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.

Questo esercizio fa parte del corso

Time Series Analysis in Python

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Istruzioni dell'esercizio

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).

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import the ARIMA module from statsmodels
from statsmodels.tsa.arima.model import ARIMA

# Fit an AR(1) model to the first simulated data
mod = ARIMA(___, order=___)
res = mod.___

# Print out summary information on the fit
print(res.___)

# Print out the estimate for phi
print("When the true phi=0.9, the estimate of phi is:")
print(res.___)

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