The United States will plunge into a recession in 2020 as the Federal Reserve raises interest rates to cool off an overheating economy, a poll of respected economists warns.
The majority of forecasters surveyed by The Wall Street Journal predict the current expansion, which began in mid-2009 and ranks as the second-longest in American history, will end only after setting a record for longevity
About 60% of economists think the U.S. economic expansion will end in 2020 with 22% predicting 2021, the Journal reported.
“The current economic expansion is getting long in the tooth by historical standards, and more late-cycle signs are emerging,” said Scott Anderson, chief economist at Bank of the West, who was among those predicting a 2020 recession.
Recessions “are notoriously difficult to predict, and sometimes are tricky to recognize even after they start,” WSJ.com explained. “The recession that began in December 2007 wasn’t officially proclaimed by the National Bureau of Economic Research’s recession-dating committee until a year had gone by. Forecasters saw the chances of a recession rise back in 2011 and in 2016; both turned out to be false alarms,” the Journal reported.
“Recessions occur because of unforeseen shocks, so by definition there is no meaningful answer,” said Deloitte economist Daniel Bachman, who declined to estimate either the timing or cause of the next downturn.
Meanwhile, predicting economic downturns is so hard that most economists don’t bother, Bloomberg explains.
Might as well try algorithmic methods, argues Bloomberg Opinion columnist Noah Smith.
“Whatever the reason, the field of macroeconomic forecasting is now exclusively the domain of central bankers, government workers and private-sector economists and consultants. But academics should try to get back in the game, because a powerful new tool is available that might be a game-changer. That tool is machine learning,” Smith wrote.
“Loosely speaking, machine learning refers to a collection of algorithmic methods that focus on predicting things as accurately as possible instead of modeling them precisely. Instead of traditional statistics, where statisticians build models of how the world works, machine learning algorithms are generally given the freedom to figure out the model on their own,” Smith wrote.
“The downside is that it’s often hard to tell exactly why the algorithms make the predictions they make, but the upside is that they are generally more accurate than traditional methods.”
(Newsmax wire services contributed to this report).
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