Consider the time series of Xt = Xt−1 + Wt, whereWt are i.i.d and Wt ∼ N (0, σ2 ) and X0 = 0. Let X¯ = 1 n Pn i=1 Xi . Derive the general form for var(X¯). (Hint: Pn i=1 i 2 = n(n+1)(2n+1) 6 )

Consider the time series of Xt = Xt−1 + Wt, whereWt are i.i.d and Wt ∼...
Consider the time series
Consider the time series of Xt Xt-1 + Wt, where Wt ~ N(0, σ2) and Xi. Derive the general form for var(X). (Hint: i=1 5 ? -n(n+1)(2n+1)
(White noise is not necessarily i.i.d.). Suppose that {Wt} and
{Zt} are independent and identically distributed (i.i.d.)
sequences, also independent of each other, with
P(Wt = 0) = P(Wt = 1) = 1/2 and P(Zt = −1) = P(Zt = 1) = 1/2.
Define the time series Xt by Xt =
. Show that {Xt} is white but not i.i.d.
w (1 – W-1) ZŁ
In the simple linear regression with zero-constant item for (xi , yi) where i = 1, 2, · · · , n, Yi = βxi + i where {i} n i=1 are i.i.d. N(0, σ2 ). (a) Derive the normal equation that the LS estimator, βˆ, satisfies. (b) Show that the LS estimator of β is given by βˆ = Pn i=1 P xiYi n i=1 x 2 i . (c) Show that E(βˆ) = β, V ar(βˆ) = σ...
3. Let Xi, , Xn be i.i.d. Lognormal(μ, σ2) (a) Suppose σ-1, prove that S-X(n)/X(i) is an ancillary statistics. (b) Suppose p 0, prove T-X(n) is a sufficient and complete statistics (c) Find a minimal sufficient statistics.
3. Let Xi, , Xn be i.i.d. Lognormal(μ, σ2) (a) Suppose σ-1, prove that S-X(n)/X(i) is an ancillary statistics. (b) Suppose p 0, prove T-X(n) is a sufficient and complete statistics (c) Find a minimal sufficient statistics.
X1, X2, . . . , Xn i.i.d. ∼ N (µ, σ2 ). Assume µ is known; show
that ˆθ = 1 n Pn i=1(Xi− µ) 2 is the MLE for σ 2 and show that it
is unbiased.
Exactly 6.4-2. Xi, X2, . . . , xn i d. N(μ, μ)2 is the MLE for σ2 and show that it is unbiased. r'). Assume μ is known; show that θ- n Ση! (X,-
Let wt for t = . . .,-2,-1, 0, 1, 2, . . . be an independent and identically distributed process with wt ~ M0, σ2). and consider the time series Determine the mean and the autocovariance function of xt and state whether it is stationary
Consider the simple moving average model Xt = 0.02 + Wt − 0.4Wt−1, where Wt is a sequence of i.i.d. normal random variables with mean zero and variance 4. What is the mean of Xt? What is the variance of Xt. Show working
Let Wt de a (Gaussian) white noise with variance σ 2 . Then, let
Xt = WtWt−1 + µ, where µ is a real constant. Determine the mean and
autocovariance of (Xt)? Is this process stationary?
Let W, de a (Gaussian) white noise with variance σ2. Then, let of where μ is a real constant. Determine the mean and (X)? Is this process stationary?
Time series analysis
2. Set n 100 and generate and plot the time series xt 2 cos(2π.06t) + 3 sin(2π.06t) Ý,-4 cos(2n. 10t) + 5 sin(2m10) z, 6 cos(2π·40t) + 7 sin(2π·40t) (a) Use the periodogram function in R to plot the periodogram of Vi. Can you explain the spikes? (b) Now let wi ~ N(0, 25) be iid and plot the periodogram of the series V +w. Does it still pick out the periodic components?
2. Set n 100...
Dr. Beldi Qiang STATWOB Flotllework #1 1. Let X.,No X~ be a i.İ.d sample form Exp(1), and Y-Σ-x. (a) Use CLT to get a large sample distribution of Y (b) For n 100, give an approximation for P(Y> 100) (c) Let X be the sample mean, then approximate P(.IX <1.2) for n 100. x, from CDF F(r)-1-1/z for 1 e li,00) and ,ero 2Consider a random sample Xi.x, 、 otherwise. (a) Find the limiting distribution of Xim the smallest order...