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We used the take-the-best heuristic to develop a model to forecast the popular two-party vote shares in U.S. presidential elections. The model draws upon information about how voters expect the candidates to deal with the most important issue facing the country. We used cross-validation to calculate a total of 1000 out-of-sample forecasts, one for each of the last 100 days of the ten U.S. presidential elections from 1972 to 2008. Ninety-seven per cent of forecasts correctly predicted the winner of the popular vote. The model forecasts were competitive compared to forecasts from methods that incorporate substantially more information (e.g., econometric models and the Iowa Electronic Markets). The purpose of the model is to provide fast advice on which issues candidates should stress in their campaign. Copyright © 2010 John Wiley & Sons, Ltd.
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Electronic copy available at: http://ssrn.com/abstract=1938849
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Predicting elections from the most important issue:
A test of the take-the-best heuristic
Forthcoming in the Journal of Behavioral Decision Making
Andreas Graefe
Institute for Technology Assessment and Systems Analysis
Karlsruhe Institute of Technology, Germany
graefe@kit.edu
J. Scott Armstrong
The Wharton School
University of Pennsylvania, Philadelphia, PA
armstrong@wharton.upenn.edu
Abstract. We used the take-the-best heuristic to develop a model to forecast the popular two-
party vote shares in U.S. presidential elections. The model draws upon information about how
voters expect the candidates to deal with the most important issue facing the country. We used
cross-validation to calculate a total of 1,000 out-of-sample forecasts, one for each of the last 100
days of the ten U.S. presidential elections from 1972 to 2008. Ninety-seven percent of forecasts
correctly predicted the winner of the popular vote. The model forecasts were competitive
compared to forecasts from methods that incorporate substantially more information (e.g.,
econometric models and the Iowa Electronic Markets). The purpose of the model is to provide
fast advice on which issues candidates should stress in their campaign.
Electronic copy available at: http://ssrn.com/abstract=1938849
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The common citizen generally has little interest in political matters. Many people do not know
how government works or have opinions on the major issues facing the county. Nonetheless, Lau
and Redlawsk (1997) found that about 75% of voters in the five U.S. Presidential Elections from
1972 to 1988 voted “correctly”. That is, people voted for the candidate that they would have also
chosen if they had possessed full information. The new conventional wisdom in political science
is that voters overcome their cognitive limits by using simple decision rules, commonly referred
to as heuristics. Heuristics enable voters to make reasonable decisions without possessing
complete information about candidates and issues. For example, instead of evaluating candidates
on multiple attributes, voters might compare candidates only on a limited set of attributes. A
common heuristic is single-issue voting whereby voters compare candidates only on the issue that
is most important to them.
Several conditions are expected to impact the use of heuristics. For example, people can
be expected to rely on heuristics more often if the decision is not very important. In the case of
U.S. presidential elections, the impact of a single vote on the election outcome is marginal. The
decision to vote might be motivated rather by fulfilling a civic duty than to find the best option. In
addition, heuristic use can be expected to be high in situations where people have to process a lot
of information and comparison of alternatives is difficult. Redlawsk (2004) reported experimental
results showing that heuristic use increases in more complex environments. In the information
rich environment of U.S. presidential elections, it seems impractical for voters to evaluate the
candidates on their positions on the complete set of issues. And how can voters evaluate
candidates on incomparable attributes? For example, voters might have difficulties in weighing
the candidates’ stands on the issues of abortion and the war in Iraq.
If a single issue dominates a campaign (such as the state of the economy in the 2008 U.S.
Presidential Election), many voters may make their voting decision solely based on the
candidates’ ability to handle this issue. We used the take-the-best heuristic to develop a model for
predicting the popular two-party vote in U.S. Presidential Elections that uses information about
which candidate is favored by voters for dealing with the most important issue. The goal was to
develop a model that can provide fast advice on which issues candidates should stress in their
campaign. Our approach uses publicly available polling data about perceptions of issue
importance and the candidates’ ability to handle the one issue seen as most important by voters.
