Using weather data and climate model output in economic analyses of climate change
Open accessAbstract
Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of data sets available, and then we discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.
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Not accounting for spatial and temporal autocorrelation might produce biased error estimates.
“Spatial and temporal autocorrelation are of high relevance for modelling and not accounting for the correlation of climatic and other variables might produce biased error estimates (Auffhammer et al., 2013).”
From Improving the evidence base: A methodological review of the quantitative climate migration literature (2021) · cites this paper as Auffhammer et al., 2013
In “Issue 4: Spatial Correlation” (page unavailable), the paper says that failing to correct for spatial correlation biases standard-error estimates. It notes that climate variables are correlated across both space and time, but does not establish the same error-estimate consequence for temporal autocorrelation. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIf correlated climatic variables are not simultaneously considered, this may lead to omitted variable biases.
“If correlated climatic variables are not simultaneously considered, this may lead to omitted variable biases (Auffhammer et al. 2013;Berlemann and Steinhardt 2017;Hsiang 2016).”
From Improving the evidence base: A methodological review of the quantitative climate migration literature (2021) · cites this paper as Auffhammer et al. 2013
Because climatic events are correlated, the estimated effects might plausibly pick up the effect of other not-included, but correlated climatic events, which would result in an omitted variable bias.
“First, because climatic events are correlated, the estimated effects might plausibly pick up the effect of other notincluded, but correlated climatic events, which would result in an omitted variable bias (Auffhammer et al., 2013).”
From Improving the evidence base: A methodological review of the quantitative climate migration literature (2021) · cites this paper as Auffhammer et al., 2013
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- Authors
- Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A.
- Affiliations
- National Bureau of Economic Research (US)
- Columbia University (US)
- Corresponding author
- Auffhammer, Maximilian
- Hsiang, Solomon M.
- Schlenker, Wolfram
- Sobel, Adam
- Venue
- Review of Environmental Economics and Policy
- Publisher
- University of Chicago Press
- Published
- 2013-07-01
- Volume
- 7
- Issue
- 2
- Pages
- 181-198
- ISSN
- 1750-6816 (ISSN-L)
- 1750-6824
- OpenAlex
- W2081198062
- Cited by
- 662
- References
- 53
- Citations by year
- 2026: 56 · 2025: 63 · 2024: 68 · 2023: 54 · 2022: 80 · 2021: 70 · 2020: 48 · 2019: 40 · 2018: 47 · 2017: 41 · 2016: 40 · 2015: 23 · 2014: 21 · 2013: 9 · 2012: 2
- FWCI
- 72.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Review of Environmental Economics and Policy (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- University of North Texas Digital Library (University of North Texas) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (1008847)
- U.S. Department of Energy (DEFG0208ER64640)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Keywords
- meteorological observations, climate model output, economic impacts of climate change, climate change projections
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