Proving conditional expectation

[Usagi]

Hi guys, assume we have an equality involving 2 random variables U and X such that E(U|X) = E(U)=0, now I was told that this assumption implies that E(U^2|X) = E(U^2). However I'm not sure on how to prove this, if anyone could show me that'd be great!

 

[CaptainBlack]

Hi guys, assume we have an equality involving 2 random variables U and X such that E(U|X) = E(U)=0, now I was told that this assumption implies that E(U^2|X) = E(U^2). However I'm not sure on how to prove this, if anyone could show me that'd be great!

Not sure this is true. Suppose \(U|(X=x) \sim N(0,x^2)\), and \(X\) has whatever distribution we like.

Then \(E(U|X=x)=0\) and \( \displaystyle E(U)=\int \int u f_{U|X=x}(u) f_X(x)\;dudx =\int E(U|X=x) f_X(x) \; dx=0\).

Now \(E(U^2|X=x)={\text{Var}}(U|X=x)=x^2\). While \( \displaystyle E(U^2)=\int E(U^2|X=x) f_X(x) \; dx= \int x^2 f_X(x) \; dx\).

Or have I misunderstood something?

CB

 

[Usagi]

Hi CB,

Actually the problem arose from the following passage regarding the homoskedasticity assumption for simple linear regression:

http://img444.imageshack.us/img444/6892/asdfsdfc.jpg
I do not understand how they came to the conclusion that \sigma^2 = E(u^2|x) \implies \sigma^2 = E(u^2)

Thanks for your help!

 

[CaptainBlack]

Hi CB,

Actually the problem arose from the following passage regarding the homoskedasticity assumption for simple linear regression:

http://img444.imageshack.us/img444/6892/asdfsdfc.jpg
I do not understand how they came to the conclusion that \sigma^2 = E(u^2|x) \implies \sigma^2 = E(u^2)

Thanks for your help!

It is the assumed homoskedasticity (that is what it means).

CB