Take-the-best heuristic
Take-the-best (TTB), developed by Gigerenzer and Goldstein (1996), is a heuristic for choosing
between alternatives based on a single piece of information. That is, starting with the most
important attribute, a decision maker looks whether this attribute discriminates between the
alternatives. If yes, he makes a decision for the alternative that is favored by the attribute. If not,
he moves on to the next most important attribute.
Czerlinksi et al. (1999) found TTB to be remarkably predictive. The authors compared
the heuristic to multiple regression and unit-weighting for 20 prediction problems (e.g.
forecasting high school drop out rates, attractiveness of men and women, homelessness and
mortality in U.S. cities, salaries of college professors, obesity among children, and fish fertility)
for which the number of variables varied between 3 and 19. Most of these examples were taken
from statistical textbooks where they had the purpose of demonstrating the application of multiple
regression analysis. Not surprisingly, multiple regression performed best when calculating in-
sample forecasts. However, when using cross-validation to predict data that had not been used to
build the model, TTB was most accurate, followed by unit-weighting. The advantage of TTB was
higher when there were fewer observations per predictor variable. Even for more than ten
observations per variable, multiple regression seldom outperformed TTB.
Martignon and Hoffrage (1999) discovered further conditions for the applicability of
TTB. They showed mathematically that a linear model cannot outperform TTB if the implicit
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importance weight of a variable is greater than the sum of the weights of all less important
variables.
TTB seems to provide a promising way to predict U.S. presidential election outcomes.
First, the number of observations is small. For forecasting U.S. presidential elections, data for the
majority of regression models is limited to about 25 elections. Information about how voters
perceive candidates to handle the issues was available only for the last ten elections. Second, a
single most important issue often dominates presidential campaigns. Third, it is now easy to
obtain information about the importance of issues. Polling institutions frequently ask voters to
name the single most important issue facing the country as well as to state which candidate can
do a better job in handling a particular issue. In recent years, the Internet has made such
information more readily available
Big-issue voting model
We developed the big-issue (BI) voting model to predict the outcome of U.S. presidential
elections. The model is based on information about how voters expect the candidates to deal with
the issue seen as most important. The model relies solely on information from polls and uses a
heuristic similar to TTB to determine the winner of the popular vote.
Data
We collected polling data on which issue voters regard as being the single most important issue
facing the country for the period from June to October in each election year (e.g., “What do you
think is the most important problem facing this country today?, Gallup Poll, October 3-October
5, 2008). To obtain this, we searched the iPOLL databank by the Roper Center for Public
Opinion Research, using the search string “most important issue OR most important problem”.
(Note that issue and problem are used interchangeably. See Wlezien (2005) for a different view.)
We obtained polling data for the last ten U.S. Presidential Elections from 1972 to 2008.
For example, during the 2008 election, voters consistently saw the state of the economy as the
most important issue. By comparison, in 2004 the economy and the war in Iraq alternated as the
most important issue.
We also obtained polling data on which candidate voters believed would be more
successful in solving the issue seen as most important. Again, we searched the iPOLL databank
by using the following search string: “[Republican candidate] AND [Democratic candidate] AND
[most important issue]”. For example, for the 2008 election, we searched for “McCain AND
Obama AND economy” and analyzed poll questions such as: “Regardless of which (2008)
presidential (election) candidate you support, please tell me if you think Barack Obama or John
McCain would better handle each of the following issues. How about...the economy?” (cf.
Gallup/USA Today Poll, October 10-October 12, 2008).
BI heuristic to determine the election winner
We used a variant of TTB to predict the winner of the popular vote in an election campaign. We
refer to this approach as the BI heuristic (BI-H). The underlying decision rule performs three
basic steps: (1) Identify the issue seen as most important by voters, (2) calculate the two-party
shares of voter support for the candidates on this issue and average them for the last three days,
and (3) predict the candidate with the higher voter support to win the popular vote. For a more
detailed description of our decision rule, see Appendix 1.
Note that the heuristic does not require information about historical elections to
determine the election winner. All necessary information can be derived from the polls in the
respective election year.
