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New Hampshire: Crosstab model shows Clinton in the lead

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The Crosstab model is included in the econometric models component of the combined PollyVote. It currently predicts a two-party vote share of 53.2% for Clinton, and 46.8% for Trump in New Hampshire. In comparison, on November 4, Clinton was predicted to achieve only 53.0% of the vote.

Historically, New Hampshire has been a battleground state, in which neither the Republican Party nor the Democratic Party has had overwhelming support to secure its electoral college votes. This is the reason why predictions here are of particular interest.

Putting the results in context

As any other method, models are subject to bias. Hence, a good strategy is to not have too much confidence in the results of a single econometric model. Rather than relying on results from single models, the evidence-based approach is to look at combined models or, even better, a combined forecast that relies on forecasts from different methods, each of which draws upon different data.

Results in comparison to other econometric models

Clinton currently runs at 0.0% of the two-party vote in an average of recent econometric models in New Hampshire. Compared to her numbers in the Crosstab model Clinton's econometric model average is 53.2 percentage points worse.

The Crosstab model in comparison with PollyVote's prediction

The latest PollyVote anticipates Clinton to gain 53.0% of the two-party vote in New Hampshire. PollyVote currently predicts Clinton to gain 0.2 percentage points less compared to the results of the Crosstab model. In comparison, a look at the PollyVote national prediction for Clinton indicates that the actual results are 0.5 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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