Forecasting the 2012 Presidential Election from History and the Polls

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1 Forecasting the 2012 Presidential Election from History and the Polls Drew Linzer Assistant Professor Emory University Department of Political Science Visiting Assistant Professor, Stanford University Center on Democracy, Development, and the Rule of Law votamatic.org Bay Area R Users Group February 12, 2013

2 The 2012 Presidential Election: Obama 332 Romney 206

3 The 2012 Presidential Election: Obama 332 Romney 206 But also: Nerds 1 Pundits 0

4 The 2012 Presidential Election: Obama 332 Romney 206 But also: Nerds 1 Pundits 0 Analyst forecasts based on history and the polls Drew Linzer, Emory University Simon Jackman, Stanford University Josh Putnam, Davidson College Nate Silver, New York Times Sam Wang, Princeton University Pundit forecasts based on intuition and gut instinct Karl Rove, Fox News Newt Gingrich, Republican politician Michael Barone, Washington Examiner George Will, Washington Post Steve Forbes, Forbes Magazine

5 What we want: Accurate forecasts as early as possible The problem: The data that are available early aren t accurate: Fundamental variables (economy, approval, incumbency) The data that are accurate aren t available early: Late-campaign state-level public opinion polls The polls contain sampling error, house effects, and most states aren t even polled on most days

6 What we want: Accurate forecasts as early as possible The problem: The data that are available early aren t accurate: Fundamental variables (economy, approval, incumbency) The data that are accurate aren t available early: Late-campaign state-level public opinion polls The polls contain sampling error, house effects, and most states aren t even polled on most days The solution: A statistical model that uses what we know about presidential campaigns to update forecasts from the polls in real time

7 What we want: Accurate forecasts as early as possible The problem: The data that are available early aren t accurate: Fundamental variables (economy, approval, incumbency) The data that are accurate aren t available early: Late-campaign state-level public opinion polls The polls contain sampling error, house effects, and most states aren t even polled on most days The solution: A statistical model that uses what we know about presidential campaigns to update forecasts from the polls in real time What do we know?

8 1. The fundamentals predict national outcomes, noisily Election year economic growth Source: U.S. Bureau of Economic Analysis

9 1. The fundamentals predict national outcomes, noisily Presidential approval, June Source: Gallup

10 2. States vote outcomes swing (mostly) in tandem Source: New York Times

11 3. Polls are accurate on Election Day; maybe not before 60 Florida: Obama, 2008 Obama vote share Actual outcome 40 May Jul Sep Nov Source: HuffPost-Pollster

12 4. Voter preferences evolve in similar ways across states 60 Florida: Obama, Virginia: Obama, 2008 Obama vote share Obama vote share May Jul Sep Nov May Jul Sep Nov 60 Ohio: Obama, Colorado: Obama, 2008 Obama vote share Obama vote share May Jul Sep Nov May Jul Sep Nov Source: HuffPost-Pollster

13 5. Voters have short term reactions to big campaign events Source: Tom Holbrook, UW-Milwaukee

14 All together: A forecasting model that learns from the polls Publicly available state polls during the campaign Cumulative number of polls fielded Months prior to Election Day Forecasts weight fundamentals Forecasts weight polls Source: HuffPost-Pollster

15 First, create a baseline forecast of each state outcome Abramowitz Time-for-Change regression makes a national forecast: Incumbent vote share = Q2 GDP growth June net approval 4.3 In office two+ terms

16 First, create a baseline forecast of each state outcome Abramowitz Time-for-Change regression makes a national forecast: Incumbent vote share = Q2 GDP growth June net approval 4.3 In office two+ terms Predicted Obama 2012 vote = (1.3) (-0.8) 4.3 (0)

17 First, create a baseline forecast of each state outcome Abramowitz Time-for-Change regression makes a national forecast: Incumbent vote share = Q2 GDP growth June net approval 4.3 In office two+ terms Predicted Obama 2012 vote = (1.3) (-0.8) 4.3 (0) Predicted Obama 2012 vote = 52.2%

18 First, create a baseline forecast of each state outcome Abramowitz Time-for-Change regression makes a national forecast: Incumbent vote share = Q2 GDP growth June net approval 4.3 In office two+ terms Predicted Obama 2012 vote = (1.3) (-0.8) 4.3 (0) Predicted Obama 2012 vote = 52.2% Use uniform swing assumption to translate to the state level: Subtract 1.5% for Obama from his 2008 state vote shares Make this a Bayesian prior over the final state outcomes

