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Forecasting U.S. State-Level Carbon Dioxide Emissions

Listed author(s):
  • J. Wesley Burnett

    (College of Charleston)

  • Xueting Zhao

    (West Virginia University)

This study explores the use of spatial models in forecasting U.S. state-level carbon dioxide emissions. We compare forecasts against empirical reality using panel data models with and without spatial effects. Understanding how to predict emissions is important for designing climate change mitigation policies. To determine if spatial econometric models can help us predict emissions, it is important to test these models to see if they are a valid strategy to describe the underlying data, in the context of forecasting. We find that a non-spatial OLS estimator performs best in all out-of-sample forecasts; however, the OLS model is not statistically distinguishable from a spatial panel data model with random effects.

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Article provided by Southern Regional Science Association in its journal The Review of Regional Studies.

Volume (Year): 44 (2014)
Issue (Month): 3 (Winter)
Pages: 223-240

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Handle: RePEc:rre:publsh:v44:y:2014:i:3:p:223-240
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  1. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2012. "Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration," Journal of Econometrics, Elsevier, vol. 167(1), pages 16-37.
  2. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, 02.
  3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  4. Itkonen, Juha V.A., 2012. "Problems estimating the carbon Kuznets curve," Energy, Elsevier, vol. 39(1), pages 274-280.
  5. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 642-675, October.
  6. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
  7. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "The relative efficiencies of various predictors in spatial econometric models containing spatial lags," Regional Science and Urban Economics, Elsevier, vol. 37(3), pages 363-374, May.
  8. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Rejoinder to comments on forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 703-715, October.
  9. Burnett, J. Wesley & Bergstrom, John C. & Dorfman, Jeffrey H., 2013. "A spatial panel data approach to estimating U.S. state-level energy emissions," Energy Economics, Elsevier, vol. 40(C), pages 396-404.
  10. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
  11. Maximilian Auffhammer & Ralf Steinhauser, 2007. "The Future Trajectory Of U.S. Co," Journal of Regional Science, Wiley Blackwell, vol. 47(1), pages 47-61.
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