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BI model to predict the two-party vote shares
We used a simple linear regression to build the BI model (BI-M). We related the incumbent party
candidate’s 3-day rolling average of voter support on the most important issue (referred to as S
below) to the dependent variable, which was the actual two-party popular vote share received by
the candidate of the incumbent party (V). Based on data from the last forecasts prior to Election
Day in the ten elections from 1972 to 2008, we derived the following vote equation:
V = 27.0 + 0.50 * S
Thus, BI-M predicts that an incumbent would start with 27% of the vote, plus a share depending
on S. If the incumbent’s voter support on the most important issue went up by 10 percentage
points, the incumbent’s vote share would go up by 5 percentage points. Consistent with
traditional econometric models, BI-M reveals an advantage for the incumbent. Assuming the
candidates achieve equal voter support (i.e., S=50.0), the candidate of the incumbent party would
be predicted as the winner, receiving a vote share of about 52%.
Forecasting accuracy of BI
For each election year, the forecast origin was 100 days prior to Election Day, which was moved
forward by one day at a time until Election Eve. Thus, over the ten U.S. Presidential Elections
from 1972 to 2008, we obtained a sample of 1,000 forecasts.
Predicting the election winner
The performance of BI for predicting the winner (i.e., the hit rate) varied during the election
campaign as new polls became available. The hit rate is the proportion of forecasts that correctly
determined the winner of the popular vote.
Over all 1,000 forecasts, BI-H correctly predicted the winner 88% of the times. This
performance was achieved without incorporating information from previous election years.
BI-M was more accurate. The model’s forecasts were calculated through N-1 cross-
validation. This means that we used the observations from 9 elections to calibrate the model and
then made a forecast for the one remaining election. Over all 1,000 forecasts, BI-M correctly
predicted the winner 97% of the times. The model’s hit rate was especially accurate close to
Election Day: on each of the last five days prior to Election Day, the BI-M forecast correctly
predicted the winner in each of the ten elections.
Combining forecasts of BI-H and BI-M was expected to further increase accuracy. BI-H
predicts the incumbent to win only if the incumbent achieved a higher voter support than the
opponent. In incorporating an advantage for the incumbent, BI-M can still predict the incumbent
to win, even if the opponent has a slight advantage in voter support for the most important issue.
Table 1 shows combined forecasts of BI-H and BI-M. The combined forecasts reveal
how often both approaches agreed in their forecasts of the winner. In 87% of all forecasts, both
approaches correctly predicted the winner, whereas they simultaneously missed the winner only
2% of the times. The cases in which the forecasts from both approaches were wrong were limited
to the elections in 2000 and 2004, both of which were very close. In 11% of the cases, BI-H and
BI-M disagreed, which can be useful as an indicator of uncertainty.
––––– Table 1 about here –––––
Predicting the incumbent’s two-party share of the popular vote
Although it is not the primary goal of this study to improve the accuracy of election forecasts, we
used well-established election forecasting methods as a benchmark to assess accuracy. Most
models issue their forecast around Labor Day in the respective election year, which is usually
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about eight to nine weeks prior to Election Day. Table 2 reports the forecasts of BI-M calculated
9 weeks, or 63 days, before Election Day. Again, the forecasts were calculated through N-1 cross
validation. Over all 10 elections, the mean absolute error (MAE) was 3.0 percentage points.
––––– Table 2 about here –––––
The critical test is how well the models forecast prospectively (that is, for years not included in
the estimation sample). We generated ex ante forecasts for the last three elections from 2000 to
2008 by successive updating. That is, only data from elections prior to the respective election
year were used for building the model. To predict the 2008 election, data on the 9 elections from
1972 to 2004 were used; for the 2004 election, data on the 8 elections from 1972 to 2000 were
used, and, for the 2000 election, data on the 7 elections from 1972 to 1996 were used.
Table 3 shows ex ante forecasts from BI-M and eight econometric models. Most of the
forecasts from the econometric models were published in PS: Political Science and Politics,
34(1), 37(4), and 41(4). The forecasts for Fair’s model were obtained from his website
(http://fairmodel.econ.yale.edu). For an overview of the predictor variables used in most of the
models, see Jones and Cuzán (2008).
