While conventional wisdom holds that the first delegate selection events in Iowa and New Hampshire are important influences on the outcome of the presidential selection process, scholars increasingly question whether victories in these ‘bellwether’ contests are sufficient to propel darkhorse candidates to the nomination. This study utilizes four OLS regression models to predict nomination outcomes from 1980 to 1996 where the incumbent president did not sit for reelection. Earlier research demonstrated the possibility of forecasting presidential nominations by examining the results of (1) public opinion polls; FEC records regarding (2) money raised; and (3) cash reserves; and (4) whether candidates were southern Democrats (Mayer 1996a; Adkins and Dowdle 2000). Utilizing measures representing the outcome of the Iowa caucuses and the New Hampshire primary, this study contrasts the effect of momentum from these early contests on final primary vote totals. Evidence suggests that New Hampshire plays a role in determining the ordinal ranking of candidate finishes, but not necessarily the winner of the party nomination.

In order to demonstrate challenges to conventional wisdom (Aldrich 1980a, b; Bartels 1985 1988; Orren and Polsby 1987), this article develops several forecasting models of the presidential primary vote to compare to a baseline model of the aggregate primary vote (APV) that uses pre-primary and New Hampshire primary data. The models indicate that candidates’ Gallup poll position and cash reserves are significant positive predictors of a candidates’ primary vote share, though there are differences between forecasting models of the primary vote in Democratic and Republican nomination campaigns. Parallel models incorporating results of the New Hampshire primary improve the predictive power of the baseline model, indicating that the bellwether primary has a “correcting” effect on the relative standings of some candidates seeking a presidential nomination. This effect is substantially greater for Democrats than for Republicans.

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Using data available at the culmination of the "exhibition season," which ends prior to the Iowa caucuses, Adkins and Dowdle (2000) attempted to forecast the outcome of presidential nominations in the postreform era using two models to predict the percentage of the primary vote total that presidential aspirants received in races in which the incumbent president did not sit for reelection. 2 Both models employ

This research explores whether data on polling, campaign expenditures, and cash reserves of campaigns collected in the year prior to the presidential election can produce accurate predictions of party nominees. Findings suggest that efforts to forecast nominations may begin as early as the first quarter of the year before the election for Republicans, where the trade‐off between timeliness and accuracy is negligible. In Democratic races where a sitting vice president does not run for president, exhibition season predictions are murky and the cost of trading timeliness for accuracy is higher.

A number of scholars successfully modeled and predicted presidential nomination outcomes from 1996-2008. However, dramatic changes occurred in subsequent years that would seem to make replicating these results challenging at best. Building on those earlier studies, we utilize a series of OLS models that included measures of preprimary resources and early campaign successes or failures to forecast that Hillary Clinton and Donald Trump would win the Democratic and Republican presidential nominations in 2016. This outcome suggests that some fundamental factors governing nomination outcomes have not changed despite the conventional wisdom. N umerous models forecast general election outcomes by employing a variety of economic and political measures to make accurate predictions about whether the party in control of the White House will retain or lose the presidency (for an overview see Campbell 2012 ). In many ways forecasting presidential nominations presents a more challenging task. Important individual-level cues such as partisanship or systemic-level factors such as economic growth or the popularity of the incumbent are helpful in understanding why a voter might choose Bill Clinton over George W. Bush in 1992. Unfortunately, they are not useful in explaining why the same individual picked Paul Tsongas over Bill Clinton or Tom Harkin nine months earlier in the New Hampshire primary (Steger, Dowdle, and Adkins 2012 ).While the McGovern-Fraser reform movement of the early 1970s created a new system of presidential nominations designed to increase the role of voters in picking party nominees, a period of stability in the nomination process of both parties' emerged by the end of the 1980s (Barilleaux and Adkins 1993 ). As these contests became more routinized, a number of scholars attempted to forecast the results of the presidential primary season by utilizing factors such as polling, fi nancial resources, and elite support (Adkins and Dowdle 2000, 2001a, 2001b, 2005Mayer 1996 ; Steger 2000 ; see Steger 2008 for a comparison of the forecasts generated by the diff erent models). Momentum from performing well in early primaries was also found to play an important role in determining nomination outcomes (Bartels 1988 ), though there is some controversy about the precise eff ect of particular contests (Adkins and Dowdle 2001a ;Christenson and Smidt 2012 ;Hull 2008 ).At first glance, current events appear to have altered this equilibrium in at least two important ways. First, super PACs, a relatively new type of political committee that arose from the Speechnow v FEC and Citizens United v FEC court decisions in 2010, should alter the impact of traditional sources of campaign fi nance (Dwyre and Braz 2015 ). Second, the Republican elite has arguably fragmented in recent years, which should aff ect elite support on the process (Steger 2015 ). Since traditional forecasting models encountered diffi culty predicting the 2004 Democratic nomination correctly (Steger 2008 ), these new factors should make predicting recent...

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