Simple linear regression with One regressor: Inference - Properties of OLS estimator under LS assumptions
6 important questions on Simple linear regression with One regressor: Inference - Properties of OLS estimator under LS assumptions
What is the Law of Iterated Expectation? Src. Bruce Hansen
Define the law of total variance and explain its relevance
var(Y ) = E[var(Y |X)] + var(E[Y |X]).
So in words:
the variance of Y
=
the average of the Variance of Y given X
+
the variance of the average of Y given X
So its basically just that the variance of Y consists of 2 components:
the avg of the variances of Y|X
+
the variance of the average of Y|X
It is relevant because it is used when deriving the formula for the variance of B1-hat
Unsolved Question; on slide 16 it defines the variance as var(b1-hat), why the decision to start with var(b1-hat - b1) ?
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What is asymptotic normality?
Then asymptotic normality = as sample sizes increases, behavior will be normal (thats what we will try to prove)
What 3 theorems will we use to proof asymptotic normality?
2. Central limit theorem
3. Slutsky's theorem
Under the 3 OLS assumptions, in large samples B0-hat and B1-hat are....
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