Consider the ARMA(1,2) process with drift yt = 1 + pyt-1 + ut + 41ut-1+ 42ut_2, ut ~ N(0,oz) white noise a) Compute the autocovariance function of y. [You may take a maxi- mum of 10 points on this question by setting o = 0 if you wish in this part only. Please be clear and indicate if you do this.] b) For what values of the parameters is this process covariance station- ary? c) Explain how to produce a two-step-ahead forecast using this model. d) Consider the poorly-formed model yt = u+ pyt-1 + yout + 414+-1+ 121t-2, ut ~ N(0,02) white noise Suppose 0* = (1*, $*, 43, 41, 12, (02)*) is a MLE for this model (given some hypothetical data). Find another MLE for this model. That is, show that the MLE for this model is not unique. (Hint: Your new MLE values will be in terms of the parameters in 0*.)