Those who follow our analysis on a regular basis already know that we consider the number of seasonally-adjusted initial unemployment insurance claims filed each week to be the most easy-to-accurately-predict number in all of economic datadom.
The reason why is because the weekly data tends to follow a straight-line path over time, with the variation in the data from week-to-week about these linear trends being easily described by a normal, bell-curve kind of statistical distribution.
Periodically, breaks in existing trends occur and new trends begin, which we are able to identify using the kind of statistical analysis that has been successfully and widely applied in manufacturing or industrial applications for over 80 years.
Here, we recognize that the cause of a break in an existing trend in the weekly jobless claims data is actually triggered by events that significantly altered the business outlook for employers some 2-3 weeks earlier in time, which corresponds to the employers implementing their decisions affecting their employee retention plans with their next pay cycle. Since most employed Americans are paid on either a weekly or biweekly basis, that works out to a two-to-three week lag from when an event significantly affecting the pace of layoffs in the U.S. occurs, to when the effects of such a change in the business outlook of employers shows up in the BLS' weekly data reports.
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