Do Econometric Models Provide More Accurate Forecasts When They are More Conservative? A Test of Political Economy Models for Forecasting Elections

25 Pages Posted: 2 Nov 2017

See all articles by Andreas Graefe

Andreas Graefe

Macromedia University of Applied Sciences

Kesten C. Green

University of South Australia - UniSA Business; Ehrenberg-Bass Institute

J. Scott Armstrong

University of Pennsylvania - Marketing Department

Date Written: August 11, 2015

Abstract

The assumptions of multiple regression analysis are not met in many practical forecasting situations and, as a result, regression models are insufficiently conservative. We tested the effect on forecast accuracy of applying three evidence-based forecasting guidelines to 18 political economy models for forecasting elections in nine countries, all of which were originally estimated using multiple regression analysis.

The guidelines direct modelers to account for uncertainty of econometric model forecasts by:

(1) modifying estimates of the strength of variable effects,

(2) combining forecasts from diverse models, and

(3) taking account of all variables that are known to be important.

Out-of-sample forecast accuracy was compared with the accuracy of forecasts from the originally published econometric models representing typical practice. While damping the estimated variable weights did not improve accuracy, equalizing them reduced error compared the original model forecasts by 10%. Combining forecasts from models for US (N=8) and Australian (N=2) elections reduced error by 25% on average. Including more causal knowledge, by using all unique variables from the different models in equal-weights index models, reduced error on average 26%.

Keywords: combining forecasts, damping, equalizing, elections, golden rule of forecasting, index method, shrinkage

Suggested Citation

Graefe, Andreas and Green, Kesten C. and Armstrong, J. Scott, Do Econometric Models Provide More Accurate Forecasts When They are More Conservative? A Test of Political Economy Models for Forecasting Elections (August 11, 2015). Available at SSRN: https://ssrn.com/abstract=3063275 or http://dx.doi.org/10.2139/ssrn.3063275

Andreas Graefe

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 ( 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 ( email )

Australia

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

J. Scott Armstrong (Contact Author)

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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