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Quasi-experimental estimates of the transient climate response using observational data

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  • Giselle Montamat

    (Harvard University)

  • James H. Stock

    (Harvard University and the National Bureau of Economic Research)

Abstract

The transient climate response (TCR) is the change in global mean temperature at the time of an exogenous doubling in atmospheric CO2 concentration increasing at a rate of 1% per year. A problem with estimating the TCR using observational data is that observed CO2 concentrations depend in turn on temperature. Therefore, the observed concentration data are endogenous, potentially leading to simultaneous causation bias of regression estimates of the TCR. We address this problem by employing instrumental variables regression, which uses changes in radiative forcing external to earth systems to provide quasi-experiments that can be used to estimate the TCR. Because the modern instrumental record is short, we focus on decadal fluctuations (up to 30-year changes), which also mitigate some statistical issues associated with highly persistent temperature and concentration data. Our estimates of the TCR for these shorter horizons, normalized to be comparable to the traditional 70-year TCR, fall within the range in the IPCC-AR5 and provide new observational confirmation of model-based estimates.

Suggested Citation

  • Giselle Montamat & James H. Stock, 2020. "Quasi-experimental estimates of the transient climate response using observational data," Climatic Change, Springer, vol. 160(3), pages 361-371, June.
    • Handle: RePEc:spr:climat:v:160:y:2020:i:3:d:10.1007_s10584-019-02589-1
    DOI: 10.1007/s10584-019-02589-1
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    References listed on IDEAS

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    1. José Luis Montiel Olea & Carolin Pflueger, 2013. "A Robust Test for Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 358-369, July.
    2. Bruns, Stephan B. & Csereklyei, Zsuzsanna & Stern, David I., 2020. "A multicointegration model of global climate change," Journal of Econometrics, Elsevier, vol. 214(1), pages 175-197.
    3. Robert K. Kaufmann & David I. Stern, 1997. "Evidence for human influence on climate from hemispheric temperature relations," Nature, Nature, vol. 388(6637), pages 39-44, July.
    4. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1130-1164, December.
    5. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice Rejoinder," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 574-575, October.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    7. Graham Elliott, 1998. "On the Robustness of Cointegration Methods when Regressors Almost Have Unit Roots," Econometrica, Econometric Society, vol. 66(1), pages 149-158, January.
    8. Vogelsang, Timothy J., 2012. "Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects," Journal of Econometrics, Elsevier, vol. 166(2), pages 303-319.
    9. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    10. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 541-559, October.
    11. David Stern & Robert Kaufmann, 2014. "Anthropogenic and natural causes of climate change," Climatic Change, Springer, vol. 122(1), pages 257-269, January.
    12. Pretis, Felix, 2020. "Econometric modelling of climate systems: The equivalence of energy balance models and cointegrated vector autoregressions," Journal of Econometrics, Elsevier, vol. 214(1), pages 256-273.
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    Cited by:

    1. Simon Dietz & Frederick van der Ploeg & Armon Rezai & Frank Venmans, 2021. "Are Economists Getting Climate Dynamics Right and Does It Matter?," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 8(5), pages 895-921.
    2. Liang Chen & Juan J. Dolado & Jesús Gonzalo & Andrey Ramos, 2023. "Heterogeneous predictive association of CO2 with global warming," Economica, London School of Economics and Political Science, vol. 90(360), pages 1397-1421, October.
    3. Yang, Tianle & Li, Fangmin & Du, Min & Huang, Miao & Li, Yinuo, 2023. "Impacts of alternative energy production innovation on reducing CO2 emissions: Evidence from China," Energy, Elsevier, vol. 268(C).
    4. Chen, Liang & Dolado, Juan José & Gonzalo, Jesús & Ramos Ramirez, Andrey David, 2013. "Revisiting Granger Causality of CO2 on Global Warming: a Quantile Factor Approach," DES - Working Papers. Statistics and Econometrics. WS 35531, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Philippe Goulet Coulombe & Maximilian Gobel, 2021. "On Spurious Causality, CO2, and Global Temperature," Papers 2103.10605, arXiv.org.
    6. Tobias Adrian & Nina Boyarchenko & Domenico Giannone & Ananthakrishnan Prasad & Dulani Seneviratne & Yanzhe Xiao, 2022. "800,000 Years of Climate Risk," Staff Reports 1031, Federal Reserve Bank of New York.
    7. Pretis, Felix, 2021. "Exogeneity in climate econometrics," Energy Economics, Elsevier, vol. 96(C).

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