Download After the 2004 presidential election, Walker

PRESIDENTIAL ELECTION FORECASTS *
David A. Walker
McDonough School of Business
Georgetown University
[email protected]
I would like to acknowledge the research assistance of Chrissy Hunt and the suggestions of Matthew
Thayer Billett, the Henry B. Tippie Research Fellow in the Tippie College of Business at the University of
Iowa and Professor Keith Ord at the Georgetown McDonough School of Business.
BACKGROUND
Recent U.S. presidential elections have suggested how difficult it can be to predict vote shares
even on the basis of proximate polls: ten days before each election of George W. Bush, he was
projected to lose. Pollsters have faced an additionally difficult task for the 2008 election because of
the potential for a Bradley or an inverse Bradley effect, the potential for distinctive contributions
from early voting and/or new voters, and such idiosyncratic factors as the initial popularity surge and
subsequent strategic difficulties of vice-presidential candidate Sarah Palin. All such considerations
lead back to an interest in modeling the vote, in the hope that a successful model can escape these
difficulties.
To cut directly to the chase: the noon polls of November 2 predicted Barack Obama as the
next president: the Real Clear Politics average had it Obama 50.4% to McCain 43.7%. A different kind
of mass opinion registry is captured by the Iowa Electronic Market (IEM,
www.iemweb.biz.uiowa.edu), which had Obama with a 53.0 percent vote share. By comparison,
application of perhaps the best-known modeling approach, from Ray Fair on his web site
(www.fairmodel.econ.yale.edu), predicted that Obama would receive 51.91% of the vote. This
suggests a certain parallelism, though all such approaches still had to be viewed circumspectly, since
in mid-October of 2000 and 2004, Gore and Kerry had appeared to be winners respectively, while in
October of 1980, Carter held a large lead in the polls over Reagan.
Considering the challenges that pollsters face in determining likely voters, in contacting a
sufficiently large sample thereof, in not excluding those who use only cell phones, and in
representing people who either refuse to respond or intentionally mislead the questioner, it is
surprising that polls predict so many elections within a percentage point or two. Modeling avoids
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some of these difficult issues, but surely introduces others. The hypothesis of this study is that
economic and financial modeling has more to contribute to forecasting the presidential election in
2008 than in many other years. Nevertheless, some of the previous modeling efforts have been
highly successful.
After the 2004 presidential election, Walker (2006) presented a macro-economics model,
based on Fair (2004), which had a prediction error of 0.9 percent from the results for the two majorparty candidates. The current paper adds two models: a re-estimated Walker “macro model”,
estimated through 2004 with 2008 predictions, and a “market model”, in which the percentage
change in the Dow Jones Industrial Average between January and October of each election year is a
newly critical variable.
The macro model predicts the election of Barack Obama under the joint conditions that the
error term is at least two standard deviations above its mean and that there is virtually no growth in
the economy (as reflected by the October 30 per capita GDP report from the U.S. Bureau of
Economic Analysis). The market model predicts the election of Barack Obama in all cases. The
literature preliminary to these models is otherwise well known among interested scholars. The
International Journal of Forecasting (IJF, 2008) provides an array of current papers by leading
scholars who have contributed to this literature over the past 20 years, except for Fair who
continually updates his research and forecasts on his own website (www.fairmodel.econ.yale.edu).
The lead IJF paper in spring 2008 by Campbell and Lewis-Beck provides a concise review of much of
the current literature.
