Exercise

# The autocorrelation function

Autocorrelations can be estimated at many lags to better assess how a time series relates to its past. We are typically most interested in how a series relates to its most recent past.

The `acf(..., lag.max = ..., plot = FALSE)`

function will estimate all autocorrelations from 0, 1, 2,..., up to the value specified by the argument `lag.max`

. In the previous exercise, you focused on the lag-1 autocorrelation by setting the `lag.max`

argument to `1`

.

In this exercise, you'll explore some further applications of the `acf()`

command. Once again, the time series `x`

has been preloaded for you and is shown in the plot on the right.

Instructions

**100 XP**

- Use
`acf()`

to view the autocorrelations of series`x`

from 0 to 10. Set the`lag.max`

argument to`10`

and keep the`plot`

argument as`FALSE`

. - Copy and paste the autocorrelation estimate (ACF) at lag-10.
- Copy and paste the autocorrelation estimate (ACF) at lag-5.