3. Let X = (X1,..., Xm)', m > 2, be random vector with covariance matrix s-[] O 12 Σ O21 222 The multivariate correlation coefficient between X1 and X2, .., Xm, denoted R1.2.m; the maximum correlation between X1 and any linear function W2X2+ (i) Show that + Wm Xm. ... 1/2 (ii) Suppose that random vector X has multivariate normal distribution. Show that R1.2,.m is equal to the correlation between X1 and E[X1|X2, ..., Xm].
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- Which of the following processes (Xt)t is weakly stationary? A: Xt = 1:6 + Xt 1 + V tB: Xt = 0:6 Xt-1 +V tC: Xt = 0:8 Xt-1 + V tD: Xt = 0:8 t + 0:6 V t – 1 The term (t) is always assumed to be white noise with variance oneLet X and Y be random variables, and a and b be constants. a) Prove that Cov(aX, bY) = ab Cov(X,Y). b) Prove that if a > 0 and b > 0, then ρaX,bY = ρX,Y. Conclude that the correlation coefficient is unaffected by changes in units.Let X1,X2,X3 be random variables such that E(Xi) = μ and Var(Xi) = σ2 for all i. Find the value of the following quantities (as a function of μ and σ2) if the Xis are all mutually independent. Then repeat these calculations if each pair of variables have a correlation of ρ, that is, corr(Xi, Xj ) =ρ for i ≠ j. (a) E(X1 −X2 +X3) (b) Var(X1 − X2 + X3) (c) E(X21 − X22 + X23)
- Let X and Y have the following joint distribution:X \Y 0 1 0 0.40 0.10 1 0.10 0.10 2 0.10 0.20 (a) Find Cov(4 + 2X , 3 − 2Y ). (b) Let Z = 3X − 2Y + 2. Find E[Z] and σ2z (c) Calculate the correlation coefficient between X and Y. What does this suggest about the relationship between X and Y? (d) Show that for two nonzero constants a and b, Cov(X + a, Y + b) = Cov(X , Y ).The joint probability mass function p(x,y) of X and Y is defined as follows: p(0,0)=0.5 p(1,0)=0.2 p(0,1)=0.2 p(1,1)=0.1 a) What is the covariance between X and Y? b) What is the correlation coefficient of X and Y? c) Are X and Y are dependent? What about are X and Y are uncorrelated?2. Suppose that Yt follows the stationary AR (1) model Yt=2.5+0.7Yt−1+ϵt, where ϵt is i.i.d. with E(ϵt)=0 and Var(ϵt)=9. a) Compute the mean and variance of Yt b) Compute the first two autocovariances of Yt c) Compute the first two autocorrelations of Yt d) Suppose that YT=102.3. Compute Yt+1|t=E(Yt+1|Yt,Yt-1...)
- f X1,X2,...,Xn constitute a random sample of size n from a geometric population, show that Y = X1 + X2 + ···+ Xn is a sufficient estimator of the parameter θ.Let X, Y, and Z be random variables, and let Cov(⋅,⋅) denote the covariance operator as usual. Suppose that the variance of X is 0.7, Cov(X,Y) = 0.4, Cov(X,Z) = 1.2, and Cov(Y,Z) = 0.8. Find each of the following to two decimal places. (a) Cov(3Y, 3X) (b) Cov(3Y + 3, 3X + 8Z)2. Suppose that Yt follows the stationary AR (1) model Yt=2.5+0.7Yt−1+ϵt, where ϵt is i.i.d. with E(ϵt)=0 and Var(ϵt)=9. a) Compute the mean and variance of Yt b) Compute the first two autocovariances of Yt c) Compute the first two autocorrelations of Yt d) Suppose that YT=102.3. Compute Yt+1|t=E(Yt+1|Yt, Yt-1...). Kindly note, sub-parts (a), (b) & (c) are solved alread.
- Suppose X has a trivariate normal distribution with mean vector 0 and covariance matrix 1 0.5 0.25 0.5 1 0 0.25 0 1 A. Find the joint distribution of W1 = X1+X2+X3 and W2 = X1 - X3 B. Find the joint distribution of (X1, X2) GIVEN X3=X3 C. P(max(X1,X2)<X3) D. P(X1>X2|X3=1)Let X and Y be random variables, and a and b be constants. ???? a) Show that Cov [aX,bY] = abCov [X,Y] . b) Show that if a > 0 and b > 0, then the correlation coefficient between aX and bY is the same as the correlation coefficient between X and Y . c) Is the correlation coefficient between X and Y unaffected by changes in the units of X and Y ?Suppose you are interested in the causal effect of Xi on Yi and Yi = α + βXi + ϵi. Moreover, cov(Xi , ϵi) = 0. However, you do not observe Yi . Instead, you only observe a proxy for Yi . Denote this proxy Y'i and assume it is related to Yi as follows: Y'i = Yi + µi where cov(µi , ϵi) = cov(µi , Xi) = 0. Suppose you regress Y'i on Xi . Would the resulting coefficient β' provide an unbiased estimate of β?