–––– Table 3 about here –––––
Although drawing on less information, the BI-M provided competitive forecasts. It yielded a
lower MAE than four of the eight econometric models and than the typical model. Furthermore,
one model that yielded a lower MAE than BI-M predicted the wrong winner in the 2004 election.
BI-M correctly predicted the winner in all three elections.
Relative accuracy of BI and the Iowa Electronic Markets
In recent elections, the number of polls has grown rapidly and new polls are published almost on
a daily basis. As a result, the BI forecasts may change frequently. Thus, we compared the daily
forecasts of BI-H and BI-M to forecasts from prediction markets, which also provide daily
updated forecasts.
Betting markets to predict election outcomes have an interesting history. Rhode and
Strumpf (2004) studied historical betting markets that existed for the 15 presidential elections
from 1884 through 1940 and concluded that these markets “did a remarkable job forecasting
elections in an era before scientific polling” (2004:127). In 1988, the Iowa Electronic Market
(IEM) was launched as a futures market in which contracts were traded on the outcome of the
presidential election that year. Initially, the IEM, commonly viewed as a prediction market,
provided more accurate election forecasts than trial-heat polls. In comparing IEM vote-share
prices with 964 trial-heat polls for the five presidential elections from 1988 to 2004, Berg et al.
(2008) found that IEM market forecasts were closer to the actual election results 74% of the time.
We compared the relative performance of BI and the IEM’s vote-share and winner-take-
all markets. The vote-share markets provide a quantitative forecast of the two-party popular vote-
shares achieved by the candidates. Winner-take-all markets provide a forecast of which candidate
will win the popular vote. For the six elections from 1988 to 2008, Table 4 shows the hit rates of
BI-H, BI-M (forecasts calculated by N-1 cross validation), and the two IEM markets for the last
100 days prior the Election Day. For each day, we used the last traded price of the two IEM
markets.
––––– Table 4 about here –––––
BI-H and BI-M performed well and yielded higher hit rates than the vote-share markets. The
winner-take-all markets, available from 1992 to 2008, performed as well as BI-H, but were
inferior to BI-M, which achieved 94% correct predictions.
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By comparison, the IEM vote-share markets were more accurate than BI-M in predicting
the actual vote-shares. Across the 100 days in the forecasting horizon, the MAE over the six
elections from 1988 to 2008 was 1.7 for the vote-share markets and 2.4 for BI-M. The superiority
of the IEM for predicting vote shares is not surprising as participants in prediction markets can
draw on information from many sources, most notably trial-heat polls. However, the results
suggest that BI-M may improve on the accuracy of prediction markets by providing an accurate
prediction of the election winner.
Discussion
The state of election forecasting has progressed to the point where it is possible to get highly
accurate forecasts for major elections. However, one area that has received little attention is how
to use forecasting as an aid to those involved with political campaigns. Although traditional
econometric models reveal that the incumbent’s chances to win decrease if he is unpopular, the
economy is doing poorly, or the federal budget deficit increased during his administration, it is
difficult for political parties and candidates to take action based on such forecasts. Such models
provide limited or no advice with respect to questions such as what type of candidate a party
should nominate or what issues should be stressed in the campaign.
Implications of the BI model for campaign strategies
In using information about how voters perceive the candidates to handle the most important issue,
the BI model can be used for advising those involved in political campaigns. To the best of our
knowledge, the BI model is the first model that incorporates such information.
The BI model uses TTB to illustrate how information on issues can be effective for
forecasting the election outcome. The model does not aim at predicting individual voting
behavior. While individual voters may use many different voting strategies, the model suggests
that the electorate as a whole behaves as if its voting decision is based on the candidates’ ability
to handle the single most important issue.
The BI model is simple to use and easy to understand, and it has decision-making
implications. Political candidates can take action upon the forecast and develop campaign
strategies. Two different strategies seem conceivable for allocating campaign efforts: (1) to be
seen as the best candidate for dealing with the most important issue or (2) to change voters’
perceptions about which issue is most important.
