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Estimation

In the last exercise, the ACF and PACF were a little inconclusive. The results suggest your data could be an ARMA(p,q) model or could be an imperfect AR(3) model. In this exercise you will search over models over some model orders to find the best one according to AIC.

The time series savings has been loaded and the ARIMA class has been imported into your environment.

Diese Übung ist Teil des Kurses

ARIMA Models in Python

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Anleitung zur Übung

  • Loop over values of p from 0 to 3 and values of q from 0 to 3.
  • Inside the loop, create an ARMA(p,q) model.
  • Then fit the model to the time series savings.
  • At the end of each loop print the values of p and q and the AIC and BIC.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Loop over p values from 0-3
for p in ____:
  
  # Loop over q values from 0-3
    for q in ____:
      try:
        # Create and fit ARMA(p,q) model
        model = ____(____, order=____)
        results = ____
        
        # Print p, q, AIC, BIC
        print(____)
        
      except:
        print(p, q, None, None)
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