Curriculum

Question 1

F1 Gelman et al. 2020

Answer 1

Close elections (small errors mean a lot – difficult to forecast)

Polls have more error than stated (only consider standard sample error 2 pct. but closer to 4 pct. because of nonsampling error: nonresponse, mode, house effect)

Argument for region (states swing similarly when they are neighbors)

Incentives among forecaster (over- and under confidence)

Basic understanding of our understanding of forecasting models

Fundamentals x calcification/polarization is new for forecasters. Politics are changing, but models are not

Question 2

F1 Victor (2021)

Answer 2

Criticism of forecasting:
(1) Partisan polarization perverts fundamentals
(2) Forecasts may affect turnout
(3) Outsized focus on the election horse race
(4) Forecasts give a false impression of science and certainty

Question 3

F1 Cohn & Katz (2018)

Answer 3

Show your probability and how it can change
Misinterpretation of uncertainty/probability

Question 4

F2 The bitter end chapter 1

Answer 4

Calcification due to:

(1) Long-term (party polarization)
(2) Short-term shift (Trump – emphasis identity, that contains more disagreement + Covid-19 trust in government)

Calcification (locked in): Party polarization (further apart – a bigger ideological leap to change) + affective polarization (worse feelings about the opposite)

Calcification manifests in many ways (vote, perception of economy, trust)

Question 5

F2 The American voter chapter 2

Answer 5

Funnel of causality (Michigan model)

Most votes are determined by sociodemographic and party identification

Fundamentals is ‘issues’: More important for swing voters/independents

Question 6

F2 The American voter chapter 13

Answer 6

Economic voting: Objective/subjective, prospective/retrospective and ego tropic (pocketbook voting) and socio tropic

Party identification can determine the relationship between economic voting and vote choice (forerunner of Brady et al. 2022)

The election is a referendum on the incumbent performance relating to economy (just like Abramowitz just narrower)

Socio tropic voting is more predominante

Question 7

F2 Brady et al. (2022)

Answer 7

Growing partisan divide in economic perception. Both Republican and democrats perceive the economy differently.

Economic variables still matter - just less than before.

Builds on the American voter just with more data from more calcified elections

Question 8

F2 Erikson & Wlezien (2008)

Answer 8

Polls and economic indicators.

Economic indicators explain more early on (Q2 GDP is imperative)

Economic indicators are incorporated in later on in polls through the campaign (Q3 GDP doesn’t tells us that much)

Question 9

F2 The bitter end chapter 8

Answer 9

Story of 2020 election: Fundamentals favored Biden, but it was closer due to Trump.

Chronically low approval for Trump (from start to beginning) = fundamentals are still relevant

Basically, Obama in 2012 (economically) but with lower approval rating

Why didn’t Trump lose bigger/didn’t plummet = calcification.

Covid-19, black life matters (big events didn’t matter that much – perfect example of calcification. Just like the shooting)

Question 10

F3 Abramowitz (2008)

Answer 10

Three parameters.

GDP is not the best predictor compared to approval. Discuss difference between incumbency and time in White House

Referendum of the presidency as a whole (broader then economic voting)

Question 11

F3 Dickinson (2014)

Answer 11

Looking at 2012 election

Argues the fundamentals still matter (TV hosts declared them not to be – Obama was such a good candidate)

Fundamentals are brought to voters through campaign (campaigns interpret them differently)

Question 12

Erikson & Wlezien (2014)

Answer 12

Same argument as 2008 text

Economic indicators are channeling into polls (bringing fundamentals to voters – Gelman especially)

Internal and external fundamentals

Around 100 days before the election the economic indicators start influencing the polls

Question 13

F3 The bitter end chapter 3

Answer 13

Trump approval rating. Why was it so low? Due to calcified politics

Populism with the Republican voters

Affective polarization

Question 14

F3 Linzer & Lauderdale (2015)

Answer 14

Much more uncertainty than what you report with fundamentals (coefficients, model specification and from national to state-level)

Uncertainty is understated! Just like polls that just report sample error

Bayesian fundamentals model. More complex model

Question 15

F4 538 2024. How pollster ratings work

Answer 15

Accounts for:

Accuracy: Average error (election result - how difficult is it to predict) and average bias (house effect)

Methodological transparency

Question 16

F4 Silver (2021). Death of polling

Answer 16

Polling in 2020 was mediocre not bad.

Pollsters try to correct their previous mistake. Risk of overcorrecting for earlier mistakes

Live caller is no longer better than internet (low response rate in general)

Distinguish between error (distance from election result) and house bias (distance from average polls)

Question 17

F4 Bailey (2014)

Answer 17

Everything about polling.

Modern problems = sample (no random sampling because of low response rate)

Types of nonresponse (dependent on the case): Ignorable (size of groups) and non-ignorable (groups acting weird).

Introduce polling mode (live calling, face to face). Sampling frame: Poll of respondents.