The first strategy seems difficult to pursue as voter perception of which candidate will do
better in handling a particular issue seldom changes. The reason is party identification. Each party
has a distinct issue handling reputation, depending on their party constituency, which changes
very slowly if at all. In reporting a small sample of polling data for the period from 1988 to 1991,
Petrocik (1996) found that Democrats were favored for welfare issues (e.g. health care and social
security), whereas Republicans had advantages for social issues (e.g. crime and upholding moral
values) and issues related to foreign policy and defense. Perceptions of the parties’ performance
on handling economic issues were mixed.
The second strategy seems more promising; that is, trying to change voters’ perceptions
about the importance of issues. An example is the 2004 election: although only two issues were
seen as most important (the war in Iraq and the economy), voters’ perceptions about the
importance of these issues changed seven times, more than in all other elections. While Bush was
favored on solving the issue with the war in Iraq, Kerry was seen as more competent in dealing
with the economy. During the 100 days prior to Election Day, the economy was seen to be the
most important issue on 86 days, despite the fact that foreign policy (i.e. the situation in Iraq and
the war against terrorism) was the dominant theme throughout the campaign. However, in the six
polls during the last five days prior to Election Day, the war in Iraq was on average seen as most
important. Our model suggests that this change in issue importance helped Bush win the election.
Similarly, in the 2000 election, our model incorrectly predicted Bush to win the popular vote
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early in the campaign, when moral values were seen as the most important issue. Bush’s early
lead might have been a response to his attacks of the Clinton scandals: restoring “honor and
dignity” in the White House was a major theme in Bush’s campaign. However, later in the
campaign, most likely due to effective campaigning by Gore, domestic issues like health care,
social security, and education caught the attention of the public.
These observations suggest that candidates should aim at promoting issues for which they
are favored in the eyes of voters. In fact, candidates have often been found to behave this way. In
analyzing New York Times articles, published in October in the election years from 1952 to 1988,
Petrocik (1996) found that candidates focused on issues for which voters traditionally favored
their party and rarely attempted to change voters’ opinion on issues that favored the opposing
party.
Spotting emerging issues
Since issue-handling reputations are difficult to change, candidates are advised to gain
competence on issues that are likely to emerge. Thus, spotting emerging issues becomes an
important task for political candidates. A new approach that can be useful for the early
identification of emerging issues relies on information about Internet searches. Search engines
provide information about how many searches have been done over time for certain keywords in
a certain geographical area (relative to the total number of searches or to the number of searches
within a predefined category). Thus, search query data should be useful to measure the
importance of certain issues: the more users search for a certain issues, the greater is the
importance that is likely to be attached to it.
We obtained search query data from Google Insights for Search to analyze how the
importance of the two most important issues in the 2004 election changed in the run-up to the
2008 election. Figure 1 shows the growth in searches (with respect to the first date on the graph)
for the keywords “Iraq” and “economy” from January 2004 to November 2008. (The data is
limited to searches that were conducted within the U.S. and within the category “Society”.) As
can be seen, searches for “Iraq” were most popular during most of 2004. However, since
September 2007 searches on “economy” took over. Even though the war in Iraq was still a key
issue in the 2008 campaign before the economic crisis, keyword searches already indicated the
increasing importance of economic issues.
Figure 1: Keyword popularity on Google Insights for Search for “Economy” and “Iraq” (2004 - 2008)
The BI model provides an easy and intuitive way to track campaigns. This is a major advantage
of the model compared to traditional econometric models. Our results suggest that candidates
should focus on raising and promoting issues for which they are favored among voters, especially
new issues that have not yet received much public attention.
Conditions for the BI model
The BI model is expected to work well if the most important issue is of widespread interest and
intense public importance. However, it may lead to poor forecasts if there is no single issue that is
clearly more important than others. In such situations, issues that were ignored may yield a
different preference order among candidates.
The data appeared to conform to these expectations. Table 5 lists the performance of the
BI combined forecasts in predicting the election winner, along with the issues seen as most
important by voters in each election year. The BI combined forecasts were particularly accurate
in elections, where (i) the most important issue never changed (as in 1972, 1976, and 2008) or (ii)
the issues seen as most important belonged to the same category (as for economic issues in 1980
and 1992). If a candidate is favored for handling the issue of inflation, it is likely that he is also
favored for handling the economy in general.
