Explanation:

The Schwarz information criterion (SIC) provides the highest penalty factor based on degrees of freedom among the options.

Key points:

MSE (Mean Squared Error) does not penalize the regression model based on the increased number of parameters (k).
Unbiased MSE uses a penalty factor of T/(T-k) where T is sample size and k is number of parameters.
Akaike information criterion (AIC) uses a penalty factor of e^(2k/T).
Schwarz information criterion (SIC) uses a penalty factor of T^(k/T).

Among these penalty factors, SIC has the highest penalty for additional parameters, making it the most conservative criterion that helps prevent overfitting and data mining by strongly penalizing model complexity.

Joel Matip, FRM, is running a regression model to forecast in-sample data. He's worried about data mining and over-fitting the data. The criterion that provides the highest penalty factor based on degrees of freedom is the:

Other

Community

NNikitesh

Schwarz information criterion

54.5%

Akaike information criterion

9.1%

Unbiased mean squared error

18.2%

Mean squared error

18.2%

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Joel Matip, FRM, is running a regression model to forecast in-sample data. He's worried about data mining and over-fitting the data. The criterion that provides the highest penalty factor based on degrees of freedom is the:

Comments (0)