PeakTrader.com

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http://mjperry.blogspot.com/2010/10/gain-of-863000-private-jobs-this-year.html

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PeakTrader said...

Looks like the Fed will provide another round of quantitative easing to boost the stock market and put some lipstick on this pig before the election.

Less than 100,000 private payroll jobs per month this year is less than the 125,000 to 150,000 jobs per month needed just to keep up with population growth, and there's still 15 million people out of work.

There's not much the Obama Administration can do now after squandering trillions of dollars failing over and over to generate a virtuous cycle of consumption-employment. So, it has to rely on creating exports, and destroying imports, and press the Fed to print more money.

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juandos said...

Ahhh, still quoting from the ever questionable (fraudulent?)) BLS, eh?

Thank you ZeroHedge:

http://www.zerohedge.com/article/charting-statistical-fraud-bls-22-out-23-consecutive-upward-revisions-initial-jobless-claims

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PeakTrader said...

Juandos, your link "Charting Statistical Fraud At The BLS: 22 Out Of 23 Consecutive Upward Revisions In Initial Jobless Claims" should say "BLS is over 99% Accurate Estimating Initial Jobless Claims."

Your link says "the BLS has underrepresented initial claims by roughly 80,000 year to date." That's less than 1%, since there have been over 450,000 claims each week.

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juandos said...

"That's less than 1%, since there have been over 450,000 claims each week">..

O.K. PT, and your point? (making like the devil's advocate here)

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PeakTrader said...

Juandos, the site could've used a moving average to fool people into believing there's a (very small) bias.

Or, perhaps, the BLS collects up to 99% of the data initially, makes an estimate, and then collects the remaining data for the revision.

I doubt the BLS collects more than 100% of the data (like Chicago adding up votes).

When you collect up to 99% of the data, you may be more likely to underestimate, and when you collect more than 100% of the data (like Chicago votes), you may be more likely to overestimate.