Help solve the following

Max has a lot of data from stochastic process: It = 0.98:1-1 + 6 - 0.let-1 where et ~ NV (0, oz) are Gaussian white noise. He tests for a unit root and mistakenly decides that the stochastic process is integrated of order 1. He then differences the data to obtain y, = It . A) Write out the ARMA model for yt . [2 points] B) If you were to fit an ARMA model to #t , what would the order of the AR and MA components be? [2 points] C) Is It causal? [1 point] D) Is the ARMA model for 1/ stationary? [1 point] E) Is the ARMA model for yt invertible? [2 points] F) Knowing the ARMA model for y = Art . would you expect the ACF for 14 , to cut off abruptly after 2 lags? Explain why. [1 point] G) Knowing the ARMA model for y = Art , would you expect the PACF for It . to cut off abruptly after 1 lag? Explain why. [1 point]6. You have estimated the following ARMA(1,1) model for some time-series data Vt = 0.036 + 0.69y+-1 + 0.426t-1 + Et Suppose that you have data for time to t-1, i.e., you know that y,-1 = 3.4 and -1 = -1.3 (a) Obtain forecasts for the series y for times t, t+1, t+2 using the estimated ARMA model. (b) If the actual values for the series turned out to be -0.032, 0.961, 0.203 for t, t+1, t+2