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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, 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.