Prez Prediction, Past Elections, Leave-One-Out Check

We will use this model to predict past elections (by canceling out that year's so it cannot tilt the prediction in any way). We will also use it for the 2016 election prediction.

from StringIO import StringIO
import statsmodels.formula.api as smf
import pandas as pd
s="""year,gdp_growth,net_approval,two_terms,incumbent_vote
2012,1.3,-0.8,0,52
2008,1.3,-37,1,46.3
2004,2.6,-0.5,0,51.2
2000,8,19.5,1,50.3
1996,7.1,15.5,0,54.7
1992,4.3,-18,1,46.5
1988,5.2,10,1,53.9
1984,7.1,20,0,59.2
1980,-7.9,-21.7,0,44.7
1976,3,5,1,48.9
1972,9.8,26,0,61.8
1968,7,-5,1,49.6
1964,4.7,60.3,0,61.3
1960,-1.9,37,1,49.9
1956,3.2,53.5,0,57.8
1952,0.4,-27,1,44.5
1948,7.5,-6,1,52.4
"""
df = pd.read_csv(StringIO(s))
regr = 'incumbent_vote ~ gdp_growth + net_approval + two_terms'
results = smf.ols(regr, data=df).fit()

def f(year):
    df2 = df[df['year'] != year]
        results2 = smf.ols(regr, data=df2).fit()
        conf = results2.conf_int()
            pred = np.array(df[df['year'] == year])[0][:-1]; pred[0] = 1.
            return np.dot(pred, conf)
            print 'bush/clinton'; print f(1992)
            print 'gore/bush'; print f(2000)
            print 'bush/kerry'; print f(2004)
            print 'mccain/obama'; print f(2008)
            print 'obama/romney'; print f(2012)

Once you run this on past elections, and using 95% confidence interval for the coefficients, the results for the popular vote percentage is,

bush/clinton
[ 43.68  52.47]
gore/bush
[ 48.31  60.68]
bush/kerry
[ 50.66  55.79]
mccain/obama
[ 41.05  46.15]
obama/romney
[ 49.81  54.45]

Bush / Clinton guess [43% 52%] points to a likely Bush loss. Clinton won. Bush/Kerry points to a definite Bush win, he won. Mccain / Obama says definite McCain loss, he lost. Bama / Romney, definite Bama win, he did.

The freak event is Bush / Gore. Two things there - there was some possibility for Bush win, and second, well.. the election was stolen. Plus, Gore won the popular vote (that's what the model predicts).