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Detecting and Quantifying Structural Breaks in Climate

Author

Listed:
  • Neil R. Ericsson

    (Division of International Finance, Board of Governors of the Federal Reserve System, Washington, DC 20551, USA
    Department of Economics and H.O. Stekler Research Program on Forecasting, The George Washington University, Washington, DC 20052, USA
    Paul H. Nitze School of Advanced International Studies (SAIS), Johns Hopkins University, Washington, DC 20036, USA)

  • Mohammed H. I. Dore

    (Department of Economics, Climate Change Laboratory, Brock University, St. Catharines, ON L2S 3A1, Canada)

  • Hassan Butt

    (Department of Economics, Climate Change Laboratory, Brock University, St. Catharines, ON L2S 3A1, Canada)

Abstract

Structural breaks have attracted considerable attention recently, especially in light of the financial crisis, Great Recession, the COVID-19 pandemic, and war. While structural breaks pose significant econometric challenges, machine learning provides an incisive tool for detecting and quantifying breaks. The current paper presents a unified framework for analyzing breaks; and it implements that framework to test for and quantify changes in precipitation in Mauritania over 1919–1997. These tests detect a decline of one third in mean rainfall, starting around 1970. Because water is a scarce resource in Mauritania, this decline—with adverse consequences on food production—has potential economic and policy consequences.

Suggested Citation

  • Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    • Handle: RePEc:gam:jecnmx:v:10:y:2022:i:4:p:33-:d:984722
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    References listed on IDEAS

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