MACRO MODEL
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The Fair and the Walker Macro models explain the U.S. presidential vote for the incumbent
party as a function of macro-economic and structural variables. The major differences between the
two are that the author (Walker) insists that the election years during the Korean War, the Viet Nam
War, and the War in Iraq be coded as war years and that autocorrelation be removed. The Walker
Macro model, estimated for 1920 - 2004 (with t-statistics in parentheses), is:
V= 45.78 + 0.72 g – 0.48 I + 1.27 N – 2.13 Part + 4.88 WAR – 0.73 et-1 R2=.88 F=25.62
(27.59) (8.20) (-2.33) (5.91) (-4.63)
(2.67)
( -4.35)
DW = 2.33
where
V = incumbent party share of the two-party presidential vote
g = growth rate of real GDP in the first three quarters of the election year
I = absolute value of the growth rate of GDP deflator in the first 15 quarters of the
administration , except for war years when the values are 0; 0 for 2008
N = number of quarters of the first 15 of an administration when g is greater than 3.2%,
except 0 for war years; 0 for 2008
Part = 1 if there is a Democratic incumbent at the time of the election and -1 if there is a
Republican incumbent; -1 for 2008.
WAR = 1 for 1920, 1944, 1948, 1952, 1968, 1972, 2004, 2008 and 0 otherwise.
The reduced Macro model for 2008 is V = 52.80 + .72 g – 0.73 et-1
MARKET MODEL
The market model captures the impact of the financial markets on presidential elections as an
alternative approach. The current financial crisis makes the consideration of such a model an
important contrast. The percentage change in the Dow Jones Industrial Average between January 1
and October 31 of each presidential election year, %∆DJ, is included to represent the financial market
activity during the election year. To reflect the increasingly important role of financial markets and
investments over time, %∆DJ is multiplied by T (= 1 for 1920, 2 for 1924,…,22 for 2004, 23 for 2008).
The other variables are the same as the MACRO model and Per = 1 if the incumbent is running for
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election and 0 otherwise. (The coefficient of Per is not statistically significant for 1920-2004 for the
Macro model.)
V= 42.05 + 0.54 g – 0.86 I + 1.57 N + 3.78 Per – 2.91 Part + 5.85 WAR + 0.015 %∆DJT R2=.89 F=24.66
(18.76) (4.85) (-2.93) (5.27)
(3.50)
(-4.84)
(2.32)
(2.96)
DW = 2.21
The reduced Market model is V= 50.80 + 0.54 g + 0.345 %∆DJ at T= 23.
FORECASTS
The two models were applied to forecast the 2008 presidential election, assuming alternative
values for g = GDP growth and et-1 for the Macro model (Table 1) and for g and %∆DJ (at T= 23) for
the Market model (Table 2). Fair’s GDP growth rate of 0.22 percent is the appropriate rate to forecast
the 2008 election since his data, 1920-2004, were employed to estimate the models. The
percentages in the tables are forecasts of a Republican victory, since V represents incumbency.
The Macro model predicted that the Democrats would win the 2008 presidential election
unless (1) the GDP growth rate for the first three quarters were 0.5 percent (at least 0.32 percent)
and et-1 were two standard deviations below its mean of zero or (2) et-1 were within one standard
deviation below its mean. A Democratic victory would be forecast from the Macro model for 7 of the
16 scenarios in Table 1. The forecast from Table 1 for the Macro model was that the Obama receive
51.59 percent of the popular vote and McCain receive 48.41 percent (GDP growth = 0.22 percent and
et-1 were at least two standard deviations below its mean of zero). Fair predicted that Obama would
receive 51.91 percent.
The Market model produced very different forecasts. For a declining stock market of even half
of the current 30 percent this year, plus GDP growth below +0.5, the Democratic candidate was
forecast to win. The opening Dow Jones Industrial Average in January of 2008 was 13,262, and the
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closing on October 31 was 9,325, a decline of 30 percent. For g= GDP growth of 0.22 percent and a 30
percent decline in the Dow for the election year, the Market model predicted that Obama receive
59.43 percent of the vote and McCain receive 40.57 percent.
FORECAST ERRORS
The forecast errors (Actual Obama two party vote share minus Obama forecast) are given in
Table 3. The forecasts for Real Clear Politics (RCP Average of 11 polls), the IEM, and Fair’s model were
collected Sunday, November 2, at noon. The RCP Average Allocated is calculated by allocating the 5.9
Sunday undecided for RCP Average in the proportions of 50.4/(43.7+50.4), which is a highly favorable
method toward the pollsters’ forecasts, in addition to RCP being an average of 11 polls.
