Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach

Francis Diebold, Maximilian Gobel, Philippe Goulet Coulombe, Glenn Rudebusch and Boyuan Zhang
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Maximilian Gobel: ISEG - Universidade de Lisboa
Philippe Goulet Coulombe: University of Pennsylvania
Boyuan Zhang: University of Pennsylvania

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: The diminishing extent of Arctic sea ice is a key indicator of climate change as well as an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that accounts for their differing volatility and cross-correlations. From this model, we extract an optimal combined measure of Arctic sea ice extent using the Kalman smoother.

Keywords: Climate modeling; nowcasting; model averaging; ensemble averaging (search for similar items in EconPapers)
JEL-codes: C22 Q54 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2020-03-31
New Economics Papers: this item is included in nep-env
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach (2021)
Working Paper: Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:20-012

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