Hi all,
Given a model:
Y = a + x(b) + z(d)+e
Then, one takes the residuals e from this regression and regress it on
a new set of explanatory variables, that is:
e+mean(Y) = a1 + k(t)+v
(note mean(Y) only affects the intercept a1)
Any idea why this method is favored over:
Y = a +x(b) +z(d) + k(t) + e? (which essentially is a one stage
regression instead of the latter 2 stage)
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