Conditional Mean: Does the Equation Hold in General?

In summary, the equation E(X|Y<y)=Cov(X,Y)*E(Y|Y<y)/Var(Y) holds under certain conditions such as X=aY+Z with Z and Y independent and a non-zero, or in the case of a bivariate normal with non-zero correlation. However, it does not hold in general, as it can be false if the variables are independent or if the correlation between Y and Z is not zero and/or the expected value of Z is not zero.
  • #1
EconMax
2
0
In a paper, I have found this relationship:

E(X|Y<y)=Cov(X,Y)*E(Y|Y<y)/Var(Y)

It seems to me that the previous equation holds if, for instance, X=aY+Z with Z and Y independent and a non zero.

It also holds if (X,Y) is a bivariate normal (with non zero correlation).

But does it hold in general?

I think the answer is no. Because, trivially, if X and Y are independent, the equation is wrong.

Am I correct?
 
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  • #2
It doesn't have to hold if the variables are independent: take X, Y as independent, so that [tex] cov(X,Y) = 0 [/tex], with [tex] E(X) = a \ne 0[/tex]. Then the left side is non-zero, the right side is zero.
 
  • #3
True. In the case that X=aY+Z, it does not hold also if Corr(Y,Z) is different from zero and/or E(Z) is different from zero (I forgot to mention this condition before).
 

1. What is conditional mean?

Conditional mean is the average value of a random variable, given that another variable has a specific value or falls within a specific range. It represents the expected value of the first variable, taking into account the constraints of the second variable.

2. How is conditional mean calculated?

The conditional mean is calculated by dividing the sum of the products of the first variable and its corresponding probabilities, with the sum of the probabilities. This can be represented mathematically as E(X|Y) = ΣxP(X=x|Y).

3. Does the equation for conditional mean hold in general?

Yes, the equation for conditional mean holds in general as long as the variables are independent or have a known relationship. However, it may not hold if there is a strong correlation between the variables or if there is a nonlinear relationship.

4. What is the significance of conditional mean in statistics?

Conditional mean is an important concept in statistics as it allows us to make predictions and draw conclusions about a random variable, taking into account the influence of another variable. It is also used in various statistical models and methods, such as regression analysis and ANOVA.

5. Can conditional mean be used to estimate the value of a variable?

Yes, conditional mean can be used to estimate the value of a variable, given the value or range of another variable. This is particularly useful in situations where the value of one variable affects the value of another, such as in economic forecasting or market analysis.

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