Probability based (random digit dialing) vs. panel (wild west – employing methods from probability-based sampling)

Partisan nonresponse: Excitement (new candidate – democrats answer more. Trump calling polls fake news – republicans are less inclined to answer).

Question 18

F4 Rentsch et al. (2019)

Answer 18

Likely voter vs. registered voters (registered overestimated Democrats)

Why: Low turnout and differential turnout. You are trying to predict an electorate that hasn’t formed yet

Likely voter model (probabilistic and deterministic)

Combination of PG + demographic because turnout is dependent on sociodemographic

Vote intention was the most important (people saying they are not going to vote – never voted)

First you deal with ignorable nonresponse and then turnout.

Question 19

F5 Jackman (2005)

Answer 19

Why do pooling: Power problem of estimating changes in polls (like 0,5%). Motivation: More data – reduces sample error

Kalman filter / random walk (today is predicting tomorrow)

Retrospectively. Evaluating polls from Australia.

Estimate house effect looking at the difference between election result and polls

Question 20

F5 Gelman 2021

Answer 20

Polling error is not that bad

The key challenges are (a) attaining a representative sample of potential voters (differential nonresponse), and (b) predicting turnout (differential turnout)

Sampling error and non-sampling error (nonresponse etc.)

Pollsters inform us about opinion trends and policy preferences

Differential nonresponse and differential turnout seem like more plausible explanations of polling error.

House effects are non-intended

Reject the shy trump vote hypothesis (nonresponse is more likely)

Question 21

F6 The American voter chapter 11

Answer 21

Group identification leads to common believes and aggregate cost/benefit due to:
1) Psychological identification
2) Linked fate
3) Group membership

Three levels: Individual – group – political leader (individual only looks towards the group not the political leader)

Strong identification with the group = strong predictor of vote

A relative understanding. How distinct is the group compared to others.

Question 22

F6 The American voter chapter 12

Answer 22

Social classes (certain level of education, income e.g.). Not so relevant anymore

Not as visual in everyday life as groups (no psychological perception)

The distinction between group and category/social class is sometimes blurred.

Four social variables class, education, age, and gender

Question 23

F6 Pew Research Center (2024)

Answer 23

Empirical information/evidence for group theory

White evangelical, black and unaffiliated lean most.

Educational divide (maybe short term because of Trump)

Question 24

F6 The bitter end chapter 4

Answer 24

Primaries for 2020. Focus on electability and Bidens winning coalition among sociodemographic groups

Question 25

F7 Kruschke (2014)

Answer 25

Why use Bayesian model: We only have one election (frequentist approach not relevant)

Updates the prior in the direction that maximizes the likelihood of data

Posterior, prior, likelihood, evidence

MCMC is used because we can’t solve the integral via Bayes Rule

Uninformative prior and more new information = less weight on prior

Question 26

F8 Lock & Gelman (2010)

Answer 26

From national level to state-level

State-level positions are relatively stable from year to year.

For 2024 the assume states are centered on the 2020 vote and then they estimate the variance based on historical data

Home state advantage 6 pct.

Our goal is not to estimate public opinion at any particular point in time but to forecast public opinion

Question 27

F8 Graefe (2018)

Answer 27

Experts x fundamentals x polls

Precision of fundamentals: Stable and not influenced by nonresponse

Combining different sources of information increases precision (especially if information is different)

MSE / judgement of models

Question 28

F8 Linzer (2013)

Answer 28

The economist works based on this more simple model.

Why combine fundamentals and polls

No correction for house effects + multilevel model is not based on sociodemographic

Question 29

F9 Heidemanns et al. (2020)

Answer 29

Introducing the 2020 economist model.

Description of different sources of bias: National, state-level, house effects, mode effects, populations effects and adjusters

Priors for error is centered on 0 and we estimate the standard deviation.

Interacts economic indicators with swing voters do dampen the effect in polarized elections

Question 30

F9 The economist (2024)

Answer 30

General introduction to the model

Leave-one-out-estimation and MCMC

Parsimonious models better predict unforeseen data.

More sophisticated way of arriving at state-level priors

Question 31

F9 Gelman 2024

Answer 31

Updating the 2020 code.

Shift in candidate: Shouldn’t have a big effect due to the Martingale property.

Unusual combination of fundamentals: Strong economic performance x unpopular.

Question 32

F10 The American voter chapter 14

Answer 32

Classification of elections: Maintaining, deviating, reinstating, realignment and balancing.

Question 33

F10 538 (2024). How their model works

Answer 33

Three parts of the covariance matrix: Sociodemographics, electoral history and region.

Include both subjective and objective measures of economy for robustness

Political and economic fundamentals

Question 34

F10 Silver (2024)

Answer 34

Trump is leading the polls in Wisconsin, fundamentals show a tie and yet Biden is ahead in the forecast.

Difference between error and drift.

To much credit to Biden for being incumbent.

Question 35

F10 Gelman (2024)

Answer 35

538 allow for a lot of uncertainty.

Consider the role of incumbency with low approval rating.

The prior should always be of less importance with more data.