1. Predicting Presidential Elections
Two Common (Empirical) Approaches:
Fundamentals
Presidential Approval Rates
Unemployment Rates
GDP Growth
Polls
Horserace polls in the leadup to election
… or both at the same time

2. Fundamentals Prediction Strategy: Common Indicators:
Analyze relationship between one or many indicators and past electoral performance
Use this relationship to predict current outcome based on the same indicators
Common Indicators:
Presidential Approval Rate at time of election
GDP Growth Rate, Unemployment Rate, Consumer Sentiment, and other Economic Indicators
Consecutive Terms by Incumbent Party (Three consecutive terms by one party has been rare)

3. Strengths and Weaknesses of Fundamentals
Strengths: Captures recurring patterns; can predict well in advance of the election; cuts away punditry and “spin”
Weaknesses: Ignores any election-specific facts; Nothing about the candidates or their policies is included in analysis

4. (Simplistic) Example: Presidential Approval
Inputs: Last Gallup Approval Rating of incumbent president before Election Day and incumbent’s party’s two-party vote share in that year’s election
Data begin in 1948 with the advent of recurring scientific polling on presidential approval

8. (Simplistic) Example: Presidential Approval
Result: Expectation is that Hillary Clinton would win because of President Obama’s 54% Approval Rate
Errors large enough to change that result have happened (1960, 2000) and look fairly similar: reasonably popular two-term president leaving office and their party’s next candidate (Nixon, Gore) fails to win a third term.
More sophisticated analyses that use multiple indicators imply that the election should be close, with either Clinton or Trump slightly favored depending on the combination of variables.
There is no “Trump” variable.

9. Polling Prediction Strategy: Ask voters who they plan to vote for!
Not so simple...

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11. Polling Prediction Strategy: Ask voters who they plan to vote for
Average (or aggregate) the results of those polls
Estimate uncertainty in the averages and simulate outcomes to determine the likelihood of results of interest

12. Strengths and Weaknesses of Polling
Strengths: Captures election-specific information, including voters’ evaluations of the candidates; fundamentals are presumably “baked into” polls; reasonably predictive of results
Weaknesses: Polling is highly inexact and prone to error; many assumptions are necessary to construct a single poll, many more to aggregate them; aggregation of polls will not neutralize errors if many pollsters make the same incorrect assumptions; Fewer than 10% of those called for a poll answer the questions, creating selection effects
Note the many recent failings of polling: the 2015 British Elections, the “Brexit” Referendum, the 2015 Israeli Election, the 2016 Democratic Primary in Michigan

13. (Simplistic Example): Polling
Inputs: Last ten national polls of Clinton v. Trump, converted to two- party vote share.
Outlet
Clinton
Trump
Clinton 2-Party
LA Times
43%
47%
47.8%
ABC
49%
51.0%
IBD
45%
51.1%
44%
46%
48.9%
41%
52.9%
50%
52.6%
Economist
51.6%
FOX News
52.7%
Pew
53.8%
51%
54.3%
AVERAGE
47.6%
44.5%
51.7%

14. (Simplistic Example): Polling
Now, make a simplistic assumption about uncertainty to simulate the national popular vote:

15. (Simplistic Example): Polling
Can also analyze at the state level. Here’s Florida:
Outlet
Clinton
Trump
Clinton 2-Party
Remington
44%
48%
47.8%
NY Times
42%
46%
47.7%
Emerson
45%
50.5%
NBC
50.6%
Dixie
Gravis
47%
Bloomberg
43%
48.9%
Florida Atlantic U
51.7%
U of North Florida
39%
52.4%
Bay News 9
51.6%
AVERAGE
44.7%
44.8%
50.0%

16. (Simplistic Example): Polling
Florida simulated:

17. (Simplistic Example): Polling
The winner is determined by the Electoral College, so state expectations can be combined. For example:
49.5% chance for Clinton in Florida
60% chance for Clinton in North Carolina
What is the probability she wins both? .297
Neither? .202
At least one? 0.501
This calculation can be carried through for all 50 states (plus DC) to produce estimates for how often Clinton would accumulate the necessary 270 Electors to win.

18. Non-Simplistic Examples:
538, New York Times’s The Upshot, Huffington Post’s Pollster, and others
These can also take into account the “fundamentals.”
They can do more sophisticated corrections, weighting polls based on quality and past predictive power
They also recognize that errors are likely correlated (if Clinton underperforms in Ohio, she is also likely to underperform in Iowa)

19. 538 as of Monday