ABSTRACT
This chapter will again consider the linear regression model y = Xβ + ε. It will be assumed that the full ideal conditions hold except that the regressor matrix X is random (at least in part) rather than nonstochastic. In particular, it is still assumed that ε ~ N(0,σ2I), that https://www.w3.org/1998/Math/MathML">Q̲=plimX′XThttps://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003066958/a1a5d68a-642a-4424-8c7d-f14e7a129864/content/math268.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> is finite and nonsingular, and that the regressors (columns of X) are linearly independent with probability one; we relax only the assumption that X is nonstochastic.