Table 3. Forecasts and Errors
Nov. 2, noon
Forecast
Obama/McCain
Actual vote share
53.24/46.76
RCP Average
50.4/43.7/5.9
RCP Average Allocated*
53.56/46.44
ABC News/Washington Post
54.6/45.4
Fair forecast
51.91/48.09
IEM vote share
53.0/47.1
Walker Macro model
51.59/48.41
Walker Market model
59.43/40.57
* proportionate allocation of undecided
Actual - Obama forecast
= Error
+2.8
-0.3
-1.4
+1.3
+0.2
+1.6
-6.2
On the basis of the actual vote share for President-elect Obama at noon on Friday, November
7, the Iowa Electronic Market had the smallest forecast error (+0.2), followed by the RCP Average
with undecided allocated (-0.3). Among the members of the Real Clear Politics polls, the closest one
to the result was the ABC News/Washington Post poll, predicting 53 percent for Obama, 44 percent
for McCain plus 3 percent undecided, or 54.6 and 45.4 with the 3 percent allocated proportionately.
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All of the forecasts in Table 3 are fairly close except for the Walker Market Model. The 30percent decrease in the Dow Jones between January 1 and October 31 of 2008 is twice as large as
any election year since the 1930s. A value so far outside the data range may explain the extreme
forecast for Obama from the market model. The Fair and Walker macro models provide similar
forecasts, which is somewhat surprising given the methodological differences.
CONCLUSION
Tis the season for presidential election forecasting. Including a variable to reflect the impact
of stock market changes on vote shares provided a very different perspective and forecast for the
2008 presidential election and gives consideration to the impacts of the current financial crisis. The
forecast in this case was the election of Barack Obama, regardless of numerous factors that have
been found to be important in other studies. The Obama forecast by Fair of 51.91 percent or the
Market model forecast of 51.59 (g=0.22 and et-1 = 3 SD) would not have been a big surprise, but the
Market forecast of 59.43 was not highly likely. The IEM is an interesting contrast to models and polls
and has an excellent record over a number of presidential elections. Nevertheless, for the reasons
discussed above, models should predict better than individual polls if the model specifications and
the data are correct.
REFERENCES
Campbell, James E. and Michael S. Lewis-Beck editors, (2008), International Journal of Forecasting,
April-June.
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Erikson, Robert E. and Christopher Wlezien, (2008), “Are Political Markets Really Superior to Polls as
Election Predictors?”, Public Opinion Quarterly, forthcoming.
Fair, Ray C., (2008), “The Effect of Economic Events on Votes for President: 2008 Update,”
http://fairmodel.econ.yale.edu/vote2008/computev.htm
Walker, David A., (2006), “Predicting Presidential Election Results,” Applied Economics, Vol. 38, pp.
483-490.
www.iemweb.biz.uiowa.edu/quotes, Iowa Electronic Market
www.realclrearpolitics.com/epolls/2008/president/us, Real Clear Politics
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Table 1. Macro Model: Republican Vote Share
V = 52.80 + .72 g – 0.73 et-1
Values of g 2008, Q1-Q3
-0.5
0
0.22
0.5
error = et-1
Mean = 0
1 SD = 2.078
2 SD = 4.156
3 SD = 6.234
52.44
50.92
49.41
47.89
52.80
51.28
49.77
48.25
52.96
51.44
49.92
48.41
53.64
51.64
50.13
48.61
Table 2. Market Model: Republican Vote Share
V= 50.80 + 0.54 g + 0.345 %∆DJ
values of
% Δ DJ
1/1/08 to
10/31/08
-15
-20
-25
-30
Values of g 2008, Q1-Q3
-0.5
0
0.22
45.36
45.62
45.74
43.63
43.90
44.02
41.90
42.18
42.29
40.18
40.45
40.57
0.5
45.90
44.17
42.44
40.72
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