Acta Universitatis Danubius. Œconomica, Vol. 11, No. 3
Abstract
The effects of the economic crisis continue to impact the world economy even if the most
difficult period of the crisis seems to have passed. In this context, the analysis of the economic
performance becomes stringent in that it not only allows for the identification of the economic
environment, but also due to the fact that it brings value by determining the automated correction of
any decision or direction in the difficult economic context of today. The paper represents a study of
some of the main macroeconomic performance indicators for the European Union countries, such as:
economic growth, current account balance, labour productivity, employment and average net earnings.
Based on a cluster analysis we identified the position of each E.U. member state via an economic
performance view and a country level particularization was then achieved. After grouping the countries
into two clusters based on their economic performances, we built two distinct equations using panel
data models that could explain the economic growth variations for both the case of highly performing
and less performing E.U. countries. The results of the analysis actually incorporate some main
components that will help formulate economic growth measures, employment and labour productivity.
References
propagation into Romanian economy. Romanian Journal of Regional Science, vol. 6, no 1, pp. 41-52.
Andreica, M.E. & Andreica, M. (2014). Forecasts of Romanian industry employment using simulations
and panel data models. Romanian Journal of Economic Forecasting –XVII (2), pp. 130-140.
Aparaschivei, L. (2012). Analysis of the Romanian employment rate: A panel data approach.
Theoretical and Applied Economics, Vol. XVIII, No 7 (572), pp. 13-24.
Baum, C. F. (2001). Residual diagnostics for cross-section time series regression models. The Stata
Journal 1, pp. 101–104.
Davidescu, A.A. (2014). Investigating the impact of unemployment rate on the Romanian shadow
economy. A complex approach based on ARDL and SVAR analysis, Romanian Journal of Economic
Forecasting, issue 4, pp. 109-127.
Drukker, D.M. (2003). Testing for serial correlation in linear panel-data models. The Stata Journal,
Vol. 3, No. 2, pp. 168–177.
Gros, D. (2012). Macroeconomic Imbalances in the Euro Area: Symptom or cause of the crisis?. CEPS
Policy Brief, 266.
Hoechle, D. (2007). Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence.
The Stata Journal, Vol. 7, No. 3, pp. 281-312.
Holinski, N., Kool, C. & Muysken J. (2012). Persistent Macroeconomic Imbalances in the Euro Area:
Causes and Consequences. Federal Reserve Bank of St. Louis Review.
Ileanu B.V. & Tanasoiu O.E. (2008). Factors of the Earning Functions and their Influence on the
Intellectual Capital of an Organization. Journal of Applied Quantitative Methods, 3(4), pp. 366-374.
Jaumotte, F. & Sodsriwiboon P. (2010). Current Account Imbalances in the Southern Euro Area. IMF
Working Paper WP/10/139.
Lazar, I. & Andreica, M.E. (2013). Current account balance, private debt and euro area sovereign debt
crisis: a comparison of North and South. Romanian Journal of Economics, 37, issue 2(46), pp. 40-52.
Matei, M., Zamfir, A. & Lungu, E. (2014). A Nonparametric Approach for Wage Differentials of
Higher Education Graduates. Procedia - Social and Behavioral Sciences, 109, pp. 188–192.
Rahman, J. (2008). Current Account Developments in New Member States of the European Union:
Equilibrium, Excess, and EU-Phoria. IMF Working Paper WP/08/92.
Orszaghova, L., Savelin, L. & Schudel, W. (2013). External Competitiveness of EU Candidate
Countries. Occasional Paper Series of the European Central Bank, 141.
Spahn, P. (2013). Competitiveness, Adjustment and Macroeconomic Risk Management in the
Eurozone, ROME Discussion Paper Series, 16.
Downloads
Published
How to Cite
Issue
Section
License
The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.
