Exercise

# 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.

Instructions

**100 XP**

- 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?