RE-ANALYSIS OF THE EU PUBLIC DEBT CRISES WITH NARX

Abstract

Purpose- This paper employs the public debt equation of motion, which covers variables that represent a country's competitiveness, such as past public debt, GDP, external balance, real exchange rate, real interest, and inflation, to estimate the public debt of Southern EU countries (Greece, Ireland, Italy, Portugal, and Spain). The paper is designed to test whether the public debt equation of motion (see Croce and Ramon, 2003; IMF, 2013; Chirwa and Odhiambo, 2018), which is characterized by significant variables representing competitiveness in macroeconomics, can statistically account for the public debt of Southern EU countries after the monetary union period including the EU public debt crisis. Consequently, based on the findings, it will be determined whether the competitiveness problems of Southern EU countries are important in the EU public debt crisis. Methodology- The analysis is performed with the nonlinear autoregressive network with exogenous inputs (NARX) with quarterly data for the period from 2005Q1 to 2021Q4. In NARX, which is a dynamic non-parametric neural network used in time series analysis, the prediction performance of the model is more robust than other neural network models, as the gradient descent approaches the local minimum perfectly (see Lin et al., 1996; Gao and Er, 2005; Diaconescu, 2008). However, it is important to define the parameters correctly in NARX to obtain effective results. In the study, parameters are defined according to the minimum Mean Squared Error values. The feedback Levenberg-Marquardt (LM) algorithm, which produces fast and effective results, is used as the training algorithm. The performance of the training algorithm for robustness is compared with testing and validation. Findings- The analysis results reveal that public debt in Southern EU countries is statistically explained by the public debt equation of motion with a confidence ratio of over 95%. Conclusion- This result implies that the public debt problem in Southern EU countries is associated with their competitiveness (see also Hall and Soskice, 2001; Dallago and Guglielmetti, 2011; Hall, 2012; Lane, 2012; Gros, 2012; Iversen et al., 2016; De Ville and Vermeiven, 2016; Frieden and Walter, 2017). In addition, the analysis goes beyond parametric analyzes that relate economic growth or a few variables with public debt and reveals the importance of inclusive variables and non-parametric analyzes in the estimation of public debt.

Keywords

• EU public debt crises
• Southern EU countries
• NARX
• competitiveness problems

References

1. Chirwa, T.G., and Odhiambo, N.M., (2018). The determinants of public debt in the euro area: A panel ARDL approach, Unisa Economic Research Working Paper Series, No.02/2018.
2. Croce, E., and Ramon, H.V.J., (2003). Assessing fiscal sustainability: A cross-country comparison, IMF Working Paper, No.WP/03/145.
3. Dallago B., and Guglielmetti, C., (2011). The eurozone crisis: Institutional setting, structural vulnerability, and policies. Openloc Working Paper Series, No.WP12/2011.
4. De Ville, F., and Vermeiren, M., (2016). The eurozone crisis and the rise of China in the global monetary and trading system: The political economy of an asymmetric shock. Comparative European Politics, 14(5), 572-603.
5. Diaconescu, E., (2008). The Use of NARX neural networks to predict chaotic time series. WSEAS Transactions on Computer Research, 3(3), 182-191.
6. Frieden, J.A., and Walter, S., (2017). Understanding the political economy of the eurozone crisis. The Annual Review of Political Science, 20, 371-390.
7. Gao, Y.E., and Meng, J., (2005). NARMAX time series model prediction: Feedforward and recurrent fuzzy neural network approach. Fuzzy Sets and Systems, 150(2), 331-350.
8. Gros, D., (2012). Macroeconomic imbalances in the euro area: Symptom or cause of the crisis? CEPS Policy Briefs Centre for European Policy Studies, No.266.

9. Hall, P.A., and Soskice, D., (2001). Varieties of capitalism: The institutional foundations of comparative advantage, Oxford University Press, Oxford.
10. Hall, P.A., (2012). The economics and politics of the euro crisis. German Politics, 21(4), 355-371.
11. Lane P.R., (2012). The european sovereign debt crisis. Journal of Economic Perspectives, 26(3), 49-68.
12. Lin, T., Horne, B.G., Tino, P., and Giles, L.C., (1996). Learning long-term dependencies in NARX recurrent neural networks, IEEE Transactions on Neural Network, 7(6), 1329-1351.
13. IMF, (2013). Staff guidance note for public debt sustainability analysis in market access countries, strategy, policy and review department, Prepared by the Strategy, Policy, and Review Department in Collaboration with the Fiscal Affairs Department.
14. Iversen, T., Soskice, D., and Hope, D., (2016). The eurozone and political economic institutions. Annual Review of Political Science, 19(1), 163-185.