Exercise

# Estimate the simple moving average model

Now that you've simulated some MA models and calculated the ACF from these models, your next step is to fit the simple moving average (MA) model to some data using the `arima()`

command. For a given time series `x`

we can fit the simple moving average (MA) model using `arima(..., order = c(0, 0, 1))`

. Note for reference that an MA model is an **ARIMA(0, 0, 1)** model.

In this exercise, you'll practice using a preloaded time series (`x`

, shown in the plot on the right) as well as the `Nile`

dataset used in earlier chapters.

Instructions

**100 XP**

- Use
`arima()`

to fit the MA model to the series`x`

. - What are the slope (
`ma1`

), mean (`intercept`

), and innovation variance (`sigma^2`

) estimates produced by your`arima()`

output? Paste these into your workspace. - Use a similar call to
`arima()`

to fit the MA model to the`Nile`

data. Save the results as`MA`

and use`print()`

to display the output. - Finally, use the pre-written commands to plot the
`Nile`

data and your fitted MA values.