Use Different Criteria for Model Selection
Select a time series model for data based on different selection criteria, such as Akaike information criterion (AIC), finite sample corrected AIC, Bayesian information criterion (BIC), or Schwarz–Bayes information criterion (SBC).
In[1]:= | ![]() X |
In[2]:= | ![]() X |
Out[2]= | ![]() |
In[3]:= | ![]() X |
Out[3]= | ![]() |
Display the table of the ranked candidate models for each criterion.
In[4]:= | ![]() X |
Out[4]= | ![]() |
Select the top three entries from each table and refine them using the maximum likelihood estimation. Models estimated with different methods may have different rankings.
In[5]:= | ![]() X |
In[7]:= | ![]() X |
Out[7]= | ![]() |
Define ranking function for each criterion.
In[8]:= | ![]() X |
Select the best maximum likelihood estimates according to different criteria.
Out[9]//TableForm= | |
![]() |