Archive for the ‘Forecasting’ Category

The Challenger disaster: A tragic lesson in data analysis …

June 6, 2016

Well-intended engineers correctly interpreted the wrong data.

Excerpted from Everydata: The Misinformation Hidden in the Little Data You Consume Every Day

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I’m sure all baby-boomers have a vivid recollection, but for younger readers, here’s some background …

“On the morning of 28 January 1986, the Space Shuttle Challenger, mission 51– L, rose into the cold blue sky over the Cape. To exuberant spectators and breathless flight controllers, the launch appeared normal. Within 73 seconds after liftoff, however, the external tank ruptured, its liquid fuel exploded, and Challenger broke apart.”

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What happened?

“The specific failure,” noted the Report of the Presidential Commission on the Space Shuttle Challenger Accident, “was the destruction of the seals that are intended to prevent hot gases from leaking.…”

Investigators quickly focused their attention on a key part of the seals— the rubber O-rings that went in between two sections of the solid rocket motor— the “tang” and the “clevis.”

The O-rings on the Challenger needed to be flexible enough to compress and expand, sometimes within milliseconds.

But O-ring resiliency “is directly related to its temperature… a warm O-ring will follow the opening of the tang-to-clevis gap. A cold O-ring may not.”

In fact, investigators found that a compressed O-ring is five times more responsive at 75 degrees Fahrenheit than at 30 degrees Fahrenheit.

The air temperature at launch was 36 degrees Fahrenheit.

The commission’s report found “it is probable” that the O-rings were not compressing and expanding as needed.

The resulting gap allowed the gases to escape, destroying the Challenger.

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So why didn’t engineers stop the launch, given the cold temperatures?

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Nums: Why are economists so bad at forecasting?

February 10, 2015

Wash Post had an interesting analysis titled “This graph shows how bad the Fed is at predicting the future

The crux of their argument: the Fed has a clear recent tendency to mis-forecast economic growth … not by a little, by a lot …  forecasting almost twice as rapid growth as is ultimately realized.

For example,  in 2009 the Fed was predicting 4.2 percent growth in 2011.  But then in 2010 it revised that down to 3.85 percent growth. And in 2011 they revised it further to 2.8 percent growth. And when all was said and done, the economy only grew about 2.4 percent that year. The Fed projected growth almost twice as fast as what actually happened.

 

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What’s going on?

 

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Blizzard miss: Meteorologists apologize … some blame climate change.

January 28, 2015

I often ask: ”Do weather forecasters feel guilty accepting their pay?”

Most people would say: “They certainly should”.

After clearing the streets of NYC for “an unprecedented blizzard of epic proportions”, weather forecasters had to eat crow when the snow-that-would-end-the world turned out to be, well, a garden-variety winter snow storm.

At least one weather-dude had the decency to apologize.

According to CNBC:

“Gary Szatkowski, the meteorologist in charge of the National Weather Service’s office in New Jersey, stunned people in the wee hours Tuesday with a heartfelt apology for the blown forecast.”

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How did the forecasters get things so wrong?

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Nums: Why are economists so bad at forecasting?

October 7, 2014

Wash Post had an interesting analysis titled “This graph shows how bad the Fed is at predicting the future

The crux of their argument: the Fed has a clear recent tendency to mis-forecast economic growth … not by a little, by a lot …  forecasting almost twice as rapid growth as is ultimately realized.

For example,  in 2009 the Fed was predicting 4.2 percent growth in 2011.  But then in 2010 it revised that down to 3.85 percent growth. And in 2011 they revised it further to 2.8 percent growth. And when all was said and done, the economy only grew about 2.4 percent that year. The Fed projected growth almost twice as fast as what actually happened.

 

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What’s going on?

 

(more…)

More: Why Fed economic forecasts are bad ….

June 28, 2013

Earlier this week we posted Nums: Why’s the Fed so bad at forecasting?

We cited Nate Silver’s thesis that economists’ forecasts are generally poor for 4 main reasons:

  1. Complexity makes it hard to to pin down cause & effect.
  2. The economy is dynamic, especially subject to policy jolts
  3. Economic data is imprecise and subject to large revisions
  4. Forecasts often reflect political bias … pro and con.

On cue, the Feds released released their revision to Q1 GDP …

Based on revised data, the economy grew at a 1.8% annual rate in the first quarter,  well below previous estimate of 2.4% growth.

The biggest change was a cut in the government’s estimate of consumer spending which is more than 70% of the economy.

Consumer spending growth dropped to 2.6% from 3.4% growth.

 

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Source: USA Today

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The revision — .6% – may initially sound like loose change, but it’s a 25% miss.

So, economic models that operating on the original (higher) estimate have a starting point that is off by 25%.

The error compounds over time.

It’s a version of what theorists call chaos theory … how a seemingly small variation at a starting point can compound into a major effect over time.

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Side note: And, in the “new normal” economy, the downward revision was good for the stock market since it puts pressure on the Fed to continue pumping money into the economy … the bulk of which is flowing straight to the stock market.

Go figure.

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Fair Econometric Model predicts election outcome …

November 1, 2012

It’s called the Fair Model – not because it’s unusually unbiased (though I assume it is unbiased – but because its creator id Prof Ray Fair … currently at Yale, previously at Princeton.

click for Prof. Fair’s original article
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We’re reporting the results for 2 reasons.

First, because I was one of Prof. Fairs research assistants at Princeton … he was the person who introduced me to econometrics … and, for that I’ll be forever grateful.

Second, because Prof. Fair’s modeling has essentially reduced presidential election outcomes down to 3 economic variables:

  • The per capita growth rate of gross domestic product in the three quarters before the elections. For the first three quarters of this year, GDP per capita grew at a 1.01% annual rate.
  • Inflation over the course of the entire presidential term, as measured by the GDP price index. The annual rate of inflation by this measure was 1.58%.
  • The number of quarters during the presidential term that GDP per capita growth exceeded 3.2%. There has been only one such “good news” quarter — the fourth quarter of last year, when GDP per capita grew 3.3%.

According to the WSJ, plug those figures into Prof. Fair’s econometric model and Romney edges Obama 51 to 49.

Said differently, “if Romney doesn’t win, it will have been despite an economy that Mr. Fair’s model suggests should have been in his favor.”

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