Assume the shock (aka, innovation), ε(t), in a time series is approximated by Gaussian white noise. The lagged (yesterday's) realization was 0.0160 and the lagged shock was - 0.280;i.e., y(t-1) = 0.0160 and ε(t-1) = -0.280. Today's shock, ε(t), is 0.190. If the weight parameter theta, θ, is equal to 0.60, which is nearest to the today's realization, y(t), under a first-order moving average, MA(1), process?
Assume the shock (aka, innovation), ε(t), in a time series is approximated by Gaussian white noise. The lagged (yesterday's) realization was 0.0160 and the lagged shock was - 0.280;i.e., y(t-1) = 0.0160 and ε(t-1) = -0.280. Today's shock, ε(t), is 0.190. If the weight parameter theta, θ, is equal to 0.60, which is nearest to the today's realization, y(t), under a first-order moving average, MA(1), process? A.-0.0027; B.0.0018; C.0.0220; D.0.1140