A Study of the Effectiveness of Model Selection Criteria for Multiple Regression Model
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Abstract
The study aims to compare the effectiveness of the ten model selection criteria for multiple regression model, namely, AIC, BIC, HQIC, AICc, AICu, HQICc, KIC, KICcC, KICcSB, and KICcHM. The conditions for simulation were differences in sample size, number of parameters in the model, regression coefficient, and error variance. The results of a small sample case showed that if the true model is difficult to identify, the appropriate criteria are AIC, HQIC, AICc, and HQICc. If the true model is easy to identify, the appropriate criteria are AICu and KICc. In a medium sample case, if the true model is difficult to identify, the appropriate criteria are AIC and AICc. If the true model is easy to identify, the appropriate criteria are AICu and KICc. For the large sample case, an appropriate criterion is BIC. It was also found that when the error variance increased, the efficiency of all model selection criteria decreased. Therefore, the error variance should be checked after model construction because it affects the model selection criteria. For the small sample size, model selection criteria have low accuracy, but it is more accurate in a larger sample.
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