Alan Abramowitz, the respected political scientist from Emory University, has a forecast model he calls “time for change.” In 2016, he predicted a Trump victory in the popular vote — one of the few professional political analysts to do so.

While he missed on the Electoral College tally, the model presented an early indication that Trump was more than capable of winning the 2016 election.

His first forecast for the 2020 race featuring Donald Trump as an incumbent is out and, given the massive media campaign against him for the last two years, Trump appears to be in pretty good shape.

While the Democratic challenger is unknown, Abramowitz thinks “the presidential election is largely a referendum on that incumbent’s performance. The challenger’s characteristics and the general election campaign itself matter only at the margins.”

Instead, the author focuses on other factors both practical and historical, to develop his model.

The time for change model predicts the outcomes of presidential elections based on three factors: the incumbent president’s net approval rating in late June or early July, the change in annualized real GDP in the second quarter of the election year, and a dummy variable based on whether the president’s party has held the White House for only one term or for more than one term. However, the model presented here differs in two crucial respects from the traditional time for change model. First, the model attempts to predict the electoral vote, not the popular vote. The reasons for this change should be obvious: the winner of the election is determined by the electoral vote and in two of the past five elections, 2000 and 2016, the winner of the popular vote lost the electoral vote. The second difference between this model and the earlier time for change model is that this one is based only on the 11 presidential elections since World War II in which an incumbent was running. That is because an examination of the data on all 18 presidential elections since World War II indicates that elections with a running incumbent are different — their outcomes are much more predictable based on the incumbent’s approval rating in the middle of the election year and the growth rate of the economy during the second quarter of the year. Thus, the correlations between real GDP growth and incumbent electoral vote is .48 for the seven open seat elections vs. .73 for the 11 incumbent elections. Likewise, the correlation between late June presidential approval and incumbent electoral vote is .56 for the seven open seat elections vs. .82 for the 11 incumbent elections.

Even a negative approval rating for Trump would not significantly impact his Electoral College count:

The results in Table 3 show a wide range of potential outcomes, from near certain defeat for the incumbent if the economy stalls out and his approval rating falls far below the neutral point, as it has from time to time, to near certain victory if the economy grows faster than expected and his approval rating rises to the neutral point, where it has essentially never been in his first two-plus years in office.

Note that even if Trump’s approval is 10 points underwater, he wins with just decent economic growth.

The GDP grew by 3% in 2018 with little sign of tapering off. If growth is that good in 2020, Trump’s approval should be about where it is now — which according to the model, would give him a decent shot at winning.

The most plausible prediction at this point, however, is for a very close contest. Given a net approval rating of -10, approximately where Trump’s approval rating has been stuck for most of the past year, and real GDP growth of between 1% to 2%, in line with most recent economic forecasts, the model predicts that he would receive between 263 and 283 electoral votes. Of course, it takes 270 electoral votes to win.

Despite the relentless and hysterical campaign against the president, he finds himself with a very realistic chance to win on Election Day 2020.