F10 Tweaking the model - MCMC and types of elections Flashcards

1
Q

What is the function of endorsements?

A

Voters can ‘borrow’ a position from a referent person. Aimed at independents

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2
Q

What are the five types of elections?

A
  1. Maintaining dominance
  2. Deviating from dominance
  3. Re-instating dominance
  4. Balancing
  5. Realignment of groups

1-3 happens when there is a clear majority in the normal vote. 4-5 when the normal vote is balanced.

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3
Q

What is the normal vote?

A

Distribution of party identification between dem and rep discounted by differences in turnout patterns of supporters of each part.

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4
Q

What is a ‘maintaining election’?

A

Stable partisan attachments continue to be a major determinant of election result

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5
Q

What is a ‘deviating election’?

A

Short-term partisan attitudes deviations lead to election of presidential nominee of the minority party

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6
Q

What is a ‘reinstating election’?

A

The majority party wins the White House back after a deviating election

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7
Q

What is a ‘realigning election’?

A

Majority party of the normal vote loses election after election because the partisanship of people changes (long term)

Not necessarily unidirectional.

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8
Q

What is a ‘balancing election’?

A

Neither party has a majority in party identification.

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9
Q

What are three examples of a realignment that political scientist agree on?

A

Social groups are basis of a realignment.

1860 (after the civil war - Lincoln and anti-slavery)

1896 (shift in regional basis - Republicans strong in the Northeast and Midwest, and Democrats in the South and rural areas)

1930 (the New Deal era - Roosevelt after 30’s depression. Social welfare policies uniting ethinic minorities, urban and poor)

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10
Q

Which elections have been balancing?

A

All elections since 1980 more or less since scientist can’t agree on a realignment.

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11
Q

What are examples of maintaining, deviating and re-instating dominance?

A

Maintaining: Lyndon B. Johnson (dem)
Deviating: Eisenhower after 20 years of dem
Re-instating: JFK after Eisenhower

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12
Q

What is a possible realignment currently?

A

Education
Gender gap exploding
Race less important (Latino vote for men especially)

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13
Q

Why do we use Markov Chain Monte Carlo?

A

Because the integral for Bayes rule is to complex to solve (the parameter space is massive).

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14
Q

How can we randomly draw samples from a posterior distribution that is unknown?

A

We take a random walk through the parameter space, favoring parameter values which have a high posterior probability.

Proposed jumps in the walk are accepted/rejected probabilistically.

We end up with sampling where probability is higher thus converging the true posterior distribution.

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15
Q
A
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