Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting

Lauderdale, Benjamin E.; Linzer, Drew A.

doi:10.1016/j.ijforecast.2015.03.002

Cited by 19 publications

(32 citation statements)

References 21 publications

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“…More importantly, it is a dangerous exercise in over-selling our precision. We agree with both Bartels and Zaller (2001) and Lauderdale and Linzer (2015) that if one wanted predictions, some form of model averaging would be the best way to do this. However, we are interested not in the best prediction but in the best understanding we can have of what motivates voter behavior.…”

Section: Do Voters Hold Elected Officials Accountable For Economic Pesupporting

confidence: 71%

Economic Voting and Economic Inequality: U.S. Presidential Elections 1952–2012

Linn

Nagler

2017

American Politics Research

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Most economic models of election outcomes make two assumptions: voters look at the aggregate economy, and they compare the state of the economy with some fixed reference. We argue that the increase in economic inequality and slowing of overall growth suggest these assumptions should no longer hold. We propose a theory that allows voters to take into account the distribution of economic growth, and we reconsider different decision rules voters could use to evaluate the incumbent. Analyzing presidential elections from 1952 through 2012, we show that models using the economic performance of individual income quintiles are indistinguishable in overall fit from models using aggregate income to predict election results, but can produce different predictions given different distributions of growth. And we show that voters do not appear to explicitly compare economic performance of the incumbent with the out-party, suggesting they have reneged on their role as rational gods of vengeance.

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Section: Do Voters Hold Elected Officials Accountable For Economic Pesupporting

confidence: 71%

Economic Voting and Economic Inequality: U.S. Presidential Elections 1952–2012

Linn

Nagler

2017

American Politics Research

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“…There is nothing magical about this kind of forecasting model or any such model (for an excellent critique and extension, see Lauderdale and Linzer 2015). We do not believe that such models are perfect predictors, tell us everything about presidential elections, or imply that the campaign is irrelevant.…”

Section: The Implications For 2016mentioning

confidence: 99%

The Electoral Landscape of 2016

Sides¹,

Tesler²,

Vavreck³

2016

The ANNALS of the American Academy of Political and Social Scie

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As 2015 got underway, most Americans were poised for another Bush vs. Clinton presidential election, but by the middle of the year it was clear something unexpected was unfolding in the race for the White House. In this article, we illuminate the political landscape heading into the 2016 election, paying special attention to the public’s mood, their assessments of government, their attitudes about race and members of the other party, and the health of the nation’s economy. Fundamental predictors of election outcomes did not clearly favor either side, but an increasing ethnic diversity in the electorate, alongside a racially polarized electorate, was favorable to Democrats. Ultimately, an ambivalent electorate divided by party and race set the stage for a presidential primary that played directly on these divisions, and for a general election whose outcome initially appeared far from certain.

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“…Second, the posterior predictive distribution that provides a forecast for the upcoming election results given the predictors . This distribution takes “coefficient uncertainty” (Lauderdale and Linzer 2015, p.967) into account by integrating over the posterior distribution of the parameters from the predictive distribution for the upcoming election: . Unlike common implementations of fundamentals models, we integrate our fundamentals model into a dynamic Bayesian measurement model, which we describe below.…”

Section: A Dynamic Bayesian Measurement Model For Multiparty Electionsmentioning

confidence: 99%

Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals

Stoetzer¹,

Neunhoeffer²,

Gschwend³

et al. 2018

Polit. Anal.

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We offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multiparty setting.

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Under-performing, over-performing, or just performing? The limitations of fundamentals-based presidential election forecasting

Cited by 19 publications

References 21 publications

Economic Voting and Economic Inequality: U.S. Presidential Elections 1952–2012

Economic Voting and Economic Inequality: U.S. Presidential Elections 1952–2012

The Electoral Landscape of 2016

Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals

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