Dynamic panel modeling of climate change
Dynamic panel modeling of climate change
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 in this model 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.
Climate modeling, Cointegration, Difference GMM, Dynamic panel, Spatio-temporal modeling, System GMM, Transient climate sensitivity, Within group estimation
1-28
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
September 2020
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
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 in this model 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.
Text
Econometric Modeling Climate ChangeA10
- Accepted Manuscript
More information
Accepted/In Press date: 21 July 2020
e-pub ahead of print date: 28 July 2020
Published date: September 2020
Additional Information:
Funding Information:
Funding: This research was funded by a Kelly Fellowship at the University of Auckland and the NSF under Grant No. SES 18-50860.
Publisher Copyright:
© 2020 by the author. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords:
Climate modeling, Cointegration, Difference GMM, Dynamic panel, Spatio-temporal modeling, System GMM, Transient climate sensitivity, Within group estimation
Identifiers
Local EPrints ID: 444577
URI: http://eprints.soton.ac.uk/id/eprint/444577
ISSN: 2225-1146
PURE UUID: 35776fb9-0000-4666-9336-d5c448fa42f3
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Date deposited: 26 Oct 2020 17:31
Last modified: 16 Mar 2024 09:38
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