Correcting for spatial and temporal dependence in estimating the economic effect of extreme heat
- 1University of Pennsylvania, Philadelphia, PA, USA.
Abstract
Callahan and Mankin argue that extreme heat, measured by maximum 5-day average temperature, has a precise and economically meaningful effect on subnational gross domestic product per-capita growth. We reexamine this claim and find that the reported precision does not survive methods that respect the data's spatial and temporal dependence. Country-preserving permutations and postselection correction substantially widen marginal-effect intervals. The estimates are also highly sensitive to influential countries and years, with exclusions sometimes collapsing the effect or reversing its sign. A hierarchical Bayesian model with region-within-country structure and AR(1) year effects yields much larger uncertainty, shrinks the estimated effect by 67 to 90%, and produces credible intervals spanning zero. Last, rolling out-of-sample tests show that adding extreme-climate variables does not improve prediction over models without them. We conclude that the claimed precision is not sustained once dependence, model selection, and influential observations are handled appropriately.
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BibTeX
@article{wyner2026correcting,
title = {{Correcting for spatial and temporal dependence in estimating the economic effect of extreme heat}},
author = {Abraham J. Wyner and Ryan S. Brill},
journal = {Science Advances},
year = {2026},
month = oct,
doi = {10.1126/sciadv.aeg1967},
url = {https://www.science.org/doi/10.1126/sciadv.aeg1967},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Correcting for spatial and temporal dependence in estimating the economic effect of extreme heat
AU - Abraham J. Wyner
AU - Ryan S. Brill
PY - 2026
DA - 2026/10/02
JO - Science Advances
DO - 10.1126/sciadv.aeg1967
UR - https://www.science.org/doi/10.1126/sciadv.aeg1967
AB - Callahan and Mankin argue that extreme heat, measured by maximum 5-day average temperature, has a precise and economically meaningful effect on subnational gross domestic product per-capita growth. We reexamine this claim and find that the reported precision does not survive methods that respect the data's spatial and temporal dependence. Country-preserving permutations and postselection correction substantially widen marginal-effect intervals. The estimates are also highly sensitive to influential countries and years, with exclusions sometimes collapsing the effect or reversing its sign. A hierarchical Bayesian model with region-within-country structure and AR(1) year effects yields much larger uncertainty, shrinks the estimated effect by 67 to 90%, and produces credible intervals spanning zero. Last, rolling out-of-sample tests show that adding extreme-climate variables does not improve prediction over models without them. We conclude that the claimed precision is not sustained once dependence, model selection, and influential observations are handled appropriately.
SN - 2375-2548
ER -