Overfit the model using both an ARIMA(p+1,d,q) and ARIMA(p,d,q+1) option. For each model, identify the estimated parameter values and standard errors to complete the table below. Circle or highlight any which are NOT significant. Based on the results, which model seems most appropriate?

Call: arima(x = cow.ts, order = c(1, 0, 1), method = "ML")

Coefficients: ar1 ma1 intercept 0.6603 0.6081 58.9152 s.e. 0.0624 0.0683 1.5984

sigma^2 estimated as 20.48: log likelihood = -513.36, aic = 1032.73

Call: arima(x = cow.ts, order = c(2, 0, 1), method = "ML")

Coefficients: ar1 ar2 ma1 intercept 0.6794 -0.0198 0.5934 58.9143 s.e. 0.1420 0.1317 0.1217 1.5810

sigma^2 estimated as 20.48: log likelihood = -513.35, aic = 1034.7

Call: arima(x = cow.ts, order = c(1, 0, 2), method = "ML")

Coefficients: ar1 ma1 ma2 intercept 0.6503 0.6221 0.0168 58.9137 s.e. 0.0944 0.1193 0.1163 1.5828

sigma^2 estimated as 20.48: log likelihood = -513.35, aic = 1034.71