Exercise

# Let's Forecast Interest Rates

You will now use the forecasting techniques you learned in the last exercise and apply it to real data rather than simulated data. You will revisit a dataset from the first chapter: the annual data of 10-year interest rates going back 56 years, which is in a Series called `interest_rate_data`

. Being able to forecast interest rates is of enormous importance, not only for bond investors but also for individuals like new homeowners who must decide between fixed and floating rate mortgages.

You saw in the first chapter that there is some mean reversion in interest rates over long horizons. In other words, when interest rates are high, they tend to drop and when they are low, they tend to rise over time. Currently they are below long-term rates, so they are expected to rise, but an AR model attempts to quantify how much they are expected to rise.

Instructions

**100 XP**

- Import the class
`ARMA`

in the module`statsmodels.tsa.arima_model`

. - Create an instance of the
`ARMA`

class called`mod`

using the annual interest rate data and choosing the`order`

for an AR(1) model. - Fit the model
`mod`

using the method`.fit()`

and save it in a results object called`res`

. - Plot the in-sample and out-of-sample forecasts of the data using the
`.plot_predict()`

method.- Pass the arguments
`start=0`

to start the in-sample forecast from the beginning, and choose`end`

to be '2022' to forecast several years in the future. - Note that the
`end`

argument 2022 must be in quotes here since it represents a date and not an integer position.

- Pass the arguments