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Virginia: Dead heat between Trump and Clinton in latest DeSart model

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The DeSart model released an updated predictionof the election outcome. The model's forecast is that Clinton will receive 49.5% of the two-party vote share in Virginia, whereas Trump will win 50.5%. In comparison, on April 27, Clinton was predicted to gain only 48.4% of the vote.

Virginia is traditionally a swing state, where Republicans and Democrats have historically gained similar levels of support among voters. Therefore, the election outcome in that state is regarded crucial in determining which party will win the majority of electoral votes.

Putting the results in context

As any other method, econometric models are subject to bias. Thus, as a general rule, a good strategy is to not focus too much on the results of an individual econometric model. Instead of trusting the results from single econometric models, we recommend to consult combined econometric models or, even better, the combined PollyVote forecast that incorporates forecasts from different methods, each of which draws upon different data.

Comparison to other econometric models

Trump currently achieves 53.8% of the two-party vote in an average of recent econometric models in Virginia. Compared to his numbers in the DeSart model Trump's econometric model average is 3.3 percentage points better.

The DeSart model in comparison with PollyVote's forecast

The econometric model results for Trump are thus 0.4 percentage points below the combined PollyVote, which at the moment predicts a value of 50.9% in Virginia. In comparison, a look at the PollyVote national prediction for Trump shows that the actual results are 3.1 percentage points higher.

This article was automatically generated by the PollyBot, which uses algorithms developed by AX Semantics to generate text from data stored in our API. The exact dataset underlying this particular article can be found here.

Please let us know if you find any typos, missing words, or grammatical errors. Your feedback helps us to further improve the quality of the texts.

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