LEADING ECONOMIC INDICATORS ALLOW EARLY ELECTION FORECASTS

As the timeline of the campaign progresses, presidential elections are easier to predict from economic indicators and polls measured at the moment. ^{Footnote 3} One way to increase prediction accuracy would be to obtain early warnings about the values of the predictor variables. The Index of Leading Economic Indicators (LEI) serves this purpose. Our measure of cumulative LEI growth through the spring of the election year incorporates the objective economy up to that point and offers (imperfect) information about upcoming economic change. This, in turn, allows advance indication of changes in presidential approval (Wlezien and Erikson Reference Wlezien and Erikson1996). Further, with LEI growth controlled, the predictive power of early trial-heat poll results increases as a manifestation of non-economic issues on the outcome. ^{Footnote 4}

Table 1 presents the details for equations predicting the incumbent party vote from 1952 through 2012. Four models are presented, one for each quarter of the election year during which we measure the trial-heat polls. Cumulative LEI growth is fixed at its value for Quarter 13—the first quarter of the election year—in all models. ^{Footnote 5} The table shows that regardless of the quarter we measure trial-heat polls, both cumulative LEI growth and trial-heat polls are statistically significant as predictors. With time, the polls grow and LEI growth recedes in terms of predictive value. When trial-heat polls are measured in quarters 13 or 14, cumulative LEI growth through winter (January, February, March) tells more about the vote in November than do contemporary trial-heat polls. ^{Footnote 6} By Quarter 15, trial-heat polls overtake cumulative LEI growth, but even then the measure of cumulative LEI growth from Quarter 13 adds some predictive power.

Table 1

Predicting the Presidential Vote during the Election Year, 1952–2012

Note: Numbers in parentheses are standard errors. Cumulative LEI Growth = summed weighted growth in leading economic indicators through quarter 13 of the election cycle, with each quarter weighted .8 times the following quarter. Trial-heat poll results are for the quarter (13 through 16) indicated, and are missing in the first quarter of 1952, leaving 15 cases for analysis in the Quarter 13 model.

*p < .05; **p < .01 (two-tailed).

The value of the model is early prediction. For the past five presidential elections, we have used our model (as updated at that time) to predict the November vote using only Quarter 13 cumulative LEI growth and either presidential approval or trial-heat polls from Quarter 15. Our public forecasts have been close, picking the correct popular vote winner each time with an average absolute error of 1.6 percentage points of the two-party vote. ^{Footnote 7}

PREDICTING 2016 USING LEADING INDICATORS AND QUARTER 14 POLLS

What does our model suggest for 2016? For 2016, cumulative LEI growth is 0.22, just slightly below the average of 0.23 over the 16 elections between 1952 and 2012. Using RealClearPolitics.com, Quarter 14 Clinton-vs.-Trump trial-heat polls average 54.2% for Clinton. Plugging in our model from table 1 with trial-heat polls measured for Quarter 14 yields 52.2% as Hillary Clinton’s predicted share of the two-party vote in 2016, which is only a little more than we predict (51.8%) with LEI growth alone. Based on the standard forecast error, the estimate implies a 76% chance of a Clinton popular vote victory. ^{Footnote 9} Now, as of this writing (July and August, 2016), we actually are into Quarter 15 and so have more recent poll readings that we can incorporate. That we are in the midst of the party conventions requires us to pay special attention to timing, as these events are known to cause large swings in electoral preferences.

PREDICTING FROM LEADING INDICATORS PLUS POLLS BEFORE AND AFTER THE CONVENTIONS

In recent election years, the national party conventions were held in August or even into September. In 2016 they occurred in July, the earliest in the calendar since 1960. Historically, the conventions have considerable impact, with the leader in the polls afterward almost always winning the election (Erikson and Wlezien Reference Erikson and Wlezien2012b). This can be seen in table 2, which displays poll shares for the incumbent party candidate from one week before the first convention and then two weeks after the second convention, along with the final vote. ^{Footnote 10} There we can see that the leader in the polls before the conventions ultimately won the popular vote in 11 of the 16 elections; after the conventions, the leader won the popular vote in every year, bearing in mind that the polls were tied in 1980.

Table 2

Incumbent Party Candidate Poll Shares Before and After The Conventions, 1952–2012

Note: Numbers are two-party poll share averages one week prior to the first convention, and two weeks after the second convention, from Erikson and Wlezien (Reference Erikson and Wlezien2012b) and Erikson and Wlezien (Reference Erikson and Wlezien2014).

The conventions clearly can meaningfully impact the polls and the election outcome, and so we need to take into the account the timing of polls. Table 3 shows the resulting pre- and post-conventions equations. As indicated by the R-squareds, predictability increases using post-convention polls. Prior to the conventions, cumulative LEI growth is the strongest predictor. Afterwards, the polls dominate. (Also see Holbrook Reference Holbrook1996; Campbell Reference Campbell2008.) One interpretation is that the conventions help to clarify for the voters the fundamentals that drive the election, and an important aspect of these fundamentals is the state of the economy, which our cumulative LEI measure reveals in advance. ^{Footnote 11}

Table 3

Predicting the Presidential Vote Before and After the Conventions, 1952–2012

Note: N = 16. Numbers in parentheses are standard errors. Cumulative LEI Growth = summed weighted growth in leading economic indicators through quarter 13 of the election cycle, with each quarter weighted .8 times the following quarter. Trial-heat poll results are for the week prior to the first party convention and for two weeks after the second convention, as indicated.

** p < .01 (two-tailed)

Table 4 shows the out-of-sample forecasts using the two models, and here we can see that the model performs well before the conventions, “forecasting” the correct winner in 13 of 16 cases, which is slightly better than what we obtain using raw polls (11 of 16, per table 2). The post-conventions model works better still, correctly predicting the popular vote winner in all 16 elections since 1952.

Table 4

Summary Statistics for Out-of-Sample Forecasts Using Pre- and Post-Convention Polls, 1952–2012

Note: For each of the pre- and post-convention periods, the out-of-sample forecast for each election year represents the vote predicted from an estimated model that excludes the particular year. Predictive accuracy is the number of elections in which the equation “forecasts” the popular vote winner.

So, what did our model say about 2016 before the conventions began? Our 2016 pre-conventions trial-heat reading, from RealClearPolitics.com is 51.5% of the two-party vote for Clinton. Plugging into our pre-conventions equation in table 3 predicts 52.0% for Clinton, with a probability of winning of .72. This is only slightly less than what we forecasted in early-June, as discussed above.

What about after the conventions? For the polls centered on dates August 9-16, fully two weeks after the Democratic convention ended, the one available poll (from PEW) indicates 52.6% for Clinton. ^{Footnote 12} Inserting the number into our post-conventions equation in table 3 predicts 52.0% for Clinton, with a probability of victory of .82. Once again, this is little changed from what we predicted prior to the conventions and also earlier, in June. Our electoral expectations have remained quite stable.