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How Volatile is ENSO for Global Greenhouse Gas Emissions and the Global Economy?


Chu, Lan-Fen y McAleer, Michael (2012) How Volatile is ENSO for Global Greenhouse Gas Emissions and the Global Economy? [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 20, 2012, ] (No publicado)

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This paper analyzes two indexes in order to capture the volatility inherent in El Niños Southern Oscillations (ENSO), develops the relationship between the strength of ENSO and greenhouse gas emissions, which increase as the economy grows, with carbon dioxide being the major greenhouse gas, and examines how these gases affect the frequency and strength of El Niño on the global economy. The empirical results show that both the ARMA(1,1)-GARCH(1,1) and ARMA(3,2)-GJR(1,1) models are suitable for modelling ENSO volatility accurately, and that 1998 is a turning point, which indicates that the ENSO strength has increased since 1998. Moreover, the increasing ENSO strength is due to the increase in greenhouse gas emissions. The ENSO strengths for Sea Surface Temperature (SST) are predicted for the year 2030 to increase from 29.62% to 81.5% if global CO2 emissions increase by 40% to 110%, respectively. This indicates that we will be faced with even stronger El Nino or La Nina effects in the future if global greenhouse gas emissions continue to increase unabated.

Tipo de documento:Documento de trabajo o Informe técnico
Información Adicional:

Revised: September 2012

Palabras clave:El Niños Southern Oscillations (ENSO), Greenhouse Gas Emissions, Global Economy, Southern Oscillation Index (SOI), Sea Surface Temperature (SST), Volatility.
Materias:Ciencias Sociales > Economía > Econometría
Título de serie o colección:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
Código ID:16597

Adams, R.M., Chen, C.C., McCarl, B., Weiher, R., 1999. The economic consequences of ENSO events on agriculture, Climate Research, 13(3), 165-172.

Ahn, J.H., Kim, H.S., 2005. Nonlinear modeling of El Niño/Southern Oscillation index, Journal of Hydrologic Engineering, 10, 8-15.

Andrews, D.W.K., 1993. Tests for parameter instability and structural change with unknown change point, Econometrica, 61(4), 821-856.

Bai, J., Perron, P., 1998. Estimating and testing linear models with multiple structural changes, Econometrica, 66(1), 47-78.

Bai, J., Perron, P., 2003. Computation and analysis of multiple structural change models, Journal of Applied Econometrics, 18(1), 1-22.

Bollerslev, T., 1986. Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 307-327.

Brian, J., Berry, L., Qkulicz-Kozaryn, A., 2008. Are there ENSO signal in the macroeconomy?, Ecology Economics, 64(3), 625-633.

Brunner, A.D., 2002. El Niño and world primary commodity prices: warm water or hot air?, Review of Economics and Statistics, 84, 176-183.

Carrasco, M., Chen, X., 2002. Mixing and moment properties of various GARCH and stochastic volatility models, Econometric Theory, 18, 17-39.

Chen, C.C., McCarl, B., 2000. The value of ENSO information: Considerations of uncertainty and trade, Journal of Agricultural and Resource Economics, 25(2), 368-385.

Chen, C.C., McCarl, B., Adams, R., 2001. Economic implications of potential ENSO frequency and strength shifts, Climatic Change, 49, 147-159.

Chen, C.C., McCarl, B.A., Hill, H., 2002. An agricultural value of ENSO information under alternative phase definition, Climatic Change, 54, 305-325.

Chen, C.C., Gillig, D., McCarl, B.A., Williams, L., 2005. ENSO impacts on regional water management: Case study of the Edwards Aquifer (Texas, USA), Climate Research, 28, 175-182.

Chu, P.S., Katz, R.W., 1985. Modeling and forecasting the Southern Oscillation: A time-domain approach, Monthly Weather Review, 113, 1876-1888.

Chow, G.C., 1960. Test of equality between sets of coefficients in two linear regressions, Econometrica, 28(3), 591-605.

Divino, J.A., McAleer, M., 2009. Modelling sustainable international tourism demand to the Brazilian Amazon, Environmental Modelling and Software, 1411-1419.

Davis, M., 2001. Late victorian holocausts: El Niño Famines and the Making of the Third World, Verso, London.

Debelle, G., Stevens, G., 1995. Monetary policy goals for inflation in Australia, Reserve Bank of Australia Research Discussion Paper No. 9503.

Dickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for autoregressive time Series with a unit root, Journal of the American Statistical Association, 74, 427-431.

Dickey, D.A., Fuller, W.A., 1981. Likelihood ratio statistics for autoregressive time series with a unit root, Econometrica, 49, 1057-1072.

Dilley, M., 1997. Climatic factors affecting annual maize yields in the valley of Oaxaca, Mexico, International Journal of Climatology, 17, 1549-1557.

