Characteristics of fiscal policy in Croatia: does it depend on the phase of the business cycle?*

Vladimir Arčabić**

Vladimir Arčabić

Affiliation: University of Zagreb, Faculty of Economics and Business, Zagreb, Croatia

0000-0003-4173-8637

Correspondence
varcabic@efzg.hr

   

Frane Banić**

Frane Banić

Affiliation: Croatian National Bank, Zagreb, Croatia

0000-0002-1672-931X

Article   |   Year:  2021   |   Pages:  433 - 457   |   Volume:  45   |   Issue:  4
Received:  July 1, 2021   |   Accepted:  September 20, 2021   |   Published online:  December 6, 2021

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https://doi.org/10.3326/pse.45.4.3

Abstract

1 Introduction

This paper analyzes public debt sustainability and fiscal policy cyclicality using Bohn’s (1998) fiscal reaction function. The main point of the paper is that we analyze fiscal policy characteristics over different phases of the business cycle, e.g., expansions and recessions. Therefore, we study the asymmetric behavior of fiscal policy, allowing for different behavior depending on the business cycle phase. Public debt sustainability tells us how policymakers react to indebtedness.

To analyze the asymmetric behavior of fiscal policy over the business cycle, we estimate Bohn’s (1998) fiscal reaction function using two nonlinear models, e.g., threshold regressions and the Markov switching model. The nonlinear models allow for an endogenous switch depending on the output gap, which defines different regimes. Then, the estimated coefficients differ across the regimes. The upper and lower regime could be related to periods of expansion and recession, respectively. Consequently, the analysis is focused on potential asymmetric effects from the aspect of fiscal policy cyclicality and public debt sustainability. 

We take business cycle measurement seriously and use two univariate output gap measures (Hamilton, 2018; Hodrick and Prescott, 1997) and one bivariate measure based on the structural vector autoregressive model (SVAR). All three measures are used both for output gap and cyclically adjusted primary balance (CAPB) calculation; regarding the character of fiscal policy, that is, it is more appropriate to use a cyclically adjusted primary balance instead of the primary balance. Then, the effect of the cycle on the budget is removed, and consequently, the problem of endogeneity is avoided. Also, the cyclically adjusted primary balance provides information on the discretionary measures of fiscal policymakers, which directly determine the behavior of fiscal policy. 

In our analysis, Bohn’s (1998) fiscal reaction function is extended with lagged cyclically adjusted primary balance and with the growth rate of the economic policy uncertainty index (EPU index) in the robustness check. The EPU index was applied given the assumed impact of uncertainty on the discretionary measures of fiscal policymakers. 

2 Literature review

1998

Most empirical research indicated the existence of a fiscal response function, i.e., empirical confirmation of the positive relationship between the budget or primary balance and lagged public debt (Berti et al., 2016; Cassou, Shadmani and Vázquez, 2017; Piergallini and Postigliola, 2012; Checherita-Westphal and Žd'árek, 2017; Banić, 2020). In other words, an increase in public debt will result in an increase in the budget or primary balance in order to mitigate the negative impact of public debt accumulation on the sustainable management of public finances. Such a response is considered to reflect sustainable fiscal policy. Checherita-Westphal and Žďárek (2017) assessed the function of the fiscal response in the European Union using a dynamic panel analysis for the period from 1970 to 2013. Furthermore, the primary balance to GDP ratio represents a function of the lagged public debt GDP ratio, control variables, i.e., lagged primary balance to GDP ratio, and certain institutional and political factors. The results of the panel analysis indicate a positive reaction of fiscal policymakers, i.e., an increase in the primary balance by about 0.03-0.05% when the share of public debt in GDP increases by one percentage point. Berti et al. (2016) assessed the fiscal response function on a sample of twelve Central and Eastern European countries in the period from the mid-1990s to 2013. In an empirical study, they applied various methods of static (pooled ordinary least squares (OLS) and panel model with fixed effects) and dynamic (generalized method of moments, GMM) panel analysis for the purpose of checking the robustness of the model, where the primary balance is defined as a function of the lagged primary balance, lagged public debt, expenditure gap and inflation. Regardless of the chosen method, all the obtained results indicated a statistically significant and positive relationship between the primary balance and lagged public debt. Piergallini and Postigliola (2012) analyzed public debt sustainability in Italy, assessing the function of the fiscal response in the period from 1861 to 2009. The results of the estimated vector autoregression (VAR) model indicate the positive fiscal response in Italy, emphasizing the fiscal policymakers’ awareness of the negative consequences of public debt accumulation on the efficient management of public finances. Observing fiscal sustainability in Croatia, Banić (2020) estimated the function of fiscal response applying the method of least squares (LS) in the period from 2002 to 2020. Given the increase in the primary balance-GDP ratio due to the accumulation of public debt in Croatia, a positive reaction of fiscal policymakers is noticeable in response to the potential deterioration of public finances. 

