Spatial Growth Regressions for the convergence analysis of renewable energy consumption in Europe
DOI:
https://doi.org/10.6092/issn.1973-2201/3984Keywords:
renewable energy consumption, convergence analysis, β-convergence model, Spatial Growth Regressions, conditional autoregressive processesAbstract
In recent years there has been an increasing awareness on problems related to the economic growth and on the conditions under which some socio-economic variables measured on European countries tend to converge over time towards a common level. This paper is concerned with the use of energy from renewable sources and considers the extent to which EU countries meet the binding commitment to reach a fifth of energy consumption from renewable sources by 2020. By discussing empirical results on the economic growth pattern of 28 countries in the period 1995-2010, we make use of several spatial growth regression models. We show that the proposed models are able to capture the complexity of the phenomenon including the possibility of estimating sitespecific convergence parameters and the identification of convergence clubs.
References
G. ARBIA (2006). Spatial Econometrics. Statistical Foundations and Applications to Regional Convergence. Springer-Verlag, Berlin, New York.
C. AZARIADIS, A. DRAZEN (1990). Threshold externalities in economic development. Quarterly Journal of Economics, 105, no. 2, pp. 501–526.
S. BANERJEE, B. CARLIN, A. GELFAND (2004). Hierarchical modeling and analysis for spatial data. Chapman & Hall/CRC, Boca Raton: Florida.
R. J. BARRO, X. SALA-I MARTIN (1991). Convergence across states and regions. Brookings Papers on Economic Activity, 1, pp. 107–182.
R. J. BARRO, X. SALA-I MARTIN (1992). Convergence. Journal of Political Economy, 100, no. 2, pp. 223–251.
R. J. BARRO, X. SALA-I MARTIN (1995). Economic Growth. McGraw Hill, Boston, MA.
C. BRUNDSON, A. FOTHERINGHAM,M. CHARLTON (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28, no. 4, pp. 281–298.
J. CRESPO CUARESMA, M. FELDKIRCHER (2013). Spatial filtering, model uncertainty and the speed of income convergence in Europe. Journal of Applied Econometrics, 28, no. 4, pp. 720–741.
N. CRESSIE (1993). Statistics for Spatial Data. Revised edition. Wiley, New York.
S. DURLAUF, P. JOHNSON (1995). Multiple regimes and cross-country growth behaviour. Journal of Applied Econometrics, 10, no. 4, pp. 365–384.
M. FISCHER, C. STIRBÖCK (2006). Pan-European regional income growth and clubconvergence. insights from a spatial econometric perspective. The Annals of Regional Science, 40, no. 4, pp. 693–721.
S. FRÜHWIRTH-SCHNATTER (2006). Finite Mixture and Markov Switching Models. Springer, Berlin.
M. GÄCTHER, E. THEURL (2011). Health status convergence at the local level: Empirical evidence from Austria. International Journal for Equity in Health, 10, no. 34.
O. GALOR (1996). Convergence inferences from theoretical models. Economic Journal, 106, no. 437, pp. 1056–1069.
A. GELMAN (1996). Inference and monitoring convergence. In R. GILKS, SPEIGELHALTER (eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall, New York.
J.GEWEKE (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In J.M. BERNARDO, J. BERGER, A. P. DAWID, J. F.M. SMITH (eds.), Bayesian Statistics 4, Oxford University Press, Oxford, pp. 169–193.
W. GILKS, S. RICHARDSON, D. SPEIGELHALTER (1996). Markov Chain Monte Carlo in Practice. Chapman & Hall, New York.
P. HALMAI, V. VASARY (2010). Real convergence in the new member states of the European Union (shorter and longer term prospects). The European Journal of Comparative Economics, 7, no. 1, pp. 229–253.
M. HURN, A. JUSTEL, C. ROBERT (2003). Estimating mixtures of regressions. Journal of Computational and Graphical Statistics, 12, no. 1, pp. 1–25.
G. JONES, M. HARAN, B. CAFFO, R. NEATH (2006). Fixed-width output analysis for Markov Chain Monte Carlo. Journal of the American Statistical Association, 101, no. 476, pp. 1537–1547.
K. KEREM, T. PUSS, M. VIIES, R. MALDRE (2008). Health and convergence of health care expenditure in EU. International Business and Economics Research Journal, 7, no. 3, pp. 29–43.
B. LIDDLE (2009). Electricity intensity convergence in IEA/OECD countries: Aggregate and sectoral analysis. Energy Policy, 37, no. 4, pp. 1470–1478.
J.-M.MARIN, C. ROBERT (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New York.
J. S.MATHUNJWA, J. TEMPLE (2007). Convergence behaviour in exogenous growth models. Bristol Economics Discussion Papers 06/590, Department of Economics University of Bristol.
D.MCMILLEN, J.MCDONALD (1997). A nonparametric analysis of employment density in a polycentric city. Journal of Regional Science, 37, no. 4, pp. 591–612.
A. NIEBUHR (2001). Convergence and the effects of spatial interaction. Jahrbuch für Regionalwissenschaft, 21, no. 2, pp. 113–133.
S. J. REY, M. J. JANIKAS (2005). Regional convergence, inequality, and space. Journal of Economic Geography, 5, no. 2, pp. 155–176.
S. RICHARDSON, P. J.GREEN (1997). On bayesian analysis ofmixtures with an unknown number of components (with discussion). Journal of the Royal Statistical Society, Series B, 59, no. 4, pp. 731–792.
S. R. SAIN, N. CRESSIE (2007). A spatial model for multivariate lattice data. Journal of Econometrics, 140, pp. 226–259.
D. SPIEGELHALTER, N. BEST, B. CARLIN, A. LINDE (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B, 64, pp. 583–639.
M. STEPHENS (2000). Bayesian methods for mixtures of normal distributions. an alternative to reversible jump methods. The Annals of Statistics, 28, no. 1, pp. 40–74.
J. WHITTAKER (1990). Graphical Models in Applied Multivariate Statistics. Wiley, New York.
J. WOLSZCZAC-DERLACZ (2009). Price convergence in the EU - an aggregate and disaggregate approach. International Economics and Economic Policy, 5, no. 1, pp. 25–47.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2013 Statistica
This journal is licensed under a Creative Commons Attribution 3.0 Unported License (full legal code).
Authors accept to transfer their copyrights to the journal.
See also our Open Access Policy.
