hello tutors have difficult solving the following questions please help thank you

Question 6. Point allocation: equal weights for each question. Suppose you want to estimate the model Vi = 1,B + u; where {ya, ray, are an i.i.d. sample with finite first- and second-order moments; E(u; |r;) = 0 and the variance of u, is known and equal to one. (a) Briefly describe a Bayesian computational method that would allow you to estimate this model with the constraint 8 > 0. Make sure you detail your choice of prior, likelihood, and posterior simulation method. (b) Suppose that in your answer to part (b) you decide on an accept/reject method based on an importance density given by an exponential density with mean of 1. Explain the advantages/disadvantages of this choice and how the Metropolis and Metropolis-Hastings algorithms will differ. (e) Instead, suppose estimate the unconstrained model. Describe a way to assess the constraint 830.Question 5. Point allocation: equal weights for each question. Consider the within-group estimator of the model yit = ay-1to + Un |a| 0, 7>0 F(y*) = 1 - exp(-vy*), Ely*] =1/7 Varly*] = 1/-2 Ely'ly'> = c+1/y We incorporate regressors by specifying 71 = exp(x,B), where x; and S are k x 1 vectors. Throughout we assume a sample of size N with independence over i. (a) Suppose x'S = a + oz and we obtain estimates a = 0.3 and 6 = 0.4. Give a meaningful interpretation of how the conditional mean of y" changes as z changes. (b) Suppose we fully observe x; but do not fully observe y,. Instead we observe di = 1 if y; 2 2 di = 0 if y; 2, and the associated x; when y; > 2. i.e. there is no record of either x; or y; when y;