In contrast to findings of the positive reaction of fiscal policymakers to the accumulation of public debt, empirical research by Arčabić (2018) and Berti et al. (2016) indicates the absence of any reaction by fiscal policymakers. In more detail, Arčabić (2018) analyzed the sustainability of fiscal policy in the period from 2002 to 2017 in the EU-28 member states. The results of a dynamic panel system GMM estimator with common correlated effects indicate unsustainable fiscal policy at the EU level; fiscal policymakers do not increase the primary surplus due to the accumulation of public debt, with exceptions related to the EU-13 group and the sample of countries with a public debt-GDP ratio greater than 90%. An analysis of fiscal sustainability in the EU-13 member states suggests dual results (Berti et al., 2016), for the fiscal response function was estimated by a vector error correction model (VECM) in the period from 1950 to 2013, the function for each country in the sample being estimated separately. The results show the absence of a fiscal response function with regard to the statistical insignificance of the public debt coefficient, while the example of France shows a fiscally unsustainable situation with regard to the negative sign of the lagged public debt coefficient in the model. However, by including in the model a dummy variable to identify the onset of the global financial crisis in 2009, the results point to a significant change in the behavior of fiscal policymakers. Moreover, for most countries in the selected group, the lagged public debt coefficient becomes positive and statistically significant. Consequently, research (Berti et al., 2016) emphasizes the importance of the dynamics of the relationship between the primary balance and public debt, which, in contrast to the assumed linear relationship in the aforementioned empirical research, could also be nonlinear. 

Gosh et al. (2011) point out that the relationship between the budget balance and lagged public debt is not exclusively linear but depends on the level of debt, i.e., the public debt-GDP ratio, emphasizing that fiscal policymakers react only when the share of public debt in GDP exceeds a certain limit that may jeopardize fiscal sustainability.¹ Also, significant accumulation of public debt can result in fiscal unsustainability despite the positive reaction of fiscal policymakers. Therefore, many studies assess the nonlinear function of the fiscal response, setting thresholds that indicate a change in the direction of fiscal policy. For example, Gosh et al. (2011) indicated the existence of a nonlinear relationship between the primary balance and lagged public debt in a sample of 23 developed economies in the period from 1970 to 2007. The relationship is approximated by the cubic function, where at low debt levels, the correlation between the primary balance and public debt does not exist, and if it does, then it is negligible. Furthermore, the panel analysis shows not only that the accumulation of public debt results in an increase in the primary balance but also that over time the reaction of fiscal policymakers tends to disappear and become completely absent at high, unsustainable levels of public debt. Cassou, Shadmani and Vázquez (2017) analyzed the fiscal sustainability of the USA in the period from 1955 to 2013, applying nonlinear threshold (TR) and Markov Switching (MS) models. An analysis on a short sample, from 1955 to 1995, indicates that fiscal policy is sustainable only in the switching model, given that the primary deficit is declining due to increasing debt. On a larger sample, from 1955 to 2013, the results point to fiscal sustainability exclusively in the Markov switching model and only during periods of economic expansion. 

