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Detection and attribution of climate change through econometric methods

Author

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  • Francisco Estrada

    (Universidad Nacional AutoÌ noma de MeÌ xico and Vrije Universiteit, Amsterdam)

  • Pierre Perron

    (Boston University)

Abstract

An ever growing body of evidence regarding observed changes in the climate system has been gathered over the last three decades and large modeling efforts have been carried to explore how climate may evolve during the present century (IPCC, 2001; 2007a). The impacts from both observed weather and climate endured during the 20th century and the magnitude of the potential future impacts of climate change have made this phenomenon of high interest for policy-makers and the society at large (IPCC, 2007b). Two fundamental questions arise for understanding the nature of this problem and the appropriate strategies to address it: is there a long-term warming signal in the observed climate, or is it the product of natural variability alone? if so, how much of this warming signal can be attributed to anthropogenic activities? As discussed in this review, these questions are intrinsically related to the study of the time-series properties of climate and radiative forcing variables and of the existence of common features such as secular co-movements. This paper presents a brief summary of how detection and attribution studies have evolved in the climate change literature and an overview of the time series and econometric methods that have been applied for these purposes.

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  • Francisco Estrada & Pierre Perron, "undated". "Detection and attribution of climate change through econometric methods," Boston University - Department of Economics - Working Papers Series 2013-015, Boston University - Department of Economics.
  • Handle: RePEc:bos:wpaper:wp2013-015
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    3. Chang, Yoosoon & Kaufmann, Robert K. & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2020. "Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate," Journal of Econometrics, Elsevier, vol. 214(1), pages 274-294.
    4. Myoung-Jin Um & Jun-Haeng Heo & Momcilo Markus & Donald J. Wuebbles, 2018. "Performance Evaluation of four Statistical Tests for Trend and Non-stationarity and Assessment of Observed and Projected Annual Maximum Precipitation Series in Major United States Cities," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 913-933, February.
    5. Kim, Dukpa & Oka, Tatsushi & Estrada, Francisco & Perron, Pierre, 2020. "Inference related to common breaks in a multivariate system with joined segmented trends with applications to global and hemispheric temperatures," Journal of Econometrics, Elsevier, vol. 214(1), pages 130-152.
    6. Omid Bozorg-Haddad & Mohammad Solgi & Hugo A. Loáiciga, 2017. "Investigation of Climatic Variability with Hybrid Statistical Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 341-353, January.
    7. Francisco Estrada & Richard S J Tol & Wouter J W Botzen, 2017. "Global economic impacts of climate variability and change during the 20th century," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    8. Francisco Estrada & Pierre Perron, 2019. "Saltos, tendencias y la atribución del cambio climático: un análisis de series de tiempo," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 42(83), pages 1-31.
    9. Dukpa Kim & Tatsushi Oka & Francisco Estrada & Pierre Perron, 2017. "Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures," Boston University - Department of Economics - Working Papers Series WP2017-003, Boston University - Department of Economics.
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