Dracup, J.A., Kahya, E., 1994. The relationships between U.S. streamflow and La Niña events, Water Resources Research, 30(7), 2133-2141.

Engle, R.F., 1982. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.

Glosten, L., Jagannathan, R., Runkle, D., 1992. On the relation between the expected value and volatility of nominal excess return on stocks, Journal of Finance, 46, 1779-1801.

Handler, P., Handler, E., 1983. Climatic anomalies in the tropical Pacific ocean and corn yields in the United States, Science, 220, 1155-1156.

Hansen, B.E., 2001. The new econometrics of structural change: Dating breaks in U.S. labor productivity, Journal of Economic Perspectives, 15(4), 117-128.

Intergovernmental Panel on Climate Change IPCC, 2007: Climate Change 2007: Impacts, Adaptation and Vulnerability, Cambridge: Cambridge University Press.

Kovats, R.S., Bouma, M.J., Hajat, S., Worrall, E., Haines, A., 2003. El Niño and health, Lancet, 362, 1481-1489.

Ling, S., McAleer, M., 2002a. Stationarity and the existence of moments of a family of GARCH processes, Journal of Econometrics, 106, 109-117.

Ling, S., McAleer, M., 2002b. Necessary and sufficient moment conditions for the GARCH (r,s) and asymmetric power GARCH(r,s) models, Econometric Theory, 18, 722-729.

Ling, S., McAleer, M., 2003a. Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory, 19, 278-308.

Ling, S., McAleer, M., 2003b. On adaptive estimation in nonstationary ARMA models with GARCH errors, Annals of Statistics, 31, 642-674.

Maddala, G.S., 1983. Limited-dependent and qualitative variables in Econometrics, Cambridge University Press.

McAleer, M., 2005. Automated inference and learning in modeling financial volatility, Econometric Theory, 21, 232-261.

McAleer, M., Chan, F., Marinova, D., 2007. An econometric analysis of asymmetric volatility: Theory and application to patents, Journal of Econometrics, 139, 259-284.

McBride, J.L., Nicholls, N., 1983. Seasonal relationships between Australian rainfall and the Southern Oscillation, Monthly Weather Review, 111, 1998-2004.

Moss, M.E., Pearson, C.P., McKerchar, A.I., 1994. The Southern Oscillation index as a predictor of the probability of low streamflows in New Zealand, Water Resources Research, 30(10), 2717-2723.

Naylor, R.L., Falcon, W.P., Rochberg, D., Wada, N., 2001. Using El Niño/Southern Oscillation climate data to predict rice production in Indonesia, Climate Change, 50, 255-265.

Nelson, D.B., 1990. Stationarity and persistence in the GARCH(1,1) model, Econometric Theory, 6, 318-334.

Nelson, D.B., 1991.Conditional heteroscedasticity in asset returns: A new approach, Econometrica, 59, 347-370.

Quandt, R.E., 1958. The estimation of parameters of a linear regression system obeying two separate regimes, Journal of the American Statistical Association, 53, 873-880.

Piechota, T.C., Dracup, J.A. 1996. Drought and regional hydrologic variation in the United States: Associations with the El Nino-Southern Oscillation, Water Resources Research, 32(5), 1359-1373.

Pielke, Jr. R.A., Landsea, C.W., 1999. La Niña, El Niño, and Atlantic hurricane damages in the United States, Bulletin of the American Meteorological Society, 80, 2027-2033.

Ropelewski, C.F., Halpert, M.S., 1989. Precipitation patter associated with the high index phase of the Southern Oscillation, Journal of Climate, 2(3), 268- 284.

Shephard, N., 1996. Statistical aspects of ARCH and stochastic volatility, in Time Series Models in Econometrics, Finance and Other Fields, Cox, D.R., Hinkley, D.V., Barndoff-Nielson, O.E. (eds.), London: Chapman and Hall, pp. 1–67.

Solow, A., Adams, R.F., Bryant, K.J., Legler, D., O’Brien, J., McCarl, B.A., Nayda, W., Weiher, R., 1998. The value of improved ENSO prediction to U.S. agriculture, Climatic Change, 39, 47-60.

Timmermann, A.J., Oberhuber, A., Bacher, M., Esch, M., Latif, Roeckner, E., 1999. Increased El Niño frequency in a climate model forced by future greenhouse warming, Nature, 398, 694-97.

Trenberth, K.E., 1997. The definition of El Niños, Bulletin of the American Meteorological Society, 78, 2771–2777.

Trenberth, K.E., Hoar, T.J., 1996. The 1990-1995 El Niños-Southern Oscillation event: Longest on Record, National Center for Atmospheric Research, Boulder, Colorado.

Wong, H., Li, W.K., 1997. On a multivariate conditional heteroscedastic model, Biometrika, 4, 111-123.

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Última Modificación:07 Feb 2014 09:32

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