Detecting and Quantifying Structural Breaks in Climate

Neil R. Ericsson (), Mohammed H. I. Dore and Hassan Butt
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Neil R. Ericsson: Division of International Finance, Board of Governors of the Federal Reserve System, Washington, DC 20551, 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

Econometrics, 2022, vol. 10, issue 4, 1-27

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.

Keywords: climate change; indicator saturation; machine learning; Mauritania; rainfall; structural breaks (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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