The behavior of fiscal policy can be countercyclical and procyclical. Tax revenues are sensitive to business cycles, i.e., they act as automatic stabilizers, and it is important to look at fiscal sustainability in the context of the cyclical nature of fiscal policy. The countercyclical policy is more desirable, and it can stimulate demand in recession by increasing government spending or reducing tax rates and do the opposite in times of economic expansion (Galí and Perotti, 2003). Arčabić (2018) determined the countercyclical behavior of fiscal policy in a sample of EU-28 countries using dynamic panel analysis. He showed a positive relationship between the output gap and the primary surplus, which can be explained as creating fiscal space to mitigate the negative effects of cycles in times of crisis. Dalić (2013) used panel data models to analyze the cyclical properties of government expenditure in the period 1999-2011 for Croatia and new member states that joined the EU in 2004 or later. Using the disaggregated approach, Dalić (2013) showed that the total general government expenditure and its main subcomponents (capital and current expenditure) behaved procyclically, while the social transfers behaved countercyclically. Regarding business cycle asymmetry, the results indicated procyclical behavior for total non-interest expenditure, capital expenditure, and non-wage government consumption in good and bad times, while for the other subcomponents, there is no strong statistical evidence of a difference in cyclical behavior during good and bad times. Cassou, Shadmani and Vázquez (2017) indicate the asymmetry of fiscal policy in the USA, given that fiscal policy in the threshold model is countercyclical during recessionary periods and less countercyclical during expansionary periods. On the other hand, fiscal policy is procyclical according to the Markov-switching model. Cassou, Shadm-ani and Vázquez (2017) explain the procyclicality of fiscal policy through empirical confirmation in research (Balassone, Francese and Zotteri, 2010) pointing out that, on the example of the EU, the increase in expenditures during the expansion is not accompanied by a decrease in expenditures during the recession, as is noticeable with revenues. Balassone, Francese and Zotteri (2010) stipulated the existence of cyclical asymmetry on a sample of EU-14 countries in the period 1970-2004, given that the budget balance deteriorates in a recession, but also that the balance does not improve during economic expansion. In the case of Croatia for the period 2003-2019, the results of Deskar Škrbić and Raos (2018) point to procyclical fiscal policy behavior during expansions and countercyclical fiscal policy behavior during recessions. The results of Deskar Škrbić and Grdović Gnip (2020) for the same period suggest the same conclusions regarding fiscal policy behavior during expansions, but not during recessions. The austerity measures implement, they suggest, resulted in procyclical fiscal policy behavior during recessionary periods. Furthermore, a significant contribution was made to the procyclical behavior of fiscal policy during expansion by expenditure growth, which is largely influenced by political cycles (Deskar Škrbić and Grdović Gnip, 2020).

3 Data and methodology

3.1 Data construction and sources

2020

We use data on real GDP, public debt, and budget balance obtained from the Eurostat database. Real GDP and budget balance series are seasonally adjusted. In addition, we use the economic policy uncertainty index for Croatia developed by Sorić and Lolić (2017). The EPU index is based on the frequency of articles in leading Croatian newspapers (Jutarnji list, Večernji list, and 24 sata) and news websites (index.hr, Poslovni dnevnik, and Dnevnik.hr), spanning the period from January 2003 to the present, various terms related to economics, politics, and uncertainty being used for the construction of the index. The index is regularly updated by the authors and can be downloaded from Baker, Bloom, and Davis (2021).

2009

(1)

We use three estimates of output gap when calculating cyclically adjusted primary balance, univariate Hodrick and Prescott (HP) (1997) and Hamilton (2018) filters, and the bivariate decomposition based on the structural vector autoregression (SVAR) model. 

Hodrick and Prescott (1997) decompose a time series into its trend and stationary components using the following optimization procedure:

(2)

where y_t represents GDP, T is the number of time series observations, and λ is a smoothing parameter that we set to 1600, which is considered common for quarterly data. The optimization process selects the τ_t series, which minimizes the sum of squares, thus giving the trend of the series, which is considered as potential output. We calculate the business cycle as a percentage deviation of GDP from the potential output. The HP filter is criticized in the literature for producing spurious cycles and for being imprecise at the beginning and the end of the sample (see Cogley and Nason, 1995; Hamilton, 2018). 

