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st: pca and predict--confusion about what it does

From   Israel Pearce <>
Subject   st: pca and predict--confusion about what it does
Date   Sat, 20 Oct 2012 10:32:37 -0700

I am confused about the principal component scores one gets from pca
postestimation. Let's say I wanted the first principal component
scores from a set of explanatory variables. I could do a pca on my x
variables getting eigen values and vectors then use predict pc1 (a
newly created var), focusing only on the first eigenvector. However,
if one does not have a y variable what is the score actually giving us
for individual observations? What is it a predicted value for? Also,
in theory I do not see why the sum of these observations must add to 0
but they are. If someone understands this I would greatly appreciate
an explanation. Thanks!
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