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Dynamic Panel Modeling of Climate Change

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Abstract

We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess the use of various estimation methodologies, including standard dynamic panel regression and cointegration techniques that have been used in earlier research. The findings reveal massive bias in system GMM estimation of the dynamic panel regression parameters, which arise from fixed effect heterogeneity across individual station level observations. Difference GMM and Within Group (WG) estimation have little bias and WG estimation is recommended for practical implementation of dynamic panel regression with highly disaggregated climate data. Intriguingly from an econometric perspective and importantly for global policy analysis, it is shown that despite the substantial differences between the estimates of the regression model parameters, estimates of global transient climate sensitivity (of temperature to a doubling of atmospheric CO {2}) are robust to the estimation method employed and to the specific nature of the trending mechanism in global temperature, radiation, and CO {2}.

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  • Peter C.B. Phillips, 2018. "Dynamic Panel Modeling of Climate Change," Cowles Foundation Discussion Papers 2150, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2150
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    1. Maurice J. G. Bun & Frank Windmeijer, 2010. "The weak instrument problem of the system GMM estimator in dynamic panel data models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 95-126, February.
    2. Park, Joon Y. & Phillips, Peter C.B., 1989. "Statistical Inference in Regressions with Integrated Processes: Part 2," Econometric Theory, Cambridge University Press, vol. 5(1), pages 95-131, April.
    3. Magnus, Jan R. & Melenberg, Bertrand & Muris, Chris, 2011. "Global Warming and Local Dimming: The Statistical Evidence," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 452-464.
    4. Phillips, Peter C. B., 2018. "Dynamic Panel Anderson-Hsiao Estimation With Roots Near Unity," Econometric Theory, Cambridge University Press, vol. 34(2), pages 253-276, April.
    5. Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
    6. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    7. Robert Kaufmann & Heikki Kauppi & Michael Mann & James Stock, 2013. "Does temperature contain a stochastic trend: linking statistical results to physical mechanisms," Climatic Change, Springer, vol. 118(3), pages 729-743, June.
    8. Kruiniger, Hugo, 2009. "Gmm Estimation And Inference In Dynamic Panel Data Models With Persistent Data," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1348-1391, October.
    9. Hayakawa, Kazuhiko, 2015. "The Asymptotic Properties Of The System Gmm Estimator In Dynamic Panel Data Models When Both N And T Are Large," Econometric Theory, Cambridge University Press, vol. 31(3), pages 647-667, June.
    10. Park, Joon Y. & Phillips, Peter C.B., 1988. "Statistical Inference in Regressions with Integrated Processes: Part 1," Econometric Theory, Cambridge University Press, vol. 4(3), pages 468-497, December.
    11. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    Cited by:

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    2. Robert K. Kaufmann & Felix Pretis, 2023. "An empirical estimate for the snow albedo feedback effect," Climatic Change, Springer, vol. 176(8), pages 1-20, August.
    3. Bre, Facundo & Lamberts, Roberto & Flores-Larsen, Silvana & Koenders, Eduardus A.B., 2023. "Multi-objective optimization of latent energy storage in buildings by using phase change materials with different melting temperatures," Applied Energy, Elsevier, vol. 336(C).
    4. Anasis, John G. & Khalil, Mohammad Aslam Khan & Butenhoff, Christopher & Bluffstone, Randall & Lendaris, George G., 2019. "Optimal energy resource mix for the US and China to meet emissions pledges," Applied Energy, Elsevier, vol. 238(C), pages 92-100.

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    More about this item

    Keywords

    Climate modeling; Cointegration; Difference GMM; Dynamic panel; Spatio-temporal modeling; System GMM; Transient climate sensitivity; Within group estimation;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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