Aaron Strauss provides more evidence that, compared to forecasts based on fundamentals, early polls give almost no information about election outcomes. Strauss writes the following about allocation of campaign resources:
The key to a effective strategy is determining, ex ante, which states will be pivotal on Election Day. Existing Bayesian election models are inappropriate for this game, as they estimate the current standings of the candidates or states rather than the final outcomes. I [Strauss] develop a Bayesian dynamic linear forecasting model that incorporates informative priors from historical regressions, updates based on in-cycle state and national polls, and accounts for the uncertainty of events that take place between the polls’ issuance and Election Day. National and state shocks are modeled as a reverse random walk beginning with the final outcome and moving backwards through time. Uncertainty about the final outcome is calculated by combining the random walk’s linearly decreasing variance over time, natural poll measurement error, house effects of national polls, and historically stable trends of election results. Using the resulting estimates of the states’ standings relative to each other, I simulate electoral vote outcomes and determine the probability of each state being pivotal. I find that early polls can be misleading to such an extent that putting any weight on them produces worse forecasts than solely relying on historical trends.