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Model Complexity and Accuracy : A COVID-19 Case Study

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.

Ketersediaan

Informasi Detail

Judul Seri

DECISION ANALYSIS; Vol.19 No.4 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 354-383

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FORECASTING
MODELING
CALIBRATION
EXPERT ELICITATION
COVID 19

Info Detail Spesifik

https://doi.org/10.1287/deca.2022.0457

Pernyataan Tanggungjawab

Colin Small, J. Eric Bickel

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