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  3. Time Series Analysis in Python

Exercise

Estimating an AR Model

You will estimate the AR(1) parameter, \(\small \phi\), of one of the simulated series that you generated in the earlier exercise. Since the parameters are known for a simulated series, it is a good way to understand the estimation routines before applying it to real data.

For simulated_data_1 with a true \(\small \phi\) of 0.9, you will print out the estimate of \(\small \phi\). In addition, you will also print out the entire output that is produced when you fit a time series, so you can get an idea of what other tests and summary statistics are available in statsmodels.

Instructions

100 XP
  • Import the class ARIMA in the module statsmodels.tsa.arima.model.
  • Create an instance of the ARIMA class called mod using the simulated data simulated_data_1 and the order (p,d,q) of the model (in this case, for an AR(1)), is order=(1,0,0).
  • Fit the model mod using the method .fit() and save it in a results object called res.
  • Print out the entire summary of results using the .summary() method.
  • Just print out an estimate of \(\small \phi\) using the .params[1] attribute (no parentheses).