Exercise

# Seasonal Adjustment During Tax Season

Many time series exhibit strong seasonal behavior. The procedure for removing the seasonal component of a time series is called seasonal adjustment. For example, most economic data published by the government is seasonally adjusted.

You saw earlier that by taking first differences of a random walk, you get a stationary white noise process. For seasonal adjustments, instead of taking first differences, you will take differences with a lag corresponding to the periodicity.

Look again at the ACF of H&R Block's quarterly earnings, pre-loaded in the DataFrame `HRB`

, and there is a clear seasonal component. The autocorrelation is high for lags 4,8,12,16,… because of the spike in earnings every four quarters during tax season. Apply a seasonal adjustment by taking the fourth difference (four represents the periodicity of the series). Then compute the autocorrelation of the transformed series.

Instructions

**100 XP**

- Create a new DataFrame of seasonally adjusted earnings by taking the lag-4 difference of quarterly earnings using the
`.diff()`

method. - Examine the first 10 rows of the seasonally adjusted DataFrame and notice that the first four rows are
`NaN`

. - Drop the
`NaN`

rows using the`.dropna()`

method. - Plot the autocorrelation function of the seasonally adjusted DataFrame.