# Technical Appendix – Will the race tighten as the election approaches?

I compiled all the polling data from RCP for the 2008 and 2012 Presidential elections with field work ending on or before October 2nd.  I then created daily averages, aggregating the polls by the date fieldwork was completed.  Most days have only one poll, but as the election nears multiple polls begin to appear.  So far in 2012, the day with the most poll releases was September 30th with five.  In 2008, there were three days in October with as many as nine polls reporting with a common final fieldwork day. I then used these common end-of-fieldwork days to measure the number of remaining Days Before the election in both 2012 and 2008.

I measure “closeness” in the polls by taking the simple absolute value of the difference in support between the two major-party candidates.  An Obama lead of three over Romney and vice versa both generate a closeness score of three.

Finally, I restricted the time period to the final 120 days before the election.  Some of the polling data for both elections dates back nearly two years.  I prefer to focus on the campaign itself and used the four months before the election as the basis of comparison.  Shortening the period to 90 or 60 days did not have a big effect on the results.

Here are the results from regressing the absolute margin on the number of days before the election, a “dummy” for 2012, and an “interaction” term multiplying the dummy with the number of days before.  Adding these two variables lets us estimate a separate relationship for each of the two elections.

```OLS; 120 days or less until Election Day (N=134)
Dependent variable: Absolute margin between the Candidates

coefficient   std. error   t-ratio   p-value
----------------------------------------------------------
2008 Estimates:
Constant      6.57763      0.534344     12.31     1.46e-23 ***
DaysBefore   −0.0350667    0.00826569   −4.242    4.17e-05 ***
2012 Estimates:
2012 Dummy   −3.08490      1.19412      −2.583    0.0109   **
2012*DaysB4   0.0334960    0.0158528     2.113    0.0365   **

Mean dependent var   4.156866   S.D. dependent var   2.638088
Sum squared resid    771.5533   S.E. of regression   2.436191