Debt Uncertainty and Economic Growth in Emerging European Economies: Some Empirical Evidence

ABSTRACT

This study investigates the effects of public debt uncertainty on economic growth in 10 emerging European economies over 2000–2015 period. Public debt uncertainty reflects fiscal policy volatility and macroeconomic instability. It also creates uncertainty about the characteristics of future fiscal policy, which further causes the rise of uncertainty in household and business incomes. Increasing the risk of future incomes leads to the reduction of household consumption and corporate investments, which negatively influences economic growth. An empirical analysis of public debt uncertainty impact on economic growth is performed by time series and panel data approaches based on quarterly data. Our key result indicates the significant detrimental effect public debt uncertainty has had on the GDP growth in emerging European economies, especially during the Great Recession episode that started in 2008. Robustness of our econometric findings is confirmed by different estimation methods and model specifications.

KEYWORDS:

• Fiscal debt uncertainty
• emerging European economies
• GARCH models
• panel estimation

JEL CLASSIFICATION:

• C32
• C33
• E60
• H60
• O23

Acknowledgments

Valuable comments of an anonymous referee and Professor William Bartlett are greatly appreciated. Remaining errors are, however, our own.

Supplementary Material

Supplemental data for this article can be accessed on the publisher’s website

Notes

1. The results are not reported, but they are available upon request.

2. The method defined to estimate uncertainty by taking into account the large set of economic indicators (Jurado, Ludvigson, and Ng 2015), would be appropriate for the single country analysis if time series were available over the long span or for the panel analysis that assumed large time dimension and similar construction of indicators for all economies in the sample.

3. Adequate GARCH models were not found for data from Poland and Latvia, which is the reason these two emerging European economies were not covered by our analysis.

4. Theoretically, the two-step estimator is more efficient than is the one-step estimator, but its application to a sample with a small cross-section dimension causes the problem of either instrument proliferation or too many instruments.

5. To additionally test for the robustness of our findings we have estimated panel specifications presented in Tables 7 and 8. based on the application of the Hamilton filter (Hamilton 2018) instead of the HP filter, in order to derive single country output gap and the EU15 output gap. Model estimations based on the Hamilton filter are similar to the findings based on the HP filter. By construction, the application of the Hamilton filter assumes smaller number of observations than the HP filter which substantially reduces the number of degrees of freedom in estimation. Therefore, we opted for the implementation of the HP filter to derive output gaps when performing model estimation. The set of results based on the Hamilton filter is available upon request.

Additional information

Funding

Financial support from the The Ministry of Education, Science and Technological Development of the Republic of Serbia [Milojko Arsić - grant 179005, Zorica Mladenović - grants 179005 and 179062, and Aleksandra Nojković - grants 179005 and 179065].