Faculty of Business and Law
School of Accounting, Economics and Finance
Economic Bias of Weather Forecasting: A Spatial
Nejat Anbarci, Eric Floehr, Jungmin Lee,
Joon Jin Song
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Economic Bias of Weather Forecasting: A Spatial Modeling Approach∗
University & IZA
Joon Jin Song
University of Arkansas
Abstract : The value of accurate weather forecast information is substantial. In this paper we exam-
ine competition among forecast providers and its implications for the quality of forecasts. A simple
economic model shows that an economic bias – geographical inequality in forecast accuracy – arises
due to the extent of the market. Using the unique data on daily high temperature forecasts for
704 U.S. cities, we find that forecast accuracy increases with population and income. Furthermore,
the economic bias gets larger when the day of forecasting is closer to the target day; i.e. when
people are more concerned about the quality of forecasts. The results hold even after we control
for location-specific heterogeneity and difficulty of forecasting.
JEL classification: C21, H4, L1, L8.
Keywords: Weather Forecasting; Extent of the Market; Forecast Verification; Accuracy; Bias;
Spatial Autoregressive Model.
School of Accounting, Economics and Finance, Deakin University, Australia.
email@example.com. Floehr: Intellovations, LLC. Lee: Department of Economics, Florida International Uni-
versity. Email: firstname.lastname@example.org. Song: Department of Mathematical Sciences, the University of Arkansas at Fayettevill