If the forecasts are always wrong, doesn't that increase the odds that the next might be right?

Related to the prior post, from Anyone Notice That The Trump Recovery Is Doing Much Better Than Expected? from the Editorial Board of Issues & Insights.  

A news report out on Monday said that 83% of companies in the S&P 500 beat expectations for earnings in the second quarter of the year, the first time that’s happened in more than a decade.

That’s been a common refrain over the past several months, as the economic recovery from the COVID-19 shutdowns has repeatedly outperformed what the “experts” expected. Here’s a sampling of headlines:

“US economy added 1.8m jobs in July, beating expectations”

“Jobs Numbers in July Beat Expectations for Third Straight Month”

“Corporate Earnings Beat Analysts’ Lowered Expectations”

“US consumer sentiment hit a 6-month high in September, beating economist forecasts”

“U.S. new home sales beat expectations in July”

In some cases, the difference between what economists were predicting at the start of the pandemic and what’s actually occurred is stark.

Take the forecasts for unemployment.

In March, economists at the Federal Reserve Bank of St. Louis projected the unemployment rate would top 32%.

That same month, Goldman Sachs said the unemployment rate will peak at around 15% later in the year.

A May survey of economists by FiveThirtyEight.com found that the median forecast for the May unemployment rate was 20%.

Even White House economic adviser Kevin Hassett predicted April’s unemployment rate would be 16-17%.

What actually happened?

The unemployment rate peaked in April at 14.7%, then dropped to 13.3% in May.

The experts were just as wrong about the speed of the jobs recovery.

They then provide numerous other examples where the expert forecasts have been  badly off and always have underestimated the recovery by pretty material margins.  Another:

Reuters joined in with a story titled: “U.S. weekly jobless claims unexpectedly rise; labor market recovery stalling”

Bloomberg warned that “U.S. Economic Recovery Is Stalling and It May Get Even Worse.”

Yet shortly after all those dire predictions, the Atlanta Fed’s GDPNow estimate for the third quarter steadily rose from just over 10% to more than 30%.

In other words, as the actual economic data started coming in for Q3, they didn’t show an economy stalling, but one doing better than initially expected.

With less than 10 days to go, the current GDPNow estimate for Q3 is an eye-popping 32%.

Yet, we continue to see headlines warning about a stalling economy.

They end with basically the same question I had in my earlier post.

So, the question we have is this: Why do mainstream economists and the press keep getting it wrong? Why do they keep making new dire predictions after their previous ones proved false?

Is it the result of flawed Keynesian-style economic models? Mainstream economists, remember, are using the same economic models that predicted a robust recovery from the Great Recession under Barack Obama, only to find those forecasts hopelessly optimistic.

Is it the result of an anti-Trump bias? After all, any good news about the economy is bad news for liberals in the economic profession and the press who are hoping to run President Donald Trump out office.

Whatever the reason, it sure isn’t based on the facts.

Bias, epistemic closure, and motivated reasoning, all driven by economic and power desperation are probably all drivers.  

But there is another observable pattern.  2016 confident forecasts of a compelling Clinton victory were dramatically wrong.  UN AGW advocate forecasts for extreme weather have been dramatically wrong.  Forecasts for Covid-19 have been dramatically wrong.  Stock market performance forecasts by Nobel Economic Prize winners were dramatically wrong.  Most sociological and psychological research papers are dramatically wrong.  Most novel public policy ends up being dramatically wrong.  

We are in an uncertain environment of loosely coupled, multi-causal, chaotic, fragile systems subject to internal failure and external shocks.  We are pretty consistently bad at forecasting at a macro-system level.  And yet we continue to repose at least some declining faith in the forecasts of "experts" despite their repeated and deepening history of failure.