Dynamic Panel Modeling of Climate Change

Peter Phillips

No 2150, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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}.

Keywords: Climate modeling; Cointegration; Difference GMM; Dynamic panel; Spatio-temporal modeling; System GMM; Transient climate sensitivity; Within group estimation (search for similar items in EconPapers)
JEL-codes: C32 C33 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2018-12
New Economics Papers: this item is included in nep-agr, nep-ecm, nep-ene and nep-env
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: Dynamic Panel Modeling of Climate Change (2020)
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