19 Combine polls across days and states to estimate trends States with many polls Florida: Obama, 2012 States with fewer polls Oregon: Obama, 2012 Obama vote share Obama vote share May Jun Jul Aug Sep Oct Nov May Jun Jul Aug Sep Oct Nov

20 Combine with baseline forecasts to guide future projections 56 Random walk (no) Florida: Obama, 2012 Obama vote share May Jun Jul Aug Sep Oct Nov

21 Combine with baseline forecasts to guide future projections Random walk (no) Florida: Obama, 2012 Mean reversion Florida: Obama, 2012 Obama vote share Obama vote share May Jun Jul Aug Sep Oct Nov May Jun Jul Aug Sep Oct Nov Forecasts compromise between history and the polls

22 A dynamic Bayesian forecasting model Model specification y k Binomial(π i[k]j[k], n k ) π ij = logit 1 (β ij + δ j ) National effects: δ j State components: β ij Election forecasts: ˆπ ij Priors β ij N(logit(h i ), τ i ) δ J 0 β ij N(β i(j+1), σβ 2) δ j N(δ (j+1), σδ 2) Number of people preferring Democrat in survey k, in state i, on day j Proportion reporting support for the Democrat in state i on day j Informative prior on Election Day, using historical predictions h i, precisions τ i Polls assumed accurate, on average Reverse random walk, states Reverse random walk, national

23 A dynamic Bayesian forecasting model Model specification y k Binomial(π i[k]j[k], n k ) π ij = logit 1 (β ij + δ j ) National effects: δ j State components: β ij Election forecasts: ˆπ ij Number of people preferring Democrat in survey k, in state i, on day j Proportion reporting support for the Democrat in state i on day j Estimated for all states simultaneously Priors β ij N(logit(h i ), τ i ) δ J 0 β ij N(β i(j+1), σβ 2) δ j N(δ (j+1), σδ 2) Informative prior on Election Day, using historical predictions h i, precisions τ i Polls assumed accurate, on average Reverse random walk, states Reverse random walk, national

24 Results: Anchoring to the fundamentals stabilizes forecasts Florida: Obama forecasts, Obama vote share Shaded area indicates 95% uncertainty Jul Aug Sep Oct Nov

25 Results: Anchoring to the fundamentals stabilizes forecasts Electoral Votes OBAMA ROMNEY 206 Jul Aug Sep Oct Nov

26 There were almost no surprises in 2012 On Election Day, average error = 1.7% Why didn t the model do more?

27 There were almost no surprises in 2012 On Election Day, average error = 1.7%

28 There were almost no surprises in 2012 On Election Day, average error = 1.7% Why didn t the model improve forecasts by more?

29 The fundamentals and uniform swing were right on target State election outcomes 2012=2008 line 2012 Obama vote OK ID WY UT AZ GA MS SC MO IN AK MT LA TX SD ND AL KS ARKY NE TN WV NY MD RI CA MA NJ CT DE IL WA ME OR NM MI MN CO NH IA PA NV WI OH VA FL NC VT HI Obama vote

30 Aggregate preferences were very stable Percent supporting: Obama Romney

31 Could the model have done better? Yes Difference between actual and predicted vote outcomes Election Day forecast error HI AK OR MS ID CA NJ MD CT LA MA ME IA AR RI NY NV NH NM SC TX IN WA GA MN MO OK AZ DE ALUT IL ND KS VT MT WY KY NE SD TN WV MI CO WI NC PA VA Obama performed better than expected FL OH Romney performed better than expected Number of polls after May 1, 2012

32 Forecasting is only one of many applications for the model 1 Who s going to win? 2 Which states are going to be competitive? 3 What are current voter preferences in each state? 4 How much does opinion fluctuate during a campaign? 5 What effect does campaign news/activity have on opinion? 6 Are changes in preferences primarily national or local? 7 How useful are historical factors vs. polls for forecasting? 8 How early can accurate forecasts be made? 9 Were some survey firms biased in one direction or the other?

33 House effects (biases) were evident during the campaign

34 Much more at

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