US election 2024: beware polling predictions as they can be wrong – but here's an approach which has often been on the money
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火曜日, 8月 22, 2023
Despite the avalanche of legal indictments, Donald Trump remains favourite to win the Republican nomination for the 2024 US presidential election.
Key Points:
- Despite the avalanche of legal indictments, Donald Trump remains favourite to win the Republican nomination for the 2024 US presidential election.
- If he does win the Republican nomination the question is: can he win the presidential election in November next year?
Forecasting presidential elections
- There is a lively community of political scientists using a variety of different methods to forecast elections, with many focusing on the US.
- Most forecasting models use polling data, but since we are 15 months away from the presidential election, current polling should be treated with caution.
- It should be noted that US pollsters have had a mixed record in forecasting elections.
Electoral college
- The election is determined by who wins the electoral college, not the popular vote.
- In the 2016 election Clinton won a larger vote share than Donald Trump but lost the contest in the electoral college.
- The electoral college was created by the US founding fathers, with delegates chosen to reflect voting support for the candidates in each state.
A forecasting model
- The analysis uses a century of elections from 1920 to 2020, and a relatively simple model has a good track record in predicting elections over this period.
- It uses two variables to predict the Republican share of the delegates in the Electoral College, using a technique called multiple regression.
- The first and most important predictor in the model is the state of the economy, with an incumbent being rewarded for a good record on economic growth and punished for a poor one.
- The model takes into account unusual events occurring over the period that can distort the results if they are ignored.
- Needless to say this is uncertain since the model is not a perfect fit to the data and so subject to errors.