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The calibration process may produce erroneous outcomes if the wrong variables are inadvertently adjusted.
According to a 2002 study by Carmen Reinhart, a leading economic historian, the ratings agencies have frequently focused on the wrong variables when rating a country's creditworthiness.
Moreover, the use of forward stepwise regression in this context may be compromised by the part-whole correlation effect (10) since it often results in the wrong variables being selected.
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To put it another way, we target the wrong variable.
Might it be that the unemployment rate is the "wrong" variable?
When we worry about the pace of change, Bell suggests, we're focusing on the wrong variable.
But this would not respect that the representation with a linear model itself increases uncertainty, for one might choose the wrong explanatory variables (influences) or not enough.
This choice is justified by first the power of Hotelling statistic to detect mean shift more than SD and CV shifts and second instead of monitoring each independently, the selection of the fifty important wavelengths to construct Hotelling will make the control of profile more easier and minimize the wrong individual variable detection.
Input the wrong kind of variables.
But what recourse does a company have when, because of faulty forecasting, the assumed values of key variables are wrong and its chosen strategy is inappropriate?
(The reason "uncertainty" complaints drive me a little nuts is because it implies there usually certainty in the movements of economic variables. Wrong. There's always uncertainty -- sure, sometime more than others... but if you can't take that heat get the h-e-double-toothpicks out of the market).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com