The Past as Prologue: How to Forecast Presidential Elections

23 Pages Posted: 17 Aug 2020

See all articles by Megan Czasonis

Megan Czasonis

State Street Corporate

Mark Kritzman

Windham Capital Management

David Turkington

State Street Associates

Date Written: August 10, 2020

Abstract

The authors apply a novel forecasting technique called Partial Sample Regression to predict the outcomes of U.S. presidential elections. This technique first measures the statistical relevance of past elections. It then employs an obscure mathematical equivalence – that the prediction from a linear regression equation equals a relevance-weighted average of the values for the dependent variable – to forecast election outcomes from a subsample of prior relevant elections. This technique has been applied successfully in finance to predict factor returns and the correlation of stock and bond returns. The authors apply Partial Sample Regression to predict the outcomes of the past five presential elections as well as the 2020 election. They also report which past elections were identified as being statistically most relevant for each of the elections they predict.

Keywords: Informativeness, logit, logit transformation, mahalanobis distance, partial sample regression, relevance, statistical similarity

JEL Classification: C00, C01, C02, C10, C13, C50

Suggested Citation

Czasonis, Megan and Kritzman, Mark and Turkington, David, The Past as Prologue: How to Forecast Presidential Elections (August 10, 2020). MIT Sloan Research Paper No. 6166-20, Available at SSRN: https://ssrn.com/abstract=3672362 or http://dx.doi.org/10.2139/ssrn.3672362

Megan Czasonis (Contact Author)

State Street Corporate ( email )

1 Lincoln Street
Boston, MA 02111
United States

Mark Kritzman

Windham Capital Management ( email )

800 Boylston Street
30th Floor
Boston, MA 02199
United States
6174193900 (Phone)
6172365034 (Fax)

David Turkington

State Street Associates ( email )

United States

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