Therefore, we use other filters as well. Hamilton (2018) proposes an alternative for the HP filter as a regression of y_t series on a constant and four lags of y_t two years ago. For our GDP series, we estimate the following equation:

The third output gap measure is obtained from the bivariate SVAR model proposed by Blanchard and Quah (1989), where supply and demand shocks are identified through long-run restrictions. The vector of endogenous variables contains GDP growth and unemployment, or  and we estimate the following VAR model:

A(L)Xt = et

(4)

To identify supply and demand shocks, we impose the restriction that demand shocks do not affect GDP growth in the long run. Potential output is then calculated as a part of the GDP that is affected by supply shocks only. Historical decomposition is used to decompose GDP into its supply and demand components. As in the previous case, the output gap is calculated as a percent deviation of the GDP from the potential output. Such a measure of the output gap is also used in Furlanetto et al. (2020). 

Figure 1 shows three output gap estimations and three corresponding cyclically adjusted primary balances. The shaded area represents the recession period in Croatia. It is obvious that the three methods for output gap calculation give different results (left panel of figure 1). Hamilton’s filter is the most volatile, and provides a good description of the expansion prior to the global financial crisis (GFC) and of the sudden and deep recession starting in 2009. It also captures the increase in the Croatian output gap from 2014 onward. The HP filter is less volatile. It captures the expansion and potential overheating of the Croatian economy prior to the GFC, but the recession does not seem to be that deep in comparison to Hamilton’s filter. In general, business cycle fluctuations are mostly close to the potential GDP according to the HP filter. Finally, the SVAR is not as volatile as the Hamilton filter but is describes business cycle fluctuations in Croatia as large swings.

The SVAR model shows a deep recession starting in 2009, with the output gap constantly decreasing until the end of the recession in 2014. The recovery after 2014 is rather strong in comparison to the other two output gaps. Such large swings from the bivariate SVAR model capture changes not only in GDP but also in the labor market. A strong recovery after the crisis is more pronounced in the SVAR model because of a substantial decrease in the unemployment rate and overall positive trends in the labor market in Croatia after the GFC. 

Figure 1

Three output gaps and three cyclically adjusted primary balances in percent of GDP

DISPLAY Figure

The differences in output gap calculation reflect the cyclically adjusted primary balance as well, which is shown in the right panel in figure 1. Nevertheless, all three balances show similar dynamics. The deficit was increasing, and it reached its maximum in 2011. After that, the policy measures by fiscal authorities turned the trend around, and the cyclically adjusted primary balance started to recover quickly. Soon after the end of the GFC in Croatia, all three measures show a surplus in the cyclically adjusted primary balance. The surplus reached its maximum in 2017 that corresponds with the end of the excessive deficit procedure in Croatia.

3.2 Methodology

(5)

Fiscal policy is considered to be sustainable when the estimated coefficient next to lagged public debt is positive (β₁ > 0). In that case, the government increases the cyclically adjusted primary balance by raising taxes or reducing spending as a response to higher indebtedness. This is considered sustainable behavior.

On the other hand, fiscal policy is considered countercyclical when the estimated coefficient next to the output gap is positive (β₂ > 0). As a response to the increase in the output gap, the government increases cyclically adjusted primary balance, thus smoothing the business cycle. We check the robustness of our results by changing the definition of the output gap between the HP, Hamilton, and SVAR gaps. 

Our model includes richer dynamics than initially proposed by Bohn (1998) by adding lagged cyclically adjusted primary balance in the equation, which it is in line with (Cassou, Shadmani and Vázquez, 2017). 

The main novelty of the paper is that we allow for the asymmetric behavior of fiscal policy. To capture such asymmetric behavior, we use two different nonlinear or switching models. We use the threshold autoregression model and Markov switching model with time-varying transition probabilities. 

(6)

We use the output gap as a threshold variable to assess fiscal asymmetry, where the upper and lower regimes are typically interpreted as expansionary and recessionary regimes. They can also be related to good and bad economic times. We are looking for the asymmetric behavior between regimes. Asymmetric effects are found when β₁₁ ≠ β₂₁ which implies that fiscal sustainability depends on the regime, and when β₁₂ ≠ β₂₂ which implies that fiscal cyclicality depends on the regime. 

