Exercise

# Taxing Exercise: Compute the ACF

In the last chapter, you computed autocorrelations with one lag. Often we are interested in seeing the autocorrelation over many lags. The quarterly earnings for H&R Block (ticker symbol HRB) is plotted on the right, and you can see the extreme cyclicality of its earnings. A vast majority of its earnings occurs in the quarter that taxes are due.

You will compute the array of autocorrelations for the H&R Block quarterly earnings that is pre-loaded in the DataFrame `HRB`

. Then, plot the autocorrelation function using the `plot_acf`

module. This plot shows what the autocorrelation function looks like for cyclical earnings data. The ACF at `lag=0`

is always one, of course. In the next exercise, you will learn about the confidence interval for the ACF, but for now, suppress the confidence interval by setting `alpha=1`

.

Instructions

**100 XP**

- Import the
`acf`

module and`plot_acf`

module from statsmodels. - Compute the array of autocorrelations of the quarterly earnings data in DataFrame
`HRB`

. - Plot the autocorrelation function of the quarterly earnings data in
`HRB`

, and pass the argument`alpha=1`

to suppress the confidence interval.