A panel data analysis for the greenhouse effects in fifteen countries of European Union.

Giovanis, Eleftherios (2008): A panel data analysis for the greenhouse effects in fifteen countries of European Union.

Abstract

This paper examines how some factors affect the greenhouse effect of fifteen countries in European Union with fixed and random effects, while we also investigate the case of the Arch effects presentation. Finally we estimate a neural network model to examine how all the factors affect the greenhouse effect and we compare the forecasting performance with that of fixed or random panel data estimation.

• Item Type: MPRA Paper
• Language: English
• Keywords: fixed and random effects, ARCH panel effects, panel unit root, cointegration, vector autoregressive models, vector error correction, principal components, neural networks
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics

References

Baltagi B.H., 2001. Econometric Analysis of Panel Data, second Edition, Wiley, 12- 20, 31-38, 131-132

Breitung, J. 2000. The Local Power of Some Unit Root Tests for Panel Data, in Baltagi (ed.), Advances in Econometrics, Vol. 15: Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Amsterdam: JAI Press, 161-178

Carslaw, K.S., Harrison R.G. and Kirkby J. 2002. Cosmic rays, clouds, and climate. Science, 298: 1732–1737.

Filho, B.A. Callander, N. Harris, A. Kattenberg and K. Maskell (Eds), Cambridge University Press, Cambridge. pp. 572

Greene H.W. 2003. Econometric Analysis, Fifth edition, Prentice Hall, New Jersey, U.S.A , 303-305

Goodroad, L.L., and D.R. Keeney. 1984. Nitrous oxide production in aerobic soils under varying pH, temperature, and water content. Soil Biol. Biochem. 16 , 39–43.

Hansen, J. E., M. Sato, A. Lacis, R. Ruedy, I. Gegen, and E. Matthews, 1998. Climate forcings in the industrial era, Proceedings of the National Academy of Sciences, 95, 12753-12758

Hendry, D., 1986. Econometric modeling with cointegrated variables: An overview, Oxford Bulletin of Economics and Statistics 48, 51-63

Hsing, Y. 2004. Impacts of Fiscal Policy, Monetary Policy, and Exchange Rate Policy on Real GDP in Brazil: A VAR Model, Brazilian Electronic Journal of Economics 6, 1-12

Huang B.N. and Yang C.W. 2004. Industrial output and stock price revisited: An application of the multivariate indirect causality model, The Manchester School 72 (3), 347-362

Im, K. S., Pesaran, M. H., and Shin, Y. 2003. Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 115, 53-74

Houghton J.T. and Miro L.G. 1996. Climate change 1995: The science of climate change. In: Intergovernmental Panel on Climate Change. IPCC.

Karl, T.R., Heim R.R. JR. and Quayle R.G. 1991. The greenhouse effect in central north America: If not now, when? Science, 251: 1058–1061.

Ledley T.S., Sundquist E.T., Schwartz E.S., Hall D.K., Fellows J.D. and Killeen T.L. 1999. Climate Change and Greenhouse Gases. EOS 80(39), 453

Levin A., Lin C.F. and Chu C. 2002. Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties. Journal of Econometrics 108, 1-24

Mazodier, P., Trognon, A., 1978. Heteroskedasticity and stratification in error components models. Annales de l’INSEE 30–31, 451–482.

Pipatti R., 1998. Emission estimates for some acidifying and greenhouse gases and options for their control in Finland. Technical research centre of Finland

VINCENT, L.A. 1998. A technique for the identification of inhomogeneities in Canadian temperature series. J. Clim. 11: 1094–1104