‘Residual Seasonality,’ Fixing the Data or Fixing the Results?

Adjusting the data? How much adjustment is good?  How do you know when the adjustments introduce distortions?   When are you fixing the data? And when are you fixing the results?  To know the answers to those question take a lot of experience and objectivity.

In an article in The Wall Street Journal ( July 1, 2016), by Jo Craven McGinty, titled, “Seasonal Fluctuations Vex Statisticians in Quest to Capture Economy’s Growth, Stripping out normal variation challenges government as it seeks to give accurate GDP figure,” Ms. McGinty addresses a current example of how data is handled.

” “We expect a certain amount of randomness in any economic data,” said Brent Moulton, who oversees GDP and other national economic statistics for the Bureau of Economic Analysis.”

“When adjusted numbers continue to exhibit the influences of seasonal effects, statisticians refer to it as residual seasonality.” That is something new.

” “Small patterns of seasonality at the individual granular level, which don’t appear to be that significant, can add up over time, over quarter and over various components to substantial residual seasonality,” said Mr. Rudebusch, who found that adjusting the GDP a second time seems to erase the effect.”

As I said, are we fixing the data, or are we fixing the results?

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