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Forecasting U.S. State-Level Carbon Dioxide Emissions

Author

Listed:
  • J. Wesley Burnett

    (College of Charleston)

  • Xueting Zhao

    (West Virginia University)

Abstract

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.

Suggested Citation

  • J. Wesley Burnett & Xueting Zhao, 2014. "Forecasting U.S. State-Level Carbon Dioxide Emissions," The Review of Regional Studies, Southern Regional Science Association, vol. 44(3), pages 223-240, Winter.
  • Handle: RePEc:rre:publsh:v44:y:2014:i:3:p:223-240
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    References listed on IDEAS

    as
    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.
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    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.
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    10. David L. Ryan & André Plourde, 2009. "Empirical Modelling of Energy Demand," Chapters,in: International Handbook on the Economics of Energy, chapter 6 Edward Elgar Publishing.
    11. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
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    More about this item

    Keywords

    spatial panel data econometrics; forecasting; carbon dioxide emissions;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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