Model choice - I

Based on the sample P/ACF pair of the logged and differenced varve data (dl_varve), an MA(1) was indicated. The best approach to fitting ARMA is to start with a low order model, and then try to add a parameter at a time to see if the results change.

In this exercise, you will fit various models to the dl_varve data and note the AIC and BIC for each model. In the next exercise, you will use these AICs and BICs to choose a model. Remember that you want to retain the model with the smallest AIC and/or BIC value.

A note before you start:

sarima(x, p = 0, d = 0, q = 1) and sarima(x, 0, 0, 1)

are the same.

The package astsa is preloaded. The varve series has been logged and differenced as dl_varve <- diff(log(varve)).
Use sarima() to fit an MA(1) to dl_varve. Take a close look at the output of your sarima() command to see the AIC and BIC for this model.
Repeat the previous exercise, but add an MA parameter by fitting an MA(2) model. Based on AIC and BIC, is this an improvement over the previous model?
Instead of adding an MA parameter, add an AR parameter to the original MA(1) fit. That is, fit an ARMA(1,1) to dl_varve. Based on AIC and BIC, is this an improvement over the previous models?

Take Hint (-30 XP)

Exercise

Model choice - I

Instructions