To forecast presidential elections, I explore the dynamic of the vote ("time") and introduce a measure of candidate support that covers both the incumbent and the challenger. Stochastic models help identify the dynamic of the presidential vote as second-order autoregressive. The strength of the candidates is gauged by an index of electoral success in presidential primaries—in particular, whether the nominee won the first pnmary. Also included as a vote predictor is the economy, as measured by gross national product (GNP) growth and inflation in the election year. The forecasting equation predicts victory for Bill Clinton, with 57.1 % of the major party vote in November 1996. Time is on his side, in the sense that the autoregressive dynamic favors election of a presidential candidate whose party just captured the White House. But what predicts a comfortable margin is Clinton's edge in the candidate comparison, with the economy exerting little electoral pull this year.
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