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Precios de exportación de gas natural para Bolivia: Modelación y pooling de pronósticos
[Bolivian natural gas export prices: Modeling and forecast pooling]

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  • Aguilar, Ruben
  • Valdivia, Daney

Abstract

The boom of commodity prices was affected by the last economic crisis. The importance of these prices - forecasting – for small and developing countries becomes an important factor in the structure of their balance sheets. In this context, we apply a pooling of different projections methods for fuel prices which are the determinants of natural gas export prices under each contract. The first three forecast methods of these fuels are developed in a short run model where in its dynamic structure is nested the long-term relationship between WTI and fuel prices and the fourth method is a univariate model by its components. The oil path price for the first three projections are also developed under three approaches: i) a GARCH model, ii) WTI future prices and iii) a dynamic GARCH model weighted by the forecast of global oil supply and only with reference purposes we made an ARIMA projection model by components. The pool of projections permits us to evaluate gas export prices ex post. We conclude that the pooling of projections report best statistical properties.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 35485.

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Date of creation: 10 Oct 2011
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Handle: RePEc:pra:mprapa:35485

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Keywords: econometrics and statistical methods; energy and macroeconomics;

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  1. Coulson, N.E. & Robins, R.P., 1989. "Forecast Combination In A Dynamic Setting," Papers 8-88-4, Pennsylvania State - Department of Economics.
  2. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
  3. Elkin Castaño & Luis Fernando Melo, . "Métodos de Combinación de Pronósticos: Una Aplicación a la Inflación Colombiana," Borradores de Economia 109, Banco de la Republica de Colombia.
  4. Deutsch, Melinda & Granger, Clive W. J. & Terasvirta, Timo, 1994. "The combination of forecasts using changing weights," International Journal of Forecasting, Elsevier, vol. 10(1), pages 47-57, June.
  5. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
  6. Meyler, Aidan & Kenny, Geoff & Quinn, Terry, 1998. "Forecasting irish inflation using ARIMA models," MPRA Paper 11359, University Library of Munich, Germany.
  7. Ahumada, Hildegart A. & Garegnani, Maria Lorena, 2012. "Forecasting a monetary aggregate under instability: Argentina after 2001," International Journal of Forecasting, Elsevier, vol. 28(2), pages 412-427.
  8. Vega, Marco, 2003. "Reportando la distribución de la proyección de inflación," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 10.
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