Accuracy Gains from Conservative Forecasting: Tests Using Variations of 19 Econometric Models to Predict 154 Elections in 10 Countries

PLoS ONE 14 (1): e0209850. 2019

14 Pages Posted: 25 Jan 2019

See all articles by Andreas Graefe

Andreas Graefe

Macromedia University of Applied Sciences

Kesten C. Green

University of South Australia - UniSA Business School; Ehrenberg-Bass Institute for Marketing Science

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Date Written: January 10, 2019

Abstract

Problem. Do conservative econometric models that comply with the Golden Rule of Forecasting provide more accurate forecasts?

Methods. To test the effects of forecast accuracy, we applied three evidence-based guidelines to 19 published regression models used for forecasting 154 elections in Australia, Canada, Italy, Japan, Netherlands, Portugal, Spain, Turkey, U.K., and the U.S. The guidelines direct forecasters using causal models to be conservative to account for uncertainty by (I) modifying effect estimates to reflect uncertainty either by damping coefficients towards no effect or equalizing coefficients, (II) combining forecasts from diverse models, and (III) incorporating more knowledge by including more variables with known important effects.

Findings. Modifying the econometric models to make them more conservative reduced forecast errors compared to forecasts from the original models: (I) Damping coefficients by 10% reduced error by 2% on average, although further damping generally harmed accuracy; modifying coefficients by equalizing coefficients consistently reduced errors with average error reductions between 2% and 8% depending on the level of equalizing. Averaging the original regression model forecast with an equal-weights model forecast reduced error by 7%. (II) Combining forecasts from two Australian models and from eight U.S. models reduced error by 14% and 36%, respectively. (III) Using more knowledge by including all six unique variables from the Australian models and all 24 unique variables from the U.S. models in equal-weight “knowledge models” reduced error by 10% and 43%, respectively.

Originality. This paper provides the first test of applying guidelines for conservative forecasting to established election forecasting models.

Usefulness. Election forecasters can substantially improve the accuracy of forecasts from econometric models by following simple guidelines for conservative forecasting. Decision-makers can make better decisions when they are provided with models that are more realistic and forecasts that are more accurate.

JEL Classification: C50, C53

Suggested Citation

Graefe, Andreas and Green, Kesten C. and Armstrong, J. Scott, Accuracy Gains from Conservative Forecasting: Tests Using Variations of 19 Econometric Models to Predict 154 Elections in 10 Countries (January 10, 2019). PLoS ONE 14 (1): e0209850. 2019. Available at SSRN: https://ssrn.com/abstract=3320599

Andreas Graefe (Contact Author)

Macromedia University of Applied Sciences ( email )

Sandstrasse 9
Munich, Bavaria 80337
Germany

HOME PAGE: http://www.andreas-graefe.org

Kesten C. Green

University of South Australia - UniSA Business School ( email )

GPO Box 2471
Adelaide, SA 5001
Australia
+61 8 83012 9097 (Phone)

HOME PAGE: http://people.unisa.edu.au/Kesten.Green

Ehrenberg-Bass Institute for Marketing Science ( email )

Australia

HOME PAGE: http://www.marketingscience.info/people/KestenGreen.html

J. Scott Armstrong

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States
215-898-5087 (Phone)
215-898-2534 (Fax)

HOME PAGE: http://marketing.wharton.upenn.edu/people/faculty/armstrong.cfm

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