Let X0 ≡ 0, and consider a stochastic recursion of the
form
Xn+1 =c+ρXn +σεn+1
for all n ≥ 0, where c, σ > 0, |ρ| < 1, and ε1, ε2, ... are iid N(0, 1). Compute the quantities:
a.) lim n→∞ EXn.
b.) lim n→∞ var Xn.




Continuing in this way n times, we get


(X0 = 0)
a)




(By Sum of infinite geometric series)
b)





(By Sum of infinite geometric series)
Let X0 ≡ 0, and consider a stochastic recursion of the form Xn+1 =c+ρXn +σεn+1 for...
Let Z1, Z2, . . . be a sequence of independent standard normal random variables. Define X0 = 0 and Xn+1 = (nXn + (Zn+1))/ (n + 1) , n = 0, 1, 2, . . . . The stochastic process {Xn, n = 0, 1, 2, } is a Markov chain, but with a continuous state space. (a) Find E(Xn) and Var(Xn). (b) Give probability distribution of Xn. (c) Find limn→∞ P(Xn > epsilon) for any epsilon > 0.
4. Let Z1, Z2,... be a sequence of independent standard normal random variables. De- fine Xo 0 and n=0, 1 , 2, . . . . TL: n+1 , The stochastic process Xn,n 0, 1,2,3 is a Markov chain, but with a continuous state space. (a) Find EXn and Var(X). (b) Give probability distribution of Xn (c) Find limn oo P(X, > є) for any e> 0. (d) Simulate two realisations of the Markov process from n = 0 until...
4.(120) Let X1,,,Xn be iid r(, 1) and g(u) given. Let 6n be the MLE of g(4) (1)(60) Find the asymptotic distribution of 6, (2)(60) Find the ARE of T Icc(X) w.r.t. on P(X1> c), c > 0 is i n i1 5.(80) Let X1, ,,Xn be iid with E(X1) = u and Var(X1) limiting distribution of nlog (1 +). o2. Find the where T n(X - 4)/s. - 1 -
4.(120) Let X1,,,Xn be iid r(, 1) and g(u)...
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Let {Xn}n=0 be a process taking values in a countable [0, 1]E and stochastic set E, and assume that for some probability vector X matriz P E(0, 1ExE we have prove that Xn ~ Markov(λ, P)
Observations X1, ..., Xn come from the density function f(x; 6) = c(x) exp{x0 – b(0)}, where 0 is unknown. In the general case, you are given that E X = b'(@) and Var X = 6"(0), where 5'(O) = db/do and V"(O) = d+b/202. 1. Suppose T is another estimator for which ET = V(@); show that E[T X] = [b'(O)]2 +6"(C)/n. Hence, show that Var T > 6"(0)/n.
Let X0,X1,... be a Markov chain whose state space is Z (the
integers).
Recall the Markov property: P(Xn = in | X0 = i0,X1 = i1,...,Xn−1
= in−1) = P(Xn = in | Xn−1 = in−1), ∀n, ∀it. Does the following
always hold: P(Xn ≥0|X0 ≥0,X1 ≥0,...,Xn−1 ≥0)=P(Xn ≥0|Xn−1 ≥0)
?
(Prove if “yes”, provide a counterexample if “no”)
Let Xo,Xi, be a Markov chain whose state space is Z (the integers). Recall the Markov property: P(X,-'n l Xo-io, Xi...
Let X1, ... , Xn be a sample from the probability density function f(x0), where 0 € {0,1}. If 0 = 0, then f(20) = ſi if 0<x<1, 10 otherwise, while if @= 1, then fale) - 27if 0<x< 1, 10 otherwise. Find the MLE of 0.
Stochastic Processes Markov
5 Let Xn, n 0, be the two-state Markov chain. (a) Find Po(To - n). (b) Find Po(T n).
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Suppose Xi, X2, ,Xn is an iid N(μ, c2μ2 sample, where c2 is known. Let μ and μ denote the method of moments and maximum likelihood estimators of μ, respectively. (a) Show that ~ X and μ where ma = n-1 Σηι X? is the second sample (uncentered) moment. (b) Prove that both estimators μ and μ are consistent estimators. (c) Show that v n(μ-μ)-> N(0, σ ) and yM(^-μ)-+ N(0, σ ). Calculate σ and σ . Which estimator...