Model Complexity and Accuracy: A COVID-19 Case Study

Colin Small () and J. Eric Bickel ()
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Colin Small: Operations Research and Industrial Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, Texas 78712
J. Eric Bickel: Operations Research and Industrial Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, Texas 78712; Department of Information, Risk, and Operations Management, McCombs School of Business, University of Texas at Austin, Austin, Texas 78712

Decision Analysis, 2022, vol. 19, issue 4, 354-383

Abstract: When creating mathematical models for forecasting and decision making, there is a tendency to include more complexity than necessary, in the belief that higher-fidelity models are more accurate than simpler ones. In this paper, we analyze the performance of models that submitted COVID-19 forecasts to the U.S. Centers for Disease Control and Prevention and evaluate them against a simple two-equation model that is specified using simple linear regression. We find that our simple model was comparable in accuracy to highly publicized models and had among the best-calibrated forecasts. This result may be surprising given the complexity of many COVID-19 models and their support by large forecasting teams. However, our result is consistent with the body of research that suggests that simple models perform very well in a variety of settings.

Keywords: modeling; forecasting; calibration; COVID-19; expert elicitation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:19:y:2022:i:4:p:354-383

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