We also use a two-state Markov switching model with transition probability dependent on the observable output gap (see Filardo, 1994), which is comparable to the threshold autoregression model. The Markov switching model is described as follows:

(7)

where s_t is the state variable that allows for two unobservable regimes. All independent variables are allowed to switch between states. States are determined by the transition matrix and transition probabilities p_ij given by:

(8)

Transitional probabilities pi_j depend on the observable variable q_t based on the logistic function of the following form:

(9)

This type of model is proposed by Filardo (1994), and it is used in a similar application of public debt sustainability by Cassou, Shadmani and Vázquez (2017). Again, asymmetric behavior is found when coefficients β₁ or β₂ are different across regimes. 

4 Results

4.1 Fiscal policy cyclicality and sustainability

The initial results obtained from the rolling regression show a potential structural change in the fiscal response function, just as shown in Gosh et al. (2011). Such results motivate the use of more sophisticated models, such as threshold regression and Markov switching models, to assess the potential asymmetric behavior of fiscal policy over the business cycle. 

From the aspect of public debt sustainability, linear models indicate the sustainable reaction of fiscal policymakers in Croatia, given that the increase in the public debt-GDP ratio results in an increase in the cyclically adjusted primary balance- GDP ratio. This is in line with the results of empirical research on fiscal sustainability in Croatia (Banić, 2020), in which the dependent variable is the primary balance. Furthermore, linear models indicate the countercyclical behavior of fiscal policy in Croatia with regard to the positive and significant sign of the lagged output gap. This result is robust to different measures of the output gap, except the SVAR model, which suggests acyclical fiscal policy, as indicated by the insignificant coefficient next to the output gap. 

It is important to emphasize the distinction between the two regimes in the TR and MS models. Intuitively, for TR models, if the value of the output gap is above the threshold, the upper regime (UP) can be related to periods of expansion, and if the value of the output gap is below the threshold, the lower regime (DOWN) can be related to periods of recession. For MS models, the regimes are estimated depending on the probability of the unobserved state variable, with the regimes aligned with business cycles to relate them to expansion and recession periods. For simplicity, we still refer to them as upper and lower regimes (UP and DOWN) to relate with the TR model, even though the models differ. Cassou, Shadmani and Vázquez (2017) emphasize the difference between regime identification in TR and MS models, which may lead to different results. Namely, in TR models, the same level of innovation volatility above and below the threshold is assumed, while in MS models it is not, which is why the volatility in each regime is analyzed. In each of the endogenously identified regimes, we measure fiscal sustainability and cyclicality by analyzing how the cyclically adjusted primary balance responds to changes in lagged public debt and the output gap. 

4.2 Robustness check

2017

2018

4.3 Discussion

2010

2020

2020

2018

2018

5 Conclusion

1998

Funding

Notes

2010

2018

* The views expressed in this paper are not necessarily the views of the Croatian National Bank. 

** The authors would like to thank two anonymous referees for their useful comments and suggestions.

¹ Reinhart and Rogoff (2010) also argue that public debt higher than 90% of GDP can have a negative effect on economic growth. However, this argument became controversial and was often rejected by subsequent research, see Arčabić et al. (2018). 

Disclosure statement

References

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2. Arčabić, V., 2015. Djelovanje neizvjesnosti na bankarsko tržište u Republici Hrvatskoj. EFZG working paper series, (3), pp. 1-34.


3. Arčabić, V., 2018. Fiscal convergence and sustainability in the European Union. Public Sector Economics, 42(4), pp. 353-380 [CrossRef]


4. Baker, S. R., Bloom, N. and Davis, S. J., 2021. Economic policy uncertainty index.


5. Balassone, F., Francese, M. and Zotteri, S., 2010. Cyclical asymmetry in fiscal variables in the EU. Empirica, 37(4), pp. 381-402 [CrossRef]


6. Banić, F., 2020. Stohastička Analiza Javnog Duga: Primjer Hrvatske. In: J. Tica and K. Bačić, eds. Ekonomska politika u 2021. godini - Hrvatska poslije pandemije, 28(10), pp. 291-320.


7. Berti, K. [et al.], 2016. Fiscal Reaction Functions for European Union Countries. European Economy Discussion Papers, 28.