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––––– Table 5 about here –––––
By comparison, the BI combined was least accurate for the three elections in 1988, 2000, and
2004. For these elections, voter perception of which issue is most important changed most
frequently and/or the most important issue belonged to different categories. In such situations,
focusing on the single most important issue might not be enough. It might be worthwhile to
consider information on a larger number of issues.
Issue-indexes: An alternative to the single most important issue
Given a situation under which (i) a large number of causal variables are important and (ii) there is
good prior knowledge on the importance of variables (and the directions of effects), one might
want to consider the index method. In using this old forecasting method, analysts prepare a list of
key variables and specify from prior evidence whether they are favorable (1) or unfavorable (0) in
their influence on a certain outcome. Then, the analysts simply tally the scores and use the totals
to calculate the forecast. The index method also draws on the idea of unit-weighting, also known
as Dawes’ rule or tallying.
Lichtman (2008) was the first to use the index method for election forecasting. His
“Keys” model for forecasting U.S. presidential election winners assigns values of zero or one to
an index of thirteen predictor variables The model predicts the incumbent party to lose the
popular vote, if it loses six or more of the thirteen keys. Although the keys include two measures
of economic conditions, they do not include information on specific issues that concern voters in
a particular election. Instead, the keys include questions such as whether the incumbent president
was involved in a major scandal or whether the current administration was affected by foreign or
military success (or failure). The “Keys” model provided correct forecasts retrospectively for all
of 31 elections and prospectively for all of the last 7 elections. No model has matched this level
of accuracy in picking the winner of the popular vote.
Armstrong and Graefe (in press) used an index of 59 biographical variables to predict the
popular vote winner in the 29 U.S. presidential election winners from 1896 to 2008. The variables
measured, for example, whether a candidate was married, went to a prestigious college, or was
taller than the opponent. This biographical index model correctly predicted the winner in 27 of
the 29 elections and yielded out-of-sample forecasts that were as accurate as the best of seven
econometric models.
In addition to providing accurate forecasts, both models have decision-making
implications for political campaigns as they advise political parties in who they should nominate.
For example, the “Keys” model includes variables that suggest that parties should choose
candidates who are considered national heroes or highly charismatic. The biographical index
model provides an extensive checklist that helps parties to evaluate the chances of different
candidates to win an election.
The index method is an effective approach for including much information in a model.
Thus, it should be useful for capturing information about how voters perceive the candidates’
ability to handle a set of issues. Such information could be a valuable addition to the BI model as
it could increase certainty in the forecast; in particular in election campaigns in which many
issues are seen as important. We are currently investigating whether an issue-index model might
offer additional useful information for election campaigns (Graefe & Armstrong 2010).
Predicting multi-party elections
Although the BI model was developed and tested for the two-party system of U.S. presidential
elections, it may also be valuable for forecasting election outcomes in multi-party systems.
However, generalizing TTB to multiple-alternative outcome tasks is not without difficulties.
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For example, the decision-maker has to decide how to proceed if there is no one best
alternative for the most important attribute. Rieskamp and Hoffrage (1999) suggested that, when
moving on to the next most important attribute, one could either compare against all alternatives
or compare only the alternatives that tied on the previous attribute.
Similar problems occur if there is no single most important attribute. Imagine a situation
with three parties (A, B, C) and two issues (x, y) of similar importance. Voter support for party A
is 55% for issue x and 10% for issue y; or A(55,10). The respective numbers for parties B and C
are B(10,55) and C(35,35). If a decision-maker makes a decision solely based on issue x (or y),
he would choose party A (or B). If he would average voter support across issues, he would
choose party C. Note that our decision rule, described in Appendix 1, avoids this problem. If
necessary, the decision rule averages results from previous polls and thus ensures that it is always
possible to identify the most important issue. Future research should evaluate the usefulness of
the BI model for predicting multi-party elections.
Conclusions
The take-the-best heuristic generated accurate forecasts based on voters’ perceptions on how the
candidates will handle the single most important issue facing the country. In a cross-validation of
1,000 out-of-sample forecasts for the ten U.S. presidential elections from 1972 to 2008 (one
forecast on each of the last 100 days per election year), the model correctly predicted the winner
of the popular vote in 97% of all forecasts. This quick and simple model can help candidates to
develop campaign strategies.
