Forecasting the 2020 Presidential Election: Leading Economic Indicators, Polls, and the Vote

Published on Jan 1, 2021in PS Political Science & Politics2.472
· DOI :10.1017/S1049096520001481
Robert S. Erikson49
Estimated H-index: 49
Christopher Wlezien45
Estimated H-index: 45
#1Robert S. Erikson (Columbia University)H-Index: 49
#2Christopher Wlezien (University of Texas at Austin)H-Index: 45
#1Christopher Wlezien (University of Oxford)H-Index: 45
#2Robert S. Erikson (Columbia University)H-Index: 49
forecast is not enough. And the most important economic shocks to the economy are the late shocks, which may arrive too late to be measured by the forecaster. Other events also impact, such as (in 2004) the Iraq war. Incorporating presidential approval into the model helps to control for "other" events that economic indicators ignore, but obviously only those that are observable by the time of the latest approval reading. They also don't reveal much about voters' comparative judgments of the two...
#1Christopher Wlezien (UH: University of Houston)H-Index: 45
#2Robert S. Erikson (UH: University of Houston)H-Index: 49
In this article we present a simple forecasting model that has been successful at predicting past presidential elections. The two variables included in the model are cumulative per capita income growth and presidential approval. These "fundamental" variables predict the vote especially well when measured shortly in advance of the election, when the outcome is already becoming clear in the polls. Their predictive power drops quite quickly as one steps back from the election, however; readings of ...
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This article discusses the need for standards for the assignment of importance to criteria and the measurement of interaction between them in multiple criteria analyses of complex systems. A strategy for criteria evaluation is considered that is suitable to account for the interaction among a wide variety of imprecisely assessed criteria applied simultaneously. It is based on the results of collecting sample information on preferences according to the specified criteria instead of merely an abst...
#1Rashad Ahmed (Office of the Comptroller of the Currency)H-Index: 3
#2M. Hashem Pesaran (University of Cambridge)H-Index: 102
Abstract null null This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000–2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral co...
ABSTRACT. The relationship between the US presidential election cycle and the S&P500 index is examined in this paper. We focus our attention on President Barack Obama and President Donald Trump presidential election cycles, as there is a research gap examining these two presidency periods. A comparative study is limited as Obama served two terms in office while Trump only one. Daily data from January 20, 2009 to November 16, 2020 was analysed. The selected dataset covers Obamas two terms and Tru...
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