8. Blanchard, O. J. and Quah, D., 1989. The Dynamic Effects of Aggregate Demand and Supply Disturbances. The American Economic Review, 79(4), pp. 655-673.


9. Bohn, H., 1998. The behavior of U.S. public debt and deficits. Quarterly Journal of Economics, 113(3), pp. 949-963 [CrossRef]


10. Cassou, S. P., Shadmani, H. and Vázquez, J., 2017. Fiscal policy asymmetries and the sustainability of US government debt revisited. Empirical economics, 53(3), pp. 1193-1215 [CrossRef]


11. Checherita-Westphal, C. and Žďárek, V., 2017. Fiscal reaction function and fiscal fatigue: evidence for the euro area. Working paper series, No. 2036.


12. Cogley, T. and Nason, J. M., 1995. Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research. Journal of Economic Dynamics and control, 19(1-2), pp. 253-278 [CrossRef]


13. Dalić, M., 2013. Cyclical properties of fiscal policy in new member states of the EU. Post-Communist Economies, 25(3), pp. 289-308 [CrossRef]


14. Deskar-Škrbić, M. and Grdović Gnip, A., 2020. Obilježja fiskalne politike u Hrvatskoj. In G. Družić [et al.], eds. Održivost javnih financija na putu u monetarnu uniju. Zagreb: HAZU : Ekonomski fakultet, pp. 121-139.


15. Deskar-Škrbić, M. and Raos, V., 2018a. Karakter fiskalne politike i politička ekonomija fiskalne konsolidacije u Hrvatskoj u post-kriznom razdoblju. EFZG working paper series, No. 2.


16. Filardo, A. J., 1994. Business-cycle phases and their transitional dynamics. Journal of Business & Economic Statistics, 12(3), pp. 299-308 [CrossRef]


17. Furlanetto, F. [et al.], 2020. Norges Bank Output Gap Estimates: Forecasting Properties, Reliability and Cyclical Sensitivity. Norges Bank Research Working Paper, No. 7.


18. Galí, J. and Perotti, R., 2003. Fiscal policy and monetary integration in Europe. Economic Policy, 18(37), pp. 533-572 [CrossRef]


19. Gosh, A. R. [et al.], 2011. Fiscal fatigue, fiscal space and debt sustainability in advanced economies. NBER Working Paper, No. 16782 [CrossRef]


20. Hamilton, J. D., 2018. Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics, 100(5), pp. 831-843 [CrossRef]


21. Hodrick, R. J. and Prescott, E. C., 1997. Postwar US business cycles: an empirical investigation. Journal of Money, Credit, and Banking, 29(1), pp. 1-16  [CrossRef]


22. Lenza, M. and Primiceri, G. E., 2020. How to Estimate a VAR after March 2020. NBER Working paper, No. 27771 [CrossRef]


23. Piergallini, A. and Postigliola, M., 2012. Fiscal policy and public debt dynamics in Italy, 1861 – 2009. CEIS Research Paper, No. 248 [CrossRef]


24. Reinhart, C. M. and Rogoff, K. S., 2010. Growth in a Time of Debt. American economic review, 100(2), pp. 573-78 [CrossRef]


25. Sorić, P. and Lolić, I., 2017. Economic uncertainty and its impact on the Croatian economy. Public Sector Economics, 41(4), pp. 443-477 [CrossRef]


26. Švaljek, S., Vizek, M. and Mervar, A., 2009. Ciklički prilagođeni proračunski saldo: primjer Hrvatske. Radni materijali EIZ-a, (1), pp. 5-51.

Arčabić, V. [et al.], 2018. Public debt and economic growth conundrum: Nonlinearity and inter-temporal relationship. Studies in Nonlinear Dynamics & Econometrics, 22(1) [CrossRef]

Arčabić, V., 2015. Djelovanje neizvjesnosti na bankarsko tržište u Republici Hrvatskoj. EFZG working paper series, (3), pp. 1-34.

Arčabić, V., 2018. Fiscal convergence and sustainability in the European Union. Public Sector Economics, 42(4), pp. 353-380 [CrossRef]

Baker, S. R., Bloom, N. and Davis, S. J., 2021. Economic policy uncertainty index.