For the six elections from 1988 to 2008, the model yielded a higher number of correct
predictions of the popular vote winner than the Iowa Electronic Markets. For the three elections
from 2000 to 2008, the BI model forecasts of the incumbent’s two-party share of the popular vote
yielded accuracy similar to the typical econometric model. In using a different method and
different information than traditional approaches, the BI model is expected to contribute to
forecasting accuracy and should be combined with other forecasting models.
Acknowledgments
Andrew Gelman, Dan Goldstein, and three anonymous reviewers provided helpful comments.
Kesten C. Green provided peer review.
References
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Table 1: Combined forecasts of BI-H and BI-M (percentages)
2008
2004
2000
1996
1992
1988
1984
1980
1976
1972
Correct
91
39
89
100
100
55
100
99
99
100
Tie
9
43
6
0
0
45
0
1
1
0
Wrong
0
18
5
0
0
0
0
0
0
0
[correct if both predicted correct winner, wrong if both predicted wrong winner, tie if forecasts disagreed]
- 12 -
Table 2: Out-of-sample forecasts and absolute errors of BI-M (9 weeks prior to Election Day)
Election
year
Actual
Predicted
Absolute
error
1972
61.8
55.7
6.1
1976
48.9
49.8
0.9
1980
45.4
47.6
2.2
1984
59.2
52.8
6.3
1988
53.9
51.3
2.6
1992
46.5
47.4
0.9
1996
54.7
55.8
1.1
2000
50.3
56.4
6.2
2004
51.2
51.7
0.5
2008
46.3
49.4
3.1
MAE
3.0
- 13 -
Table 3: BI-M vs. benchmark models: Absolute errors of out-of-sample forecasts
(last three elections from 2000 to 2008, calculated by successive updating)
Model
Approx. date of forecast
2000
2004
2008
MAE
BI-M
Late August / early September
6.8
0.8
3.1
3.6
Econometric models
Norpoth
January
4.7
3.5
3.6
3.9
Lockerbie
May / June
10.0
6.4
4.5
7.0
Fair
Late July
0.5
6.3
2.2
3.0
Abramowitz
Late July / August
2.9
2.5
0.6
2.0
Holbrook
Late August / early September
10.0
3.3
2.0
5.1
Lewis-Beck and Tien
Late August
5.1
1.3*
3.6
3.3
Wlezien and Erikson
Late August
4.9
0.5
1.5
2.3
Campbell
Early September
2.5
2.6
6.4*
3.8
MAE (error of the typical econometric model)
5.1
3.6
2.6
3.8
* predicted the wrong winner
- 14 -
Table 4: Hit rate (in %) of BI-H and BI-M and the IEM vote-share and winner-take-all markets
Election year (n=100 per election)
Mean
Mean
1988
1992
1996
2000
2004
2008
1988-2008
(n=600)
1992-2008
(n=500)
BI-H
55
100
100
89
39
100
81
86
BI-M
100
100
100
95
82
91
95
94
IEM vote-share
67
63
100
29
85
100
74
75
IEM winner-takes-all
-
86
100
47
96
100
-
86
- 15 -
Table 5: BI performance and most important issues as seen by voters
% of combined BI forecasts
of election winner
Problem category
Election
year
Correct
Tie
Wrong
No. of most
important
issues
Economic
Foreign
Other
No. of times
voter
perception
changed
2008
91
9
0
1
1
0
0
0
2004
39
43
18
2
1
1
0
7
2000
89
6
5
4
0
0
4
6
1996
100
0
0
4
2
0
2
3
1992
100
0
0
2
2
0
0
1
1988
55
45
0
3
2
0
1
6
1984
100
0
0
3
2
1
0
3
1980
99
1
0
2
2
0
0
2
1976
99
1
0
1
1
0
0
0
1972
100
0
0
1
0
1
0
0
- 16 -
Figure 1: Keyword popularity on Google Insights for Search for “Economy” and “Iraq” (2004 - 2008)
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