sustainability hypothesis. They cited the problem of low power of the cointegration test because of low sample size. Panel datasets circumvent the power problem as it gives an opportunity to increase the sample size. Firstly, panels present more informative data because it has long sample size, provides more variability, involves less collinearity among variables and gives more degree of freedom for the model (Baltagi, 2008). Secondly, panel data affords researchers the opportunity to construct and test more advanced and complicated models as compared to time series or cross-sectional data, and finally panel helps to control for the effects of omitted variables bias in econometric (Hsiao, 2003). Hence the study will resort to a panel test which will subject the residuals in Eq. (9) to a cointegration test. The test is dynamic enough to account for structural breaks and cross-sectional dependence which is common to panel data analysis. The forthcoming sections will provide discussions on the econometric procedures for the panel test.

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.

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.

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 revenue-expenditure 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 upward-sloping relationship can be observed between the two variables, which provide some hints as to the nature of the relationship between the two fiscal variables.

Figure 1

Figure 2

Figure 3

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 non-rejection of the null hypothesis, hence leading to a wrong conclusion about results of the test (Carrion-i-Silvestre, 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 F-statistics 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 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.

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).

Next, we test for evidence of cross-sectional dependence of the individual units in the panel. We deem it feasible to test for cross-sectional 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 cross-section in the panel data is independent. Such The so-called first generational econometric test test usually suffers from size distortions, which affects the inferences (Banerjee, Marcellino & Osbat, 2004). Properly accounting for cross-sectional dependence in panels improves the efficiency of parameter estimates and simplifies statistical inferences (Hsiao, 2014). Two main cross-sectional 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 N and large T (Pesaran, 2004).

Table 5 presents results of the Breusch–Pagen and Pesaran cross-sectional 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 cross-sectional 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 cross-country dependence in the panel data and hence justifies the need to choose an econometric procedure that accounts for cross-sectional 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 (11) Rit=αi+γjGGit+εit {R_{it}} = {\alpha _i} + {\gamma _j}G{G_{it}} + {\varepsilon _{it}} where R_it is government revenue to GDP ratio, G_it is government expenditure to GDP ratio, ɛ_it represents the error term or residuals and α_i is the time-invariant intercept. From the slope coefficient, j could be the individual countries (heterogeneous across the countries) or time (heterogeneous across the time). In each case, we use the Chow (1960) test analogous to the F-test to test for poolability under the assumption that the residuals are normally distributed with a zero mean and constant variance. The test statistics is constructed by looking at the difference between the sum of squared residuals (SSR) of the restricted model and the SSR of the unrestricted model, and dividing by the SSR of the unrestricted model considering their degrees of freedom. In the context of panels, the unrestricted model could be a fixed effect (FE) within the model (with variable intercepts) or a pooled OLS regression model (constant intercept). A detailed discussion of the Chow test can be found in Wooldridge (2009) and Baltagi (2005).

From Table 6, if we consider an unrestricted model of a FE-within 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.

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 cross-sectional dependence at the same time. This test is one of the few second generational unit root tests that accounts for both cross-sectional 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 cross-sectional dependence. A Fourier approximation can be used to model structural shifts of any form or non-linearity 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. Carrion-i-Silvestre et al. (2005) developed a panel unit root test which is capable of accommodating sharp breaks in panels by using dummy variables.

The null hypothesis of the test implies ‘stationarity’ against the alternative of a unit root. The test depends on the Fourier frequency (k) which determines the swings and amplitude of the series. Considering the span of the time series in the panel (25 years), we choose k = 3, sufficient enough to cover the length of the time series. Tables 6 and 7 present the results of the panel univariate stationarity test and the test for the individual countries for expenditure and revenue, respectively

I would like to thank Saban Nazlioglu for making the Gauss codes available.

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 one-stop-shop by accounting for structural breaks and cross-sectional 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 root-based 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 Z_τ(N) is based on the least square estimate of the residual slope, whereas the second test statistics Z_φ(N) is based on estimating the t-ratio of the slope. A maximum of 3 breaks is chosen for the cointegration relationship. The selection of optimum lag length is based on an automatic procedure adopted from Campbell and Perron (1991) (Table 8).

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 Z_τ(N) test and no cointegration for Z_φ(N). Hence, even with no breaks, the cointegration relationship is not strongly confirmed. This provides fresh evidence of lack of cointegration between total revenues and expenditures (all ratios of GDP)

I would like to thank Joakim Westerlund for making the Gauss codes available.

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 mean-reverting, 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 cross-sectional 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 long-term 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 super-rich 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.