Balassone, F., Francese, M. and Zotteri, S., 2010. Cyclical asymmetry in fiscal variables in the EU. Empirica, 37(4), pp. 381-402 [CrossRef]

Banić, F., 2020. Stohastička Analiza Javnog Duga: Primjer Hrvatske. In: J. Tica and K. Bačić, eds. Ekonomska politika u 2021. godini - Hrvatska poslije pandemije, 28(10), pp. 291-320.

Berti, K. [et al.], 2016. Fiscal Reaction Functions for European Union Countries. European Economy Discussion Papers, 28.

Blanchard, O. J. and Quah, D., 1989. The Dynamic Effects of Aggregate Demand and Supply Disturbances. The American Economic Review, 79(4), pp. 655-673.

Bohn, H., 1998. The behavior of U.S. public debt and deficits. Quarterly Journal of Economics, 113(3), pp. 949-963 [CrossRef]

Cassou, S. P., Shadmani, H. and Vázquez, J., 2017. Fiscal policy asymmetries and the sustainability of US government debt revisited. Empirical economics, 53(3), pp. 1193-1215 [CrossRef]

Checherita-Westphal, C. and Žďárek, V., 2017. Fiscal reaction function and fiscal fatigue: evidence for the euro area. Working paper series, No. 2036.

Cogley, T. and Nason, J. M., 1995. Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research. Journal of Economic Dynamics and control, 19(1-2), pp. 253-278 [CrossRef]

Dalić, M., 2013. Cyclical properties of fiscal policy in new member states of the EU. Post-Communist Economies, 25(3), pp. 289-308 [CrossRef]

Deskar-Škrbić, M. and Grdović Gnip, A., 2020. Obilježja fiskalne politike u Hrvatskoj. In G. Družić [et al.], eds. Održivost javnih financija na putu u monetarnu uniju. Zagreb: HAZU : Ekonomski fakultet, pp. 121-139.

Deskar-Škrbić, M. and Raos, V., 2018a. Karakter fiskalne politike i politička ekonomija fiskalne konsolidacije u Hrvatskoj u post-kriznom razdoblju. EFZG working paper series, No. 2.

Filardo, A. J., 1994. Business-cycle phases and their transitional dynamics. Journal of Business & Economic Statistics, 12(3), pp. 299-308 [CrossRef]

Furlanetto, F. [et al.], 2020. Norges Bank Output Gap Estimates: Forecasting Properties, Reliability and Cyclical Sensitivity. Norges Bank Research Working Paper, No. 7.

Galí, J. and Perotti, R., 2003. Fiscal policy and monetary integration in Europe. Economic Policy, 18(37), pp. 533-572 [CrossRef]

Gosh, A. R. [et al.], 2011. Fiscal fatigue, fiscal space and debt sustainability in advanced economies. NBER Working Paper, No. 16782 [CrossRef]

Hamilton, J. D., 2018. Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics, 100(5), pp. 831-843 [CrossRef]

Hodrick, R. J. and Prescott, E. C., 1997. Postwar US business cycles: an empirical investigation. Journal of Money, Credit, and Banking, 29(1), pp. 1-16  [CrossRef]

Lenza, M. and Primiceri, G. E., 2020. How to Estimate a VAR after March 2020. NBER Working paper, No. 27771 [CrossRef]

Piergallini, A. and Postigliola, M., 2012. Fiscal policy and public debt dynamics in Italy, 1861 – 2009. CEIS Research Paper, No. 248 [CrossRef]

Reinhart, C. M. and Rogoff, K. S., 2010. Growth in a Time of Debt. American economic review, 100(2), pp. 573-78 [CrossRef]

Sorić, P. and Lolić, I., 2017. Economic uncertainty and its impact on the Croatian economy. Public Sector Economics, 41(4), pp. 443-477 [CrossRef]

Švaljek, S., Vizek, M. and Mervar, A., 2009. Ciklički prilagođeni proračunski saldo: primjer Hrvatske. Radni materijali EIZ-